Explanation:

CRM Analytics dashboards display data based on the actual records present in the synced dataset. Even though the Category field includes the new picklist value and the field is part of the sync and recipe:

If no Account records currently use the new picklist value, then no rows in the dataset will contain it.
As a result, the dashboards will not show this value in filters, groupings, or visualizations—because it doesn’t exist in the data yet.

This is a common scenario when new picklist values are added but not yet used in any records.

❌ Why the other options are incorrect:
Option A: If the user didn’t have access to the field, the recipe would likely fail or omit the field entirely—not just the new value.
Option B: The Integration User profile controls sync access, but if the field is already syncing, then access is not the issue. The problem is the absence of data using the new value.

References:
Salesforce Help: Sync Salesforce Data to CRM Analytics
Trailhead: Prepare Data with Recipes

Explanation:

In CRM Analytics, when working with datasets that include missing values, especially in fields like Income that may be sparse but highly predictive, the best practice is to use the “Predict Missing Values” transformation in recipes.

This transformation:
Uses machine learning to estimate missing values based on patterns in other fields.
Preserves the column for modeling while improving data quality.
Is ideal when the field is informative but incomplete—like Income in this case.

Since the consultant believes Income is valuable for predicting Customer Lifetime Value, dropping it would reduce model performance. Predicting missing values is a scalable and intelligent way to retain the feature.

❌ Why the other options are incorrect:
Option A: Filling with the average is a simplistic method that can introduce bias and reduce variance, especially when only 1% of values are present.
Option C: Dropping the field discards potentially valuable predictive information, which contradicts the consultant’s belief that it’s informative.

References:
Salesforce Help: Predict Missing Values Transformation
Trailhead: Prepare Data with Recipes

Explanation:

Correct Answer (C):

What sfdcDigest does: The sfdcDigest node is a core component in CRM Analytics dataflows and recipes. Its specific function is to extract data directly from a Salesforce object (e.g., Account, Opportunity, Case).

The Role of Data Sync: In Salesforce CRM Analytics, data sync is an essential prerequisite for most data ingestion processes. When you enable data sync for a Salesforce object, CRM Analytics creates a "staging" dataset. This staging dataset is a replica of the Salesforce object's data, which is refreshed on a schedule (the data sync schedule).

The Dependency: The sfdcDigest node in a dataflow or recipe doesn't go directly to the live Salesforce object every time it runs. Instead, it reads the data from the staging dataset that was created and populated by the data sync process. If you've just enabled data sync but haven't run it yet, that staging dataset is empty or doesn't exist.
The Failure: When the dataflow/recipe starts, the sfdcDigest node looks for its source data in the staging area. Since the sync hasn't run even once, the data is not there. This missing data source causes the node to fail, which in turn causes the entire dataflow or recipe to fail.

Incorrect Answers (A & B):

A. Dataflowsysrecipes with computeExpression nodes fail until syne has run for the first time.
Reason: The computeExpression node is a transformation node, not a data extraction node. It's used to create a new field based on a SAQL expression using data that has already been digested or loaded into the dataflow. It doesn't rely on the initial data sync. If a dataflow fails at this node, it's because of an issue with the expression itself or the data it's trying to process, not the sync.

B. Dataflows/recipes with Augment nodes fail until sync has run for the first time.
Reason: The Augment node is used to join two datasets together. It relies on the presence of two already existing datasets within the dataflow. It has no dependency on the initial data sync process. A failure at this node would be due to a join key mismatch or an issue with the datasets being joined, not the absence of the first sync run.

Explanation:

This question tests the consultant's understanding of how to manage the refresh schedule for an entire templated app and its associated data pipeline in CRM Analytics.

Why C is Correct:
Templated apps (like Sales Analytics) are designed as integrated, pre-built solutions. When you install such an app, it creates a complex, interdependent set of assets (dataflows, recipes, datasets, lenses, dashboards, and Einstein Discovery models). The App Install History page provides a centralized "master schedule" for the entire app. Scheduling from this location ensures that all the underlying components run in the correct, managed sequence. Scheduling the app itself guarantees that dataflows run first to bring in raw data, then recipes transform it, and finally, any dependent Einstein Discovery models are retrained—all automatically and in the right order.

Why A is Incorrect:
While technically possible, this is a highly inefficient, error-prone, and non-scalable approach. Manually scheduling each individual asset (every recipe, dataflow, connector, and Einstein model) is tedious. More importantly, it breaks the managed dependencies. You risk a recipe trying to run before its source dataflow has finished, or an Einstein model retraining before its source dataset is updated, leading to data inconsistencies and failures.

Why B is Incorrect:
This is an improvement over option A but is still incomplete. Scheduling only the recipes and dataflows would update the core datasets. However, it would not automatically trigger the refresh of the Einstein Discovery models and prediction definitions. These are separate assets that rely on the updated datasets. Using the App Schedule is the only method that encompasses the entire pipeline, including Einstein assets.

Key Concept
Managed App Schedules: The key concept here is that a templated app is a managed package of analytics content. The platform provides a top-level scheduling mechanism (App Install History) specifically to handle the orchestration of all its components. This is the Salesforce-recommended and most robust method for ensuring a consistent and reliable data refresh for the entire application.
Orchestration: A critical part of a consultant's role is understanding data pipeline dependencies. The App Schedule handles the orchestration automatically, eliminating the need for complex manual workflow management.

Explanation:

In CRM Analytics (formerly Tableau CRM), external objects in Salesforce represent data stored outside of Salesforce but accessible via Salesforce Connect. While these objects can be viewed in Salesforce, they are not supported by the sfdcDigest transformation used in recipes or dataflows to extract Salesforce data into CRM Analytics.
The sfdcDigest transformation only works with local Salesforce objects that are synced via the standard connector.
External objects are excluded from this capability, meaning they cannot be ingested directly into CRM Analytics using recipes or dataflows.
To work with external data, you would need to:
Use middleware or ETL tools to bring the data into Salesforce as local objects.
Or load the data into CRM Analytics via external connectors, but not through the standard Salesforce connector or digest transformation.

❌ Why the other options are incorrect:
Option B: CRM Analytics does not support custom connectors for external objects in the way implied. External objects require special handling and are not directly ingestible via standard connectors.
Option C: You cannot load external object data into CRM Analytics using recipes unless it’s first transformed into a supported format. So joining in recipes is not feasible without preprocessing.

References:
Salesforce Help: Unsupported Salesforce Objects and Fields in CRM Analytics
Salesforce Help: digest Transformation