diff --git a/website/docs/docs/build/environment-variables.md b/website/docs/docs/build/environment-variables.md
index 8f3c968e50b..955bb79ed22 100644
--- a/website/docs/docs/build/environment-variables.md
+++ b/website/docs/docs/build/environment-variables.md
@@ -139,10 +139,14 @@ _The following variables are currently only available for GitHub, GitLab, and Az
 
 Environment variables can be used in many ways, and they give you the power and flexibility to do what you want to do more easily in dbt Cloud.
 
-#### Clone private packages
+<Expandable alt_header="Clone private packages">
+
 Now that you can set secrets as environment variables, you can pass git tokens into your package HTTPS URLs to allow for on-the-fly cloning of private repositories. Read more about enabling [private package cloning](/docs/build/packages#private-packages).
 
-#### Dynamically set your warehouse in your Snowflake connection
+</Expandable>
+
+<Expandable alt_header="Dynamically set your warehouse in your Snowflake connection">
+
 Environment variables make it possible to dynamically change the Snowflake virtual warehouse size depending on the job. Instead of calling the warehouse name directly in your project connection, you can reference an environment variable which will get set to a specific virtual warehouse at runtime. 
 
 For example, suppose you'd like to run a full-refresh job in an XL warehouse, but your incremental job only needs to run in a medium-sized warehouse. Both jobs are configured in the same dbt Cloud environment. In your connection configuration, you can use an environment variable to set the warehouse name to `{{env_var('DBT_WAREHOUSE')}}`. Then in the job settings, you can set a different value for the `DBT_WAREHOUSE` environment variable depending on the job's workload.
@@ -163,7 +167,10 @@ However, there are some limitations when using env vars with Snowflake OAuth Con
 Something to note, if you supply an environment variable in the account/host field, Snowflake OAuth Connection will **fail** to connect. This happens because the field doesn't pass through Jinja rendering, so dbt Cloud simply passes the literal `env_var` code into a URL string like `{{ env_var("DBT_ACCOUNT_HOST_NAME") }}.snowflakecomputing.com`, which is an invalid hostname. Use [extended attributes](/docs/deploy/deploy-environments#deployment-credentials) instead.
 :::
 
-#### Audit your run metadata
+</Expandable>
+
+<Expandable alt_header="Audit your run metadata">
+
 Here's another motivating example that uses the dbt Cloud run ID, which is set automatically at each run. This additional data field can be used for auditing and debugging:
 
 ```sql
@@ -189,3 +196,13 @@ select *,
 
 from users_aggregated
 ```
+
+</Expandable>
+
+<Expandable alt_header="Configure Semantic Layer credentials">
+
+import SLEnvVars from '/snippets/_sl-env-vars.md';
+
+<SLEnvVars/>
+
+</Expandable>
diff --git a/website/docs/docs/build/metricflow-commands.md b/website/docs/docs/build/metricflow-commands.md
index 1f50e501261..55472ba53ce 100644
--- a/website/docs/docs/build/metricflow-commands.md
+++ b/website/docs/docs/build/metricflow-commands.md
@@ -8,7 +8,7 @@ tags: [Metrics, Semantic Layer]
 
 Once you define metrics in your dbt project, you can query metrics, dimensions, and dimension values, and validate your configs using the MetricFlow commands. 
 
-MetricFlow allows you to define and query metrics in your dbt project in the [dbt Cloud CLI](/docs/cloud/cloud-cli-installation), [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud), or [dbt Core](/docs/core/installation-overview). To experience the power of the universal [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and dynamically query those metrics in downstream tools, you'll need a dbt Cloud [Team or Enterprise](https://www.getdbt.com/pricing/) account. 
+MetricFlow allows you to define and query metrics in your dbt project in the [dbt Cloud](/docs/cloud/about-develop-dbt) or [dbt Core](/docs/core/installation-overview). To experience the power of the universal [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl) and dynamically query those metrics in downstream tools, you'll need a dbt Cloud [Team or Enterprise](https://www.getdbt.com/pricing/) account. 
 
 MetricFlow is compatible with Python versions 3.8, 3.9, 3.10, and 3.11.
 
@@ -18,33 +18,18 @@ MetricFlow is a dbt package that allows you to define and query metrics in your
 
 Using MetricFlow with dbt Cloud means you won't need to manage versioning &mdash; your dbt Cloud account will automatically manage the versioning.
 
-**dbt Cloud jobs** &mdash; MetricFlow commands aren't supported in dbt Cloud jobs yet. However, you can add MetricFlow validations with your git provider (such as GitHub Actions) by installing MetricFlow (`python -m pip install metricflow`). This allows you to run MetricFlow commands as part of your continuous integration checks on PRs.
+dbt Cloud jobs support the `dbt sl validate` command to [automatically test your semantic nodes](/docs/deploy/ci-jobs#semantic-validations-in-ci). You can also add MetricFlow validations with your git provider (such as GitHub Actions) by installing MetricFlow (`python -m pip install metricflow`). This allows you to run MetricFlow commands as part of your continuous integration checks on PRs.
 
 <Tabs>
 
-<TabItem value="cloudcli" label="dbt Cloud CLI">
+<TabItem value="cloud" label="MetricFlow with dbt Cloud">
 
-- MetricFlow [commands](#metricflow-commands) are embedded in the dbt Cloud CLI. This means you can immediately run them once you install the dbt Cloud CLI and don't need to install MetricFlow separately.
-- You don't need to manage versioning &mdash; your dbt Cloud account will automatically manage the versioning for you.
- 
-</TabItem>
-
-<TabItem value="cloud ide" label="dbt Cloud IDE">
-
-:::info
-You can create metrics using MetricFlow in the dbt Cloud IDE and run the [dbt sl validate](/docs/build/validation#validations-command) command. Support for running more MetricFlow commands in the IDE will be available soon.
-:::
+In dbt Cloud, run MetricFlow commands directly in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) or in the [dbt Cloud CLI](/docs/cloud/cloud-cli-installation). 
 
+For dbt Cloud CLI users, MetricFlow commands are embedded in the dbt Cloud CLI, which means you can immediately run them once you install the dbt Cloud CLI and don't need to install MetricFlow separately. You don't need to manage versioning because your dbt Cloud account will automatically manage the versioning for you.
 </TabItem>
 
-<TabItem value="core" label="dbt Core">
-
-:::tip Use dbt Cloud CLI for semantic layer development
-
-You can use the dbt Cloud CLI for the experience in defining and querying metrics in your dbt project.
-
-A benefit to using the dbt Cloud is that you won't need to manage versioning &mdash; your dbt Cloud account will automatically manage the versioning.
-:::
+<TabItem value="core" label="MetricFlow with dbt Core">  
 
 You can install [MetricFlow](https://github.com/dbt-labs/metricflow#getting-started) from [PyPI](https://pypi.org/project/dbt-metricflow/). You need to use `pip` to install MetricFlow on Windows or Linux operating systems:
 
@@ -54,31 +39,37 @@ You can install [MetricFlow](https://github.com/dbt-labs/metricflow#getting-star
 
 **Note**, you'll need to manage versioning between dbt Core, your adapter, and MetricFlow.
 
-</TabItem>
+Something to note, MetricFlow `mf` commands return an error if you have a Metafont latex package installed. To run `mf` commands, uninstall the package.
 
+</TabItem>
 </Tabs>
 
-Something to note, MetricFlow `mf` commands return an error if you have a Metafont latex package installed. To run `mf` commands, uninstall the package.
-
 ## MetricFlow commands
 
 MetricFlow provides the following commands to retrieve metadata and query metrics. 
 
 <Tabs>
-<TabItem value="cloud" label="Commands for dbt Cloud CLI">
+<TabItem value="cloudcommands" label="Commands for dbt Cloud">
 
-You can use the `dbt sl` prefix before the command name to execute them in the dbt Cloud CLI. For example, to list all metrics, run `dbt sl list metrics`. For a complete list of the MetricFlow commands and flags, run the `dbt sl --help` command in your terminal.
+You can use the `dbt sl` prefix before the command name to execute them in the dbt Cloud IDE or dbt Cloud CLI. For example, to list all metrics, run `dbt sl list metrics`. 
+
+dbt Cloud CLI users can run `dbt sl --help` in the terminal for a complete list of the MetricFlow commands and flags.
+
+The following table lists the commands compatible with the dbt Cloud IDE and dbt Cloud CLI:
+
+| <div style={{width:'250px'}}>Command</div>  | <div style={{width:'100px'}}>Description</div> | dbt Cloud IDE | dbt Cloud CLI |
+|---------|-------------|---------------|---------------|
+| [`list`](#list) | Retrieves metadata values. | ✅ | ✅ |
+| [`list metrics`](#list-metrics) | Lists metrics with dimensions. |  ✅ | ✅ |
+| [`list dimensions`](#list) | Lists unique dimensions for metrics. |  ✅  | ✅ |
+| [`list dimension-values`](#list-dimension-values) | List dimensions with metrics. | ✅ | ✅ |
+| [`list entities`](#list-entities) | Lists all unique entities.  |  ✅  | ✅ |
+| [`list saved-queries`](#list-saved-queries) | Lists available saved queries. Use the `--show-exports` flag to display each export listed under a saved query or `--show-parameters` to show the full query parameters each saved query uses. |  ✅ | ✅ |
+| [`query`](#query) | Query metrics, saved queries, and dimensions you want to see in the command line interface. Refer to [query examples](#query-examples) to help you get started.  |  ✅ | ✅ |
+| [`validate`](#validate) | Validates semantic model configurations. |  ✅ | ✅ |
+| [`export`](#export) |  Runs exports for a singular saved query for testing and generating exports in your development environment. You can also use the `--select` flag to specify particular exports from a saved query. |  ❌ | ✅ |
+| [`export-all`](#export-all) | Runs exports for multiple saved queries at once, saving time and effort. |  ❌ | ✅ |
 
-- [`list`](#list) &mdash; Retrieves metadata values.
-- [`list metrics`](#list-metrics) &mdash; Lists metrics with dimensions.
-- [`list dimensions`](#list) &mdash; Lists unique dimensions for metrics.
-- [`list dimension-values`](#list-dimension-values) &mdash; List dimensions with metrics.
-- [`list entities`](#list-entities) &mdash; Lists all unique entities.
-- [`list saved-queries`](#list-saved-queries) &mdash; Lists available saved queries. Use the `--show-exports` flag to display each export listed under a saved query.
-- [`query`](#query) &mdash; Query metrics, saved queries, and dimensions you want to see in the command line interface. Refer to [query examples](#query-examples) to help you get started.
-- [`export`](#export) &mdash;  Runs exports for a singular saved query for testing and generating exports in your development environment. You can also use the `--select` flag to specify particular exports from a saved query.
-- [`export-all`](#export-all) &mdash; Runs exports for multiple saved queries at once, saving time and effort.
-- [`validate`](#validate) &mdash; Validates semantic model configurations.
 
 <!--below commands aren't supported in dbt cloud yet
 - [`health-checks`](#health-checks) &mdash; Performs data platform health check.
@@ -99,7 +90,7 @@ Check out the following video for a short video demo of how to query or preview
 
 </TabItem>
 
-<TabItem value="core" label="Commands for dbt Core">
+<TabItem value="corecommands" label="Commands for dbt Core">
 
 Use the `mf` prefix before the command name to execute them in dbt Core. For example, to list all metrics, run `mf list metrics`.
 
@@ -502,8 +493,6 @@ The following tabs present additional query examples, like exporting to a CSV. S
 
 <Tabs>
 
-
-
 <TabItem value="eg6" label="--compile/--explain flag">
 
 Add `--compile` (or `--explain` for dbt Core users) to your query to view the SQL generated by MetricFlow. 
@@ -522,24 +511,24 @@ mf query --metrics order_total --group-by metric_time,is_food_order --limit 10 -
  ```bash
  ✔ Success 🦄 - query completed after 0.28 seconds
 🔎 SQL (remove --compile to see data or add --show-dataflow-plan to see the generated dataflow plan):
-SELECT
+select
   metric_time
   , is_food_order
-  , SUM(order_cost) AS order_total
-FROM (
-  SELECT
-    cast(ordered_at as date) AS metric_time
+  , sum(order_cost) as order_total
+from (
+  select
+    cast(ordered_at as date) as metric_time
     , is_food_order
     , order_cost
-  FROM ANALYTICS.js_dbt_sl_demo.orders orders_src_1
-  WHERE cast(ordered_at as date) BETWEEN CAST('2017-08-22' AS TIMESTAMP) AND CAST('2017-08-27' AS TIMESTAMP)
+  from analytics.js_dbt_sl_demo.orders orders_src_1
+  where cast(ordered_at as date) between cast('2017-08-22' as timestamp) and cast('2017-08-27' as timestamp)
 ) subq_3
-WHERE is_food_order = True
-GROUP BY
+where is_food_order = True
+group by
   metric_time
   , is_food_order
-ORDER BY metric_time DESC
-LIMIT 10
+order by metric_time desc
+limit 10
 ```
 
 </TabItem>
diff --git a/website/docs/docs/build/metricflow-time-spine.md b/website/docs/docs/build/metricflow-time-spine.md
index 7fd196f7f7c..f3387399ffe 100644
--- a/website/docs/docs/build/metricflow-time-spine.md
+++ b/website/docs/docs/build/metricflow-time-spine.md
@@ -14,9 +14,10 @@ MetricFlow requires you to define a time-spine table as a model-level configurat
 - [Conversion metrics](/docs/build/conversion)
 - [Slowly Changing Dimensions](/docs/build/dimensions#scd-type-ii)
 - [Metrics](/docs/build/metrics-overview) with the `join_to_timespine` configuration set to true
+
 To see the generated SQL for the metric and dimension types that use time-spine joins, refer to the respective documentation or add the `compile=True` flag when querying the Semantic Layer to return the compiled SQL.
 
-#### Configuring time-spine
+## Configuring time-spine in YAML
 - You only need to configure time-spine models that the Semantic Layer should recognize.
 - At a minimum, define a time-spine table for a daily grain.
 - You can optionally define a time-spine table for a different granularity, like hourly.
@@ -66,6 +67,9 @@ The example creates a time spine at a daily grain and an hourly grain. A few thi
 * You can add a time spine for each granularity you intend to use if query efficiency is more important to you than configuration time, or storage constraints. For most engines, the query performance difference should be minimal and transforming your time spine to a coarser grain at query time shouldn't add significant overhead to your queries.
 * We recommend having a time spine at the finest grain used in any of your dimensions to avoid unexpected errors. i.e., if you have dimensions at an hourly grain, you should have a time spine at an hourly grain.
 
+## Example time-spine tables
+
+### Daily
 <File name="metricflow_time_spine.sql">
 
 <VersionBlock lastVersion="1.6">
@@ -134,7 +138,7 @@ and date_hour < dateadd(day, 30, current_timestamp())
 ```
 </VersionBlock>
 
-
+### Daily (BigQuery)
 Use this model if you're using BigQuery. BigQuery supports `DATE()` instead of `TO_DATE()`:
 <VersionBlock lastVersion="1.6">
 
@@ -197,7 +201,7 @@ and date_hour < dateadd(day, 30, current_timestamp())
 
 </File>
 
-## Hourly time spine
+### Hourly 
 <File name='time_spine_hourly.sql'>
 
 ```sql
diff --git a/website/docs/docs/cloud-integrations/semantic-layer/excel.md b/website/docs/docs/cloud-integrations/semantic-layer/excel.md
index e666bda0e58..31a028f3d81 100644
--- a/website/docs/docs/cloud-integrations/semantic-layer/excel.md
+++ b/website/docs/docs/cloud-integrations/semantic-layer/excel.md
@@ -6,8 +6,6 @@ tags: [Semantic Layer]
 sidebar_label: "Microsoft Excel"
 ---
 
-# Microsoft Excel <Lifecycle status='preview'/>
-
 The dbt Semantic Layer offers a seamless integration with Excel Online and Desktop through a custom menu. This add-on allows you to build dbt Semantic Layer queries and return data on your metrics directly within Excel.
 
 ## Prerequisites
@@ -25,7 +23,7 @@ import SLCourses from '/snippets/_sl-course.md';
 
 ## Installing the add-on
 
-The dbt Semantic Layer Microsoft Excel integration is available to download directly on [Microsoft AppSource](https://appsource.microsoft.com/en-us/marketplace/apps?product=office). You can choose to download this add in for both [Excel Desktop](https://pages.store.office.com/addinsinstallpage.aspx?assetid=WA200007100&rs=en-US&correlationId=4132ecd1-425d-982d-efb4-de94ebc83f26) and [Excel Online](https://pages.store.office.com/addinsinstallpage.aspx?assetid=WA200007100&rs=en-US&correlationid=4132ecd1-425d-982d-efb4-de94ebc83f26&isWac=True)
+The dbt Semantic Layer Microsoft Excel integration is available to download directly on [Microsoft AppSource](https://appsource.microsoft.com/en-us/product/office/WA200007100?tab=Overview). You can choose to download this add-on in for both [Excel Desktop](https://pages.store.office.com/addinsinstallpage.aspx?assetid=WA200007100&rs=en-US&correlationId=4132ecd1-425d-982d-efb4-de94ebc83f26) and [Excel Online](https://pages.store.office.com/addinsinstallpage.aspx?assetid=WA200007100&rs=en-US&correlationid=4132ecd1-425d-982d-efb4-de94ebc83f26&isWac=True)
 
 1. In Excel, authenticate with your host, dbt Cloud environment ID, and service token.
    - Access your Environment ID, Host, and URLs in your dbt Cloud Semantic Layer settings. Generate a service token in the Semantic Layer settings or API tokens settings
diff --git a/website/docs/docs/cloud/configure-cloud-cli.md b/website/docs/docs/cloud/configure-cloud-cli.md
index df94f9354de..2e0fc174517 100644
--- a/website/docs/docs/cloud/configure-cloud-cli.md
+++ b/website/docs/docs/cloud/configure-cloud-cli.md
@@ -108,7 +108,6 @@ To set environment variables in the dbt Cloud CLI for your dbt project:
 2. Then select **Profile Settings**, then **Credentials**.
 3. Click on your project and scroll to the **Environment Variables** section.
 4. Click **Edit** on the lower right and then set the user-level environment variables.  
-   - Note, when setting up the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl), using [environment variables](/docs/build/environment-variables) like `{{env_var('DBT_WAREHOUSE')}}` is not supported. You should use the actual credentials instead.
 
 ## Use the dbt Cloud CLI
 
diff --git a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
index af303d0d9a0..97f5dbfdb40 100644
--- a/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
+++ b/website/docs/docs/cloud/dbt-cloud-ide/develop-in-the-cloud.md
@@ -101,6 +101,7 @@ Nice job, you're ready to start developing and building models 🎉!
 ### Considerations
 - To improve your experience using dbt Cloud, we suggest that you turn off ad blockers. This is because some project file names, such as `google_adwords.sql`, might resemble ad traffic and trigger ad blockers.
 - To preserve performance, there's a file size limitation for repositories over 6 GB. If you have a repo over 6 GB, please contact [dbt Support](mailto:support@getdbt.com) before running dbt Cloud.
+- The IDE's idle session timeout is one hour.
 - <Expandable alt_header="About the start up process and work retention">
   
   ### Start-up process
@@ -127,6 +128,7 @@ Nice job, you're ready to start developing and building models 🎉!
   - If a model or test fails, dbt Cloud makes it easy for you to view and download the run logs for your dbt invocations to fix the issue.
   - Use dbt's [rich model selection syntax](/reference/node-selection/syntax) to [run dbt commands](/reference/dbt-commands) directly within dbt Cloud.
   - Starting from dbt v1.6, leverage [environments variables](/docs/build/environment-variables#special-environment-variables) to dynamically use the Git branch name. For example, using the branch name as a prefix for a development schema.
+  - Run [MetricFlow commands](/docs/build/metricflow-commands) to create and manage metrics in your project with the [dbt Semantic Layer](/docs/use-dbt-semantic-layer/dbt-sl).
 
 - **Generate your YAML configurations with dbt Assist** <Lifecycle status="beta"/>  &mdash; [dbt Assist](/docs/cloud/dbt-assist) is a powerful artificial intelligence (AI) co-pilot feature that helps automate development in dbt Cloud. It generates documentation and tests for your dbt SQL models directly in the dbt Cloud IDE, with a click of a button, and helps you accomplish more in less time. Available for dbt Cloud Enterprise plans.
 
diff --git a/website/docs/docs/cloud/manage-access/audit-log.md b/website/docs/docs/cloud/manage-access/audit-log.md
index 70ef4d66f8e..9c80adaf2f8 100644
--- a/website/docs/docs/cloud/manage-access/audit-log.md
+++ b/website/docs/docs/cloud/manage-access/audit-log.md
@@ -60,7 +60,6 @@ The audit log supports various events for different objects in dbt Cloud. You wi
 | Event Name                 | Event Type                               | Description                                            |
 | -------------------------- | ---------------------------------------- | ------------------------------------------------------ |
 | Auth Provider Changed      | auth_provider.Changed          | Authentication provider settings changed               |
-| Credential Login Failed    | auth.CredentialsLoginFailed    | User login via username and password failed            |
 | Credential Login Succeeded | auth.CredentialsLoginSucceeded | User successfully logged in with username and password |
 | SSO Login Failed           | auth.SsoLoginFailed            | User login via SSO failed                              |
 | SSO Login Succeeded        | auth.SsoLoginSucceeded         | User successfully logged in via SSO  
diff --git a/website/docs/docs/collaborate/data-tile.md b/website/docs/docs/collaborate/data-tile.md
index efd6a0d59aa..70318922d68 100644
--- a/website/docs/docs/collaborate/data-tile.md
+++ b/website/docs/docs/collaborate/data-tile.md
@@ -6,14 +6,8 @@ description: "Embed data health tiles in your dashboards to distill trust signal
 image: /img/docs/collaborate/dbt-explorer/data-tile-pass.jpg
 ---
 
-# Embed data health tile in dashboards <Lifecycle status='beta' />
-
 With data health tiles, stakeholders will get an at-a-glance confirmation on whether the data they’re looking at is stale or degraded. This trust signal allows teams to immediately go back into Explorer to see more details and investigate issues.
 
-:::info Available in beta
-Data health tile is currently available in open beta.
-:::
-
 The data health tile:
 
 - Distills trust signals for data consumers.
@@ -122,17 +116,14 @@ Follow these steps to embed the data health tile in Tableau:
 <Lightbox src="/img/docs/collaborate/dbt-explorer/tableau-example.png" width="80%" title="Embed data health tile iFrame in Tableau"/>
 
 1. Create a dashboard in Tableau and connect to your database to pull in the data.
-2. Ensure you've copied the iFrame snippet available in dbt Explorer's **Data health** section, under the **Embed data health into your dashboard** toggle.
-3. Embed the snippet in your dashboard.
-
-    `<iframe src='https://metadata.YOUR_ACCESS_URL/exposure-tile?uniqueId=<exposure_unique_id>&environmentType=production&environmentId=<environment_id>&token=<metadata_token>' />`
+2. Ensure you've copied the URL or iFrame snippet available in dbt Explorer's **Data health** section, under the **Embed data health into your dashboard** toggle.
+3. Insert a **Web Page** object.
+4. Insert the URL and click **Ok**.
 
+    `https://metadata.cloud.getdbt.com/exposure-tile?uniqueId=exposure.snowflake_tpcds_sales_spoke.customer360_test&environmentType=production&environmentId=220370&token=<YOUR_METADATA_TOKEN>`
+    
     *Note, replace the placeholders with your actual values.*
-
-   - **For job-based exposure tile** &mdash; Insert the following fields into the following iFrame. Then embed them with your dashboard. The next [section](#job-based-data-health) will have more details on the job-based exposure tile.
-      - `<iframe src='https://metadata.YOUR_ACCESS_URL/exposure-tile?name=<exposure_name>&environment_id=<environment_id>&token=<metadata_token>' />`
-
-        *Note, replace the placeholders with your actual values.*
+5. You should now see the data health tile embedded in your Tableau dashboard.
 
 </TabItem>
 </Tabs>
diff --git a/website/docs/docs/collaborate/explore-projects.md b/website/docs/docs/collaborate/explore-projects.md
index 1c469409e4f..3af5e9886f8 100644
--- a/website/docs/docs/collaborate/explore-projects.md
+++ b/website/docs/docs/collaborate/explore-projects.md
@@ -28,7 +28,7 @@ Navigate the dbt Explorer overview page to access your project's resources and m
 - **Sidebar** &mdash; Use the left sidebar to access model [performance](/docs/collaborate/model-performance), [project recommendations](/docs/collaborate/project-recommendations) in the **Project details** section. Browse your project's [resources, file tree, and database](#browse-with-the-sidebar) in the lower section of the sidebar.
 - **Lineage graph** &mdash; Explore your project's or account's [lineage graph](#project-lineage) to visualize the relationships between resources.
 - **Latest updates** &mdash; View the latest changes or issues related to your project's resources, including the most recent job runs, changed properties, lineage, and issues.
-- **Marts and public models** &mdash; View the [marts](/best-practices/how-we-structure/1-guide-overview#guide-structure-overview) and [public models](/docs/collaborate/govern/model-access#access-modifiers) in your project. 
+- **Marts and public models** &mdash; View the [marts](/best-practices/how-we-structure/1-guide-overview#guide-structure-overview) and [public models](/docs/collaborate/govern/model-access#access-modifiers) in your project. You can also navigate to all public models in your account through this view.
 - **Model query history** &mdash; Use [model query history](/docs/collaborate/model-query-history) to track consumption queries on your models for deeper insights.
 - **Auto-exposures** &mdash; [Set up and view auto-exposures](/docs/collaborate/auto-exposures) to automatically expose relevant data models from Tableau to enhance visibility.
 
diff --git a/website/docs/docs/collaborate/govern/model-versions.md b/website/docs/docs/collaborate/govern/model-versions.md
index f255aa9db1a..eefcf76e824 100644
--- a/website/docs/docs/collaborate/govern/model-versions.md
+++ b/website/docs/docs/collaborate/govern/model-versions.md
@@ -69,7 +69,7 @@ When you make updates to a model's source code &mdash; its logical definition, i
 
 **Versioned models are different.** Defining model `versions` is appropriate when people, systems, and processes beyond your team's control, inside or outside of dbt, depend on your models. You can neither simply go migrate them all, nor break their queries on a whim. You need to offer a migration path, with clear diffs and deprecation dates.
 
-Multiple versions of a model will live in the same code repository at the same time, and be deployed into the same data environment simultaneously. This is similar to how web APIs are versioned: Multiple versions are live simultaneously, two or three, and not more). Over time, newer versions come online, and older versions are sunsetted .
+Multiple versions of a model will live in the same code repository at the same time, and be deployed into the same data environment simultaneously. This is similar to how web APIs are versioned: Multiple versions live simultaneously, two or three, and not more). Over time, newer versions come online, and older versions are sunsetted .
 
 ## How is this different from just creating a new model?
 
diff --git a/website/docs/docs/collaborate/model-query-history.md b/website/docs/docs/collaborate/model-query-history.md
index d8e08bf63da..0f43c9b163f 100644
--- a/website/docs/docs/collaborate/model-query-history.md
+++ b/website/docs/docs/collaborate/model-query-history.md
@@ -5,15 +5,15 @@ description: "Import and auto-generate exposures from dashboards and understand
 image: /img/docs/collaborate/dbt-explorer/model-query-queried-models.jpg
 ---
 
-# About model query history <Lifecycle status='beta' />
+# About model query history <Lifecycle status='preview' />
 
 Model query history allows you to:
 
-- View the count of consumption queries for a model based on the data warehouse's query logs.
+- View the count of consumption queries for a model based on the data warehouse's query logs. 
 - Provides data teams insight, so they can focus their time and infrastructure spend on the worthwhile used data products.
 - Enable analysts to find the most popular models used by other people.
 
-Model query history is powered by a single consumption query of the query log table in your data warehouse aggregated on a daily basis. 
+Model query history is powered by a single consumption query of the query log table in your data warehouse aggregated on a daily basis. It currently supports Snowflake and BigQuery only, with additional platforms coming soon.
 
 :::info What is a consumption query?
 Consumption query is a metric of queries in your dbt project that has used the model in a given time. It filters down to `select` statements only to gauge model consumption and excludes dbt model build and test executions.
@@ -28,7 +28,23 @@ To access the features, you should meet the following:
 1. You have a dbt Cloud account on the [Enterprise plan](https://www.getdbt.com/pricing/).
 2. You have set up a [production](https://docs.getdbt.com/docs/deploy/deploy-environments#set-as-production-environment) deployment environment for each project you want to explore, with at least one successful job run. 
 3. You have [admin permissions](/docs/cloud/manage-access/enterprise-permissions) in dbt Cloud to edit project settings or production environment settings.
-4. Use Snowflake or BigQuery as your data warehouse and can enable query history permissions or work with an admin to do so (Support for additional data platforms coming soon).
+4. Use Snowflake or BigQuery as your data warehouse and can enable query history permissions or work with an admin to do so. Support for additional data platforms coming soon.
+
+## Enable query history in dbt Cloud
+
+To enable model query history in dbt Cloud, follow these steps:
+
+1. Navigate to **Deploy** and then **Environments**.
+2. Select the environment marked **PROD** and click **Settings**.
+3. Click **Edit** and scroll to the **Query History** section to enable the query history toggle. When it’s green and to the right, it's enabled.
+4. Click the **Test Permissions** button to validate the deployment credentials permissions are sufficient to support query history.
+
+<DocCarousel slidesPerView={1}>
+
+<Lightbox src="/img/docs/collaborate/dbt-explorer/enable-query-history.jpg" width="95%" title="Enable query history in your environment settings." />
+<Lightbox src="/img/docs/collaborate/dbt-explorer/enable-query-history-success.jpg" width="95%" title="Example of permissions verified result after clicking Test Permissions." />
+
+</DocCarousel>
 
 ## Credential permissions
 
@@ -61,25 +77,12 @@ The model query history feature uses the credentials in your production environm
 
     </Expandable>
 
-## Enable query history in dbt Cloud
-
-:::info
-During beta, the dbt Labs team will manually enable query history for your dbt Cloud projects. Later on, you’ll be able to do it yourself.
-
-:::
-<!--
-1. Navigate to **Deploy** and then **Environments**.
-2. Select the environment marked **PROD** and click **Settings**. 
-3. Enable the checkbox for query history in **General Settings**. 
-4. Click the **Test** button to validate the deployment credentials permissions are sufficient to support query history.
--->
-
 ## View query history in Explorer
 
 To enhance your discovery, you can view your model query history in various locations within dbt Explorer:
-- [View from Performance charts](#view-from-performance-charts) 
-* [View from Project lineage](#view-from-project-lineage) 
-- [View from Model list](#view-from-model-list) 
+- [View from Performance charts](#view-from-performance-charts)
+* [View from Project lineage](#view-from-project-lineage)
+- [View from Model list](#view-from-model-list)
 
 ### View from Performance charts
 
@@ -92,7 +95,7 @@ To enhance your discovery, you can view your model query history in various loca
 4. Click on a model for more details and go to the **Performance** tab.
 5. On the **Performance** tab, scroll down to the **Model performance** section. 
 6. Select the **Consumption queries** tab to view the consumption queries over a given time for that model. 
-<Lightbox src="/img/docs/collaborate/model-consumption-queries.jpg" width="85%" title="View consumption queries over time for a given model." />
+<Lightbox src="/img/docs/collaborate/model-consumption-queries.jpg" width="90%" title="View consumption queries over time for a given model." />
 
 ### View from Project lineage
 
diff --git a/website/docs/docs/dbt-cloud-apis/admin-cloud-api.md b/website/docs/docs/dbt-cloud-apis/admin-cloud-api.md
index d4ec0a82d5f..fe024e60831 100644
--- a/website/docs/docs/dbt-cloud-apis/admin-cloud-api.md
+++ b/website/docs/docs/dbt-cloud-apis/admin-cloud-api.md
@@ -24,14 +24,14 @@ link="/dbt-cloud/api-v2-legacy"
     icon="pencil-paper"/>
 
 <Card
-    title="API v2 (beta docs)"
-    body="Our legacy API version, with limited endpoints and features. Contains information not available in v3. These docs are in beta and may not be complete."
+    title="API v2"
+    body="Our legacy API version, with limited endpoints and features. Contains information not available in v3."
 link="/dbt-cloud/api-v2"
     icon="pencil-paper"/>
 
 <Card
-    title="API v3 (beta docs)"
-    body="Our latest API version, with new endpoints and features. These docs are in beta and may not be complete."
+    title="API v3"
+    body="Our latest API version, with new endpoints and features."
 link="/dbt-cloud/api-v3"
     icon="pencil-paper"/>
 
diff --git a/website/docs/docs/dbt-versions/release-notes.md b/website/docs/docs/dbt-versions/release-notes.md
index f3a0c53844e..7c2614b2c10 100644
--- a/website/docs/docs/dbt-versions/release-notes.md
+++ b/website/docs/docs/dbt-versions/release-notes.md
@@ -20,6 +20,11 @@ Release notes are grouped by month for both multi-tenant and virtual private clo
 
 ## September 2024
 
+- **Enhancement**: You can now run [Semantic Layer commands](/docs/build/metricflow-commands) commands in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud). The supported commands are `dbt sl list`, `dbt sl list metrics`, `dbt sl list dimension-values`, `dbt sl list saved-queries`, `dbt sl query`, `dbt sl list dimensions`, `dbt sl list entities`, and `dbt sl validate`. 
+- **New**: Microsoft Excel, a dbt Semantic Layer integration, is now generally available. The integration allows you to connect to Microsoft Excel to query metrics and collaborate with your team. Available for [Excel Desktop](https://pages.store.office.com/addinsinstallpage.aspx?assetid=WA200007100&rs=en-US&correlationId=4132ecd1-425d-982d-efb4-de94ebc83f26) or [Excel Online](https://pages.store.office.com/addinsinstallpage.aspx?assetid=WA200007100&rs=en-US&correlationid=4132ecd1-425d-982d-efb4-de94ebc83f26&isWac=True). For more information, refer to [Microsoft Excel](/docs/cloud-integrations/semantic-layer/excel).
+- **New**: [Data health tile](/docs/collaborate/data-tile) is now generally available in dbt Explorer. Data health tiles provide a quick at-a-glance view of your data quality, highlighting potential issues in your data. You can embed these tiles in your dashboards to quickly identify and address data quality issues in your dbt project.
+- **New**: dbt Explorer's Model query history feature is now in Preview for dbt Cloud Enterprise customers. Model query history allows you to view the count of consumption queries for a model based on the data warehouse's query logs. This feature provides data teams insight, so they can focus their time and infrastructure spend on the worthwhile used data products. To learn more, refer to [Model query history](/docs/collaborate/model-query-history). 
+- **Enhancement**: You can now use [Extended Attributes](/docs/dbt-cloud-environments#extended-attributes) and [Environment Variables](/docs/build/environment-variables) when connecting to the Semantic Layer. If you set a value directly in the Semantic Layer Credentials, it will have a higher priority than Extended Attributes. When using environment variables, the default value for the environment will be used.  If you're using exports, job environment variable overrides aren't supported yet, but they will be soon.
 - **New:** There are two new [environment variable defaults](/docs/build/environment-variables#dbt-cloud-context) &mdash; `DBT_CLOUD_ENVIRONMENT_NAME` and `DBT_CLOUD_ENVIRONMENT_TYPE`.
 - **New:** The [Amazon Athena warehouse connection](/docs/cloud/connect-data-platform/connect-amazon-athena) is available as a public preview for dbt Cloud accounts that have upgraded to [`versionless`](/docs/dbt-versions/versionless-cloud).
 
diff --git a/website/docs/docs/deploy/ci-jobs.md b/website/docs/docs/deploy/ci-jobs.md
index 649d64d30c7..420672c9595 100644
--- a/website/docs/docs/deploy/ci-jobs.md
+++ b/website/docs/docs/deploy/ci-jobs.md
@@ -14,6 +14,7 @@ dbt Labs recommends that you create your CI job in a dedicated dbt Cloud [deploy
 - You have a dbt Cloud account. 
 - CI features:
    - For both the [concurrent CI checks](/docs/deploy/continuous-integration#concurrent-ci-checks) and [smart cancellation of stale builds](/docs/deploy/continuous-integration#smart-cancellation) features, your dbt Cloud account must be on the [Team or Enterprise plan](https://www.getdbt.com/pricing/).
+   - The [SQL linting](/docs/deploy/continuous-integration#sql-linting) feature is currently available in beta to a limited group of users and is gradually being rolled out. If you're in the beta, the **Linting** option is available for use. 
 - [Advanced CI](/docs/deploy/advanced-ci) features:<Lifecycle status="preview" />
    - For the [compare changes](/docs/deploy/advanced-ci#compare-changes) feature, your dbt Cloud account must have access to Advanced CI. Please ask your [dbt Cloud administrator to enable](/docs/cloud/account-settings#account-access-to-advanced-ci-features) this for you.
 - Set up a [connection with your Git provider](/docs/cloud/git/git-configuration-in-dbt-cloud). This integration lets dbt Cloud run jobs on your behalf for job triggering.
@@ -35,6 +36,7 @@ To make CI job creation easier, many options on the **CI job** page are set to d
 
 1. Options in the **Execution settings** section:
     - **Commands** &mdash; By default, it includes the `dbt build --select state:modified+` command. This informs dbt Cloud to build only new or changed models and their downstream dependents. Importantly, state comparison can only happen when there is a deferred environment selected to compare state to. Click **Add command** to add more [commands](/docs/deploy/job-commands)  that you want to be invoked when this job runs.
+    - **Linting**<Lifecycle status="beta" /> &mdash; Enable this option for dbt to [lint the SQL files](/docs/deploy/continuous-integration#sql-linting) in your project as the first step in `dbt run`. If this check runs into an error, dbt can either **Fail job run** or **Continue running job**. 
     - **Run compare changes**<Lifecycle status="preview" /> &mdash; Enable this option to compare the last applied state of the production environment (if one exists) with the latest changes from the pull request, and identify what those differences are. To enable record-level comparison and primary key analysis, you must add a [primary key constraint](/reference/resource-properties/constraints) or [uniqueness test](/reference/resource-properties/data-tests#unique). Otherwise, you'll receive a "Primary key missing" error message in dbt Cloud.
     
       To review the comparison report, navigate to the [Compare tab](/docs/deploy/run-visibility#compare-tab) in the job run's details. A summary of the report is also available from the pull request in your Git provider (see the [CI report example](#example-ci-report)). 
diff --git a/website/docs/docs/deploy/continuous-integration.md b/website/docs/docs/deploy/continuous-integration.md
index 49d050f3146..2119724e609 100644
--- a/website/docs/docs/deploy/continuous-integration.md
+++ b/website/docs/docs/deploy/continuous-integration.md
@@ -34,6 +34,7 @@ The [dbt Cloud scheduler](/docs/deploy/job-scheduler) executes CI jobs different
 - **Concurrent CI checks** &mdash; CI runs triggered by the same dbt Cloud CI job execute concurrently (in parallel), when appropriate.
 - **Smart cancellation of stale builds** &mdash; Automatically cancels stale, in-flight CI runs when there are new commits to the PR.
 - **Run slot treatment** &mdash; CI runs don't consume a run slot.
+- **SQL linting**<Lifecycle status="beta" /> &mdash; When enabled, automatically lints all SQL files in your project as a run step before your CI job builds.
 
 ### Concurrent CI checks
 
@@ -54,3 +55,11 @@ When you push a new commit to a PR, dbt Cloud enqueues a new CI run for the late
 ### Run slot treatment <Lifecycle status="team,enterprise" />
 
 CI runs don't consume run slots. This guarantees a CI check will never block a production run.
+
+### SQL linting <Lifecycle status="beta" />
+
+When enabled for your CI job, dbt invokes [SQLFluff](https://sqlfluff.com/) which is a modular and configurable SQL linter that warns you of complex functions, syntax, formatting, and compilation errors. By default, it lints all the SQL files in your project. 
+
+If the linter runs into errors, you can specify whether dbt should fail the job or continue running it. When failing jobs, it helps reduce compute costs by avoiding builds for pull requests that don't meet your SQL code quality CI check. 
+
+To override the default linting behavior, create an `.sqlfluff` config file in your project and add your linting rules to it. dbt Cloud will use the rules defined in the config file when linting. For details about linting rules, refer to [Custom Usage](https://docs.sqlfluff.com/en/stable/gettingstarted.html#custom-usage) in the SQLFluff documentation.
diff --git a/website/docs/docs/use-dbt-semantic-layer/exports.md b/website/docs/docs/use-dbt-semantic-layer/exports.md
index 65ef1c6e3db..5d6e4c0d996 100644
--- a/website/docs/docs/use-dbt-semantic-layer/exports.md
+++ b/website/docs/docs/use-dbt-semantic-layer/exports.md
@@ -54,7 +54,7 @@ Before you're able to run exports in development or production, you'll need to m
 
 There are two ways to run an export:
   
-- [Run exports in development](#exports-in-development) using the [dbt Cloud CLI](/docs/cloud/cloud-cli-installation) to test the output before production (You can configure exports in the dbt Cloud IDE, however running them directly in the IDE isn't supported yet).
+- [Run exports in development](#exports-in-development) using the [dbt Cloud CLI](/docs/cloud/cloud-cli-installation) to test the output before production (You can configure exports in the dbt Cloud IDE, however running them directly in the IDE isn't supported yet). If you're using the dbt Cloud IDE, use `dbt build` to run exports. Make sure you have the [environment variable](#set-environment-variable) enabled. 
 - [Run exports in production](#exports-in-production) using the [dbt Cloud job scheduler](/docs/deploy/job-scheduler) to write these queries within your data platform.
 
 ## Exports in development
diff --git a/website/docs/guides/sl-snowflake-qs.md b/website/docs/guides/sl-snowflake-qs.md
index fb72ee0057e..cf9bca98147 100644
--- a/website/docs/guides/sl-snowflake-qs.md
+++ b/website/docs/guides/sl-snowflake-qs.md
@@ -339,9 +339,7 @@ If you used Partner Connect, you can skip to [initializing your dbt project](#in
 <Snippet path="tutorial-managed-repo" />
 
 ## Initialize your dbt project and start developing
-This guide assumes you use the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) to develop your dbt project and define metrics. However, the dbt Cloud IDE doesn't support using [MetricFlow commands](/docs/build/metricflow-commands) to query or preview metrics (support coming soon).
-
-To query and preview metrics in your development tool, you can use the [dbt Cloud CLI](/docs/cloud/cloud-cli-installation) to run the [MetricFlow commands](/docs/build/metricflow-commands).
+This guide assumes you use the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) to develop your dbt project, define metrics, and query and preview metrics using [MetricFlow commands](/docs/build/metricflow-commands).
 
 Now that you have a repository configured, you can initialize your project and start development in dbt Cloud using the IDE:
 
diff --git a/website/docs/reference/dbt-jinja-functions/set.md b/website/docs/reference/dbt-jinja-functions/set.md
index d85e0539924..fa4de60e968 100644
--- a/website/docs/reference/dbt-jinja-functions/set.md
+++ b/website/docs/reference/dbt-jinja-functions/set.md
@@ -27,6 +27,10 @@ __Args__:
 {% do log(my_set) %}  {# None #}
 ```
 
+```
+{% set email_id = "'admin@example.com'" %}
+```
+
 ### set_strict
 
 The `set_strict` context method can be used to convert any iterable to a sequence of iterable elements that are unique (a set). The difference to the `set` context method is that the `set_strict` method will raise an exception on a `TypeError`, if the provided value is not a valid iterable and cannot be converted to a set.
diff --git a/website/docs/reference/project-configs/require-dbt-version.md b/website/docs/reference/project-configs/require-dbt-version.md
index 42dc49c4546..97b42e036ec 100644
--- a/website/docs/reference/project-configs/require-dbt-version.md
+++ b/website/docs/reference/project-configs/require-dbt-version.md
@@ -93,7 +93,7 @@ In the following example, the project will only run with dbt v1.5:
 <File name='dbt_project.yml'>
 
 ```yml
-require-dbt-version: 1.5
+require-dbt-version: "1.5.0"
 ```
 
 </File>
diff --git a/website/docs/reference/resource-configs/target_schema.md b/website/docs/reference/resource-configs/target_schema.md
index 893686a7513..ffa95df9be7 100644
--- a/website/docs/reference/resource-configs/target_schema.md
+++ b/website/docs/reference/resource-configs/target_schema.md
@@ -4,9 +4,9 @@ description: "Target_schema - Read this in-depth guide to learn about configurat
 datatype: string
 ---
 
-:::note
+:::info
 
-For [versionless](/docs/dbt-versions/core-upgrade/upgrading-to-v1.8#versionless) dbt Cloud accounts and dbt Core v1.9+, this functionality is no longer required. Use the [schema](/reference/resource-configs/schema) config as an alternative to define a custom schema while still respecting the `generate_schema_name` macro. 
+For [versionless](/docs/dbt-versions/core-upgrade/upgrading-to-v1.8#versionless) dbt Cloud accounts and dbt Core v1.9+, this configuration is no longer required. Use the [schema](/reference/resource-configs/schema) config as an alternative to define a custom schema while still respecting the `generate_schema_name` macro. 
 
 :::
 
@@ -33,12 +33,14 @@ snapshots:
 </File>
 
 ## Description
-The schema that dbt should build a [snapshot](/docs/build/snapshots) <Term id="table" /> into. Snapshots build into the same `target_schema`, no matter who is running them.
+The schema that dbt should build a [snapshot](/docs/build/snapshots) <Term id="table" /> into. When `target_schema` is provided, snapshots build into the same `target_schema`, no matter who is running them.
 
 On **BigQuery**, this is analogous to a `dataset`.
 
 ## Default
-This is a **required** parameter, no default is provided.
+
+<VersionBlock lastVersion="1.8" >This is a required parameter, no default is provided. </VersionBlock>
+<VersionBlock firstVersion="1.9.1">For versionless dbt Cloud accounts and dbt Core v1.9+, this is not a required parameter. </VersionBlock>
 
 ## Examples
 ### Build all snapshots in a schema named `snapshots`
@@ -53,38 +55,10 @@ snapshots:
 
 </File>
 
-### Use a target-aware schema
-Use the [`{{ target }}` variable](/reference/dbt-jinja-functions/target) to change which schema a snapshot <Term id="table" /> is built in.
-
-Note: consider whether this use-case is right for you, as downstream `refs` will select from the `dev` version of a snapshot, which can make it hard to validate models that depend on snapshots (see above [FAQ](#faqs))
-
-<File name='dbt_project.yml'>
-
-```yml
-snapshots:
-  +target_schema: "{% if target.name == 'prod' %}snapshots{% else %}{{ target.schema }}{% endif %}"
-
-```
-
-</File>
+<VersionBlock lastVersion="1.8" >
 
 ### Use the same schema-naming behavior as models
 
-Leverage the [`generate_schema_name` macro](/docs/build/custom-schemas) to build snapshots in schemas that follow the same naming behavior as your models.
-
-Notes:
-* This macro is not available when configuring from the `dbt_project.yml` file, so must be configured in a snapshot config block.
-* Consider whether this use-case is right for you, as downstream `refs` will select from the `dev` version of a snapshot, which can make it hard to validate models that depend on snapshots (see above [FAQ](#faqs))
-
+For native support of environment-aware snapshots, upgrade to dbt Core version 1.9+ and remove any existing `target_schema` configuration. 
 
-<File name='snapshots/orders_snapshot.sql'>
-
-```sql
-{{
-    config(
-      target_schema=generate_schema_name('snapshots')
-    )
-}}
-```
-
-</File>
+</VersionBlock>
\ No newline at end of file
diff --git a/website/docs/reference/resource-configs/updated_at.md b/website/docs/reference/resource-configs/updated_at.md
index 896405bf063..69abb463842 100644
--- a/website/docs/reference/resource-configs/updated_at.md
+++ b/website/docs/reference/resource-configs/updated_at.md
@@ -27,6 +27,12 @@ snapshots:
 
 </File>
 
+:::caution
+
+You will get a warning if the data type of the `updated_at` column does not match the adapter-configured default.
+
+:::
+
 ## Description
 A column within the results of your snapshot query that represents when the record row was last updated.
 
diff --git a/website/docs/reference/resource-properties/unit-testing-versions.md b/website/docs/reference/resource-properties/unit-testing-versions.md
index 4d28e19e71d..39ef241c122 100644
--- a/website/docs/reference/resource-properties/unit-testing-versions.md
+++ b/website/docs/reference/resource-properties/unit-testing-versions.md
@@ -27,7 +27,7 @@ unit_tests:
   - name: test_is_valid_email_address
     model: my_model 
     versions:
-      include: 
+      exclude: 
         - 1
     ...
 
diff --git a/website/sidebars.js b/website/sidebars.js
index 3ecff4567ce..5dbf3caf036 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -648,12 +648,12 @@ const sidebarSettings = {
             },
             {
               type: "link",
-              label: "API v2 (beta docs)",
+              label: "API v2",
               href: "/dbt-cloud/api-v2",
             },
             {
               type: "link",
-              label: "API v3 (beta docs)",
+              label: "API v3",
               href: "/dbt-cloud/api-v3",
             },
           ],
diff --git a/website/snippets/_new-sl-setup.md b/website/snippets/_new-sl-setup.md
index b9c64bc36f6..9079de6b80f 100644
--- a/website/snippets/_new-sl-setup.md
+++ b/website/snippets/_new-sl-setup.md
@@ -1,3 +1,5 @@
+import SLEnvVars from '/snippets/_sl-env-vars.md';
+
 You must be part of the Owner group and have the correct [license](/docs/cloud/manage-access/seats-and-users) and [permissions](/docs/cloud/manage-access/self-service-permissions) to set up the Semantic Layer at the environment and project level.
 - Enterprise plan:
   - Developer license with Account Admin permissions, or
@@ -37,7 +39,7 @@ This credential controls the physical access to underlying data accessed by the
 2. Click the **Add Semantic Layer credential** button. 
 3. In the **1. Add credentials** section, enter the credentials specific to your data platform that you want the Semantic Layer to use.
    - Use credentials with minimal privileges. The Semantic Layer requires read access to the schema(s) containing the dbt models used in your semantic models for downstream applications
-   - Note, environment variables such as  `{{env_var('DBT_WAREHOUSE') }}`, aren't supported in the dbt Semantic Layer yet. You must use the actual credentials.
+   - <SLEnvVars/>
 
 <Lightbox src="/img/docs/dbt-cloud/semantic-layer/sl-add-credential.jpg" width="55%" title="Add credentials and map them to a service token. " />
 
@@ -68,7 +70,7 @@ We recommend configuring credentials and service tokens to reflect your teams an
 Note that: 
 - Admins can link multiple service tokens to a single credential within a project, but each service token can only be linked to one credential per project.
 - When you send a request through the APIs, the service token of the linked credential will follow access policies of the underlying view and tables used to build your semantic layer requests.
-- [Environment variables](/docs/build/environment-variables), like `{{env_var('DBT_WAREHOUSE') }}` aren't supported in the dbt Semantic Layer yet. You must use the actual credentials instead.
+- <SLEnvVars/>
 
 To add multiple credentials and map them to service tokens:
 
diff --git a/website/snippets/_packages_or_dependencies.md b/website/snippets/_packages_or_dependencies.md
index 8d21768b0bf..3cd0361a099 100644
--- a/website/snippets/_packages_or_dependencies.md
+++ b/website/snippets/_packages_or_dependencies.md
@@ -7,7 +7,7 @@ Starting from dbt v1.6, we added a new configuration file called `dependencies.y
 
 If your dbt project doesn't require the use of Jinja within the package specifications, you can simply rename your existing `packages.yml` to `dependencies.yml`. However, something to note is if your project's package specifications use Jinja, particularly for scenarios like adding an environment variable or a [Git token method](/docs/build/packages#git-token-method) in a private Git package specification, you should continue using the `packages.yml` file name.
 
-Examine the following tabs to understand the differences and determine when should use to `dependencies.yml` or `packages.yml`.
+Examine the following tabs to understand the differences and determine when to use `dependencies.yml` or `packages.yml` (or both at the same time).
 
 <Tabs>
 <TabItem value="project" label="When to use Project dependencies">
diff --git a/website/snippets/_sl-env-vars.md b/website/snippets/_sl-env-vars.md
new file mode 100644
index 00000000000..eddb3952782
--- /dev/null
+++ b/website/snippets/_sl-env-vars.md
@@ -0,0 +1,5 @@
+Use [Extended Attributes](/docs/dbt-cloud-environments#extended-attributes) and [Environment Variables](/docs/build/environment-variables) when connecting to the Semantic Layer. If you set a value directly in the Semantic Layer Credentials, it will have a higher priority than Extended Attributes. When using environment variables, the default value for the environment will be used.
+
+For example, set the warehouse by using `{{env_var('DBT_WAREHOUSE')}}` in your Semantic Layer credentials.
+
+Similarly, if you set the account value using `{{env_var('DBT_ACCOUNT')}}` in Extended Attributes, dbt will check both the Extended Attributes and the environment variable.
diff --git a/website/snippets/_sl-partner-links.md b/website/snippets/_sl-partner-links.md
index 71daaaa1d0a..28e4dc24b39 100644
--- a/website/snippets/_sl-partner-links.md
+++ b/website/snippets/_sl-partner-links.md
@@ -17,7 +17,7 @@ The following tools integrate with the dbt Semantic Layer:
     icon="google-sheets-logo-icon"/>
 
   <Card
-    title="Microsoft Excel (preview)"
+    title="Microsoft Excel"
     link="/docs/cloud-integrations/semantic-layer/excel"
     body="Connect to Microsoft Excel to query metrics and collaborate with your team. Available for Excel Desktop or Excel Online."
     icon="excel"/>
diff --git a/website/snippets/_sl-test-and-query-metrics.md b/website/snippets/_sl-test-and-query-metrics.md
index 0b637550cbb..9d996554b31 100644
--- a/website/snippets/_sl-test-and-query-metrics.md
+++ b/website/snippets/_sl-test-and-query-metrics.md
@@ -1,6 +1,6 @@
 To work with metrics in dbt, you have several tools to validate or run commands. Here's how you can test and query metrics depending on your setup:
 
-- [**dbt Cloud IDE users**](#dbt-cloud-ide-users) &mdash; Currently, running MetricFlow commands directly in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) isn't supported, but is coming soon. You can view metrics visually through the DAG in the **Lineage** tab without directly running commands.
+- [**dbt Cloud IDE users**](#dbt-cloud-ide-users) &mdash; Run [MetricFlow commands](/docs/build/metricflow-commands#metricflow-commands) directly in the [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) to query/preview metrics. View metrics visually in the **Lineage** tab.
 - [**dbt Cloud CLI users**](#dbt-cloud-cli-users) &mdash; The [dbt Cloud CLI](/docs/cloud/cloud-cli-installation) enables you to run [MetricFlow commands](/docs/build/metricflow-commands#metricflow-commands) to query and preview metrics directly in your command line interface.
 - **dbt Core users** &mdash; Use the MetricFlow CLI for command execution. While this guide focuses on dbt Cloud users, dbt Core users can find detailed MetricFlow CLI setup instructions in the [MetricFlow commands](/docs/build/metricflow-commands#metricflow-commands) page. Note that to use the dbt Semantic Layer, you need to have a [Team or Enterprise account](https://www.getdbt.com/).
 
@@ -8,7 +8,9 @@ Alternatively, you can run commands with SQL client tools like DataGrip, DBeaver
 
 ### dbt Cloud IDE users
 
-You can view your metrics in the dbt Cloud IDE by viewing them in the **Lineage** tab. The dbt Cloud IDE **Status button** (located in the bottom right of the editor) displays an **Error** status if there's an error in your metric or semantic model definition. You can click the button to see the specific issue and resolve it.
+You can use the `dbt sl` prefix before the command name to execute them in dbt Cloud. For example, to list all metrics, run `dbt sl list metrics`. For a complete list of the MetricFlow commands available in the dbt Cloud IDE, refer to the [MetricFlow commands](/docs/build/metricflow-commands#metricflow-commandss) page.
+
+The dbt Cloud IDE **Status button** (located in the bottom right of the editor) displays an **Error** status if there's an error in your metric or semantic model definition. You can click the button to see the specific issue and resolve it.
 
 Once viewed, make sure you commit and merge your changes in your project.
 
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