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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Einstein Discovery | 20% | - Implement Einstein Discovery
|
| Topic 2: Security | 10% | - Determine security and sharing settings
|
| Topic 3: Data Layer | 25% | - Implement and configure the right data transformations
|
| Topic 4: Analytics Dashboard Configuration | 25% | - Configure dashboards
|
| Topic 5: Analytics Dashboard Design | 20% | - Design and build CRM Analytics dashboards
|
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NEW QUESTION # 100
Universal Containers asks a CRM Analytics consultant to review the performance of its local data sync.
After removing unused objects and fields from connected data, what else should the consultant do to improve performance of the data sync?
Answer: A
Explanation:
To improve the performance of local data sync in Universal Containers, evaluating the connection mode for each connected object is a practical approach. Here's the rationale:
* Optimization of Resources: Different connection modes (e.g., Full Sync, Incremental Sync) use different amounts of resources. Choosing the right mode for each object based on how frequently its data changes can optimize the sync process and reduce load times.
* Efficient Data Handling: By tailoring the connection mode to the needs of specific data objects, the overall efficiency of the data sync process is improved, leading to faster refresh rates and more timely data availability.
* Cost and Performance Balance: Evaluating and selecting the appropriate connection mode can also help balance performance needs with cost constraints, as some modes may consume more compute resources than others.
NEW QUESTION # 101
A CRM Analytics consultant has prepared a CSV file to be uploaded to CRM Analytics. By mistake, one of the column headers is modified as random non-alphanumeric characters "*&**(&*(%", which went unnoticed prior to uploading the file.
What is the expected behavior of the uploaded CSV column?
Answer: A
Explanation:
When uploading CSV files into CRM Analytics, column headers must follow certain formatting rules.
Headers containing non-alphanumeric characters, such as "&**(&(%", will automatically be adjusted.
Specifically, if the column header starts with non-alphanumeric characters or contains such characters, CRM Analytics will prefix the header with "X" to ensure compatibility with internal naming conventions. This behavior ensures that the column can be referenced in the platform without causing errors or conflicts.
NEW QUESTION # 102
What is the purpose of the CRM Analytics Dashboard Inspector?
Answer: A
Explanation:
The CRM Analytics Dashboard Inspector is a powerful tool used to troubleshoot and optimize dashboards. Its primary function is to display the underlying SAQL (Salesforce Analytics Query Language) query executed for each widget. It helps users see the final query that is run and the corresponding results. This feature allows CRM Analytics consultants and developers to diagnose issues, optimize performance, and understand how data is being processed in the dashboard.
While the Inspector helps view execution times and identify bottlenecks, it does not automatically resolve performance issues (which is why option B is incorrect). It simply provides visibility into query performance and execution details, allowing the user to make manual optimizations.
NEW QUESTION # 103
CRM Analytics users at Cloud Kicks are granted access to an app with specific dashboards. When trying to download a specific widget, they are unable to do so.
What is causing the issue?
Answer: B
Explanation:
As in the previous question, the issue is related to permissions. Users can be granted access to dashboards and apps, but if their permission set does not explicitly allow the "Download Data" action, they will not be able to download the widget data. This is a common issue encountered when users have restricted permissions, especially in environments where security and data access are tightly controlled.
NEW QUESTION # 104
A CRM Analytics consultant wants to move a dataflow to a recipe in order to use aggregation nodes. To do so, they use the Dataflow to Recipe convertor and the recipe runs successfully.
However, they are unable to see the aggregated data in the dataset.
What is causing the issue?
Answer: A
NEW QUESTION # 105
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