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| Section | Objectives |
|---|---|
| Topic 1: Dashboard and App Development | - Interactive filters and user experience optimization - Lens creation and dashboard design |
| Topic 2: Data Management and Preparation | - Dataset creation and configuration in CRM Analytics - Data transformation and dataflows |
| Topic 3: Data Integration and Connectivity | - Connecting external data sources - Data synchronization and refresh strategies |
| Topic 4: Einstein Discovery | - Model building and predictions - Insights interpretation and recommendations |
| Topic 5: Security and Access | - App and dataset permissions - Row-level security and sharing rules |
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問題 #42
A consultant sets up a Sales Analytics templated app that is very useful for sales operations at Universal Containers (UC). UC wants to make sure all of the data assets associated with the app, including:
recipes, dataflows, connectors, Einstein Discovery models, and prediction definitions are refreshedeveryday at
6:00 AM EST.
How should the consultant proceed?
答案：C
問題 #43
consultant is reviewing a model that is set to maximize the daily sales quantity of consumer products in stores, and they see this recommendation.
Which action should the consultant take?
答案：A
解題說明：
Upon reviewing the data model and noticing the high correlation alert between 'Store' and daily sales quantity, the appropriate action is to verify with the client their expectations regarding the influence of the Store field on daily sales. Here's the rationale:
* Understanding the Role of 'Store' in the Model: Before making any changes to the model, it's crucial to understand whether the 'Store' field is expected to be a strong predictor based on the business context. If the client expects that different stores inherently have different sales volumes due to factors like location, size, or customer base, this correlation may be both meaningful and desired.
* Potential Data Leakage: High correlation warnings can sometimes indicate data leakage, where a predictor (like 'Store') might inadvertently include information about the outcome variable (daily sales quantity). It's essential to verify whether this correlation makes sense logically or if it's skewing the model predictions.
* Client Consultation: Consulting with the client helps ensure that any modeling decisions align with their business knowledge and expectations. It's about validating the model against real-world expectations and ensuring it remains a useful tool for decision-making.
By taking these steps, the consultant not only adheres to best practices in data science by validating model inputs and their implications but also ensures that the model aligns with the client's business strategies and operational realities.
問題 #44
A CRM Analytics consultant is asked to make changes to the current sales dashboard at Cloud Kicks. The dashboard is crucial to track the daily sales performance of the company and needs to be available for other users while the consultant works on the changes.
How should the consultant proceed to update the dashboard?
答案：B
問題 #45
A project team member uploads a CSV file to CRM Analytics, and they notice a few records failed during the upload. The manager wants to view the error log generated so this can be fixed and uploaded again. The manager has the CRM Analytics administrator permissionbut is unable to download the error log details.
Why is the manager unable to download the log details?
答案：A
解題說明：
In CRM Analytics, when a CSV file is uploaded and errors occur during the upload process, an error log is generated. However, only the user who uploaded the external data file can download the error log, even if other users have administrative permissions. This restriction ensures that only the user responsible for the data upload can access the details to resolve the issues.
Reference: The limitations and permissions surroundingexternal data uploadsand error logs are outlined inCRM Analyticspermissions documentation.
問題 #46
consultant is reviewing a model that is set to maximize the daily sales quantity of consumer products in stores, and they see this recommendation.
Which action should the consultant take?
答案：A
解題說明：
Upon reviewing the data model and noticing the high correlation alert between 'Store' and daily sales quantity, the appropriate action is to verify with the client their expectations regarding the influence of the Store field on daily sales. Here's the rationale:
* Understanding the Role of 'Store' in the Model: Before making any changes to the model, it's crucial to understand whether the 'Store' field is expected to be a strong predictor based on the business context. If the client expects that different stores inherently have different sales volumes due to factors like location, size, or customer base, this correlation may be both meaningful and desired.
* Potential Data Leakage: High correlation warnings can sometimes indicate data leakage, where a predictor (like 'Store') might inadvertently include information about the outcome variable (daily sales quantity). It's essential to verify whether this correlation makes sense logically or if it's skewing the model predictions.
* Client Consultation: Consulting with the client helps ensure that any modeling decisions align with their business knowledge and expectations. It's about validating the model against real-world expectations and ensuring it remains a useful tool for decision-making.
By taking these steps, the consultant not only adheres to best practices in data science by validating model inputs and their implications but also ensures that the model aligns with the client's business strategies and operational realities.
問題 #47
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