Salesforce Updated Analytics-Con-201 Exam Questions and Answers by boris

For Cloud Kicks’ requirement to refresh dashboard data synchronously with the sync of an external connection to Google BigQuery, the most efficient method is:

Event-Based Triggers: Using CRM Analytics, you can set up recipes to run on an event-based trigger. This ensures that the recipe (which processes the data for the dashboard) only runs after the external data sync is completed.

Salesforce External Connection Syncs: By specifically targeting the synchronization event of the Salesforce external connection with Google BigQuery, the recipe ensures data consistency and timeliness without manual intervention.

Automation and Efficiency: This approach minimizes latency and maximizes data freshness, aligning data processing closely with data availability, thus enhancing dashboard accuracy.

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.