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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions (Q38-Q43):
NEW QUESTION # 38
Cloud Kicks (CK) wants to use CRM Analytics to analyze trends of its sales pipeline in order to accelerate the company's sales process. To do so, CK needs to know the average time an opportunity spends in each stage. The data can be found in the Opportunity History object, but the value is not pre- calculated in Salesforce, so a consultant recommends using a recipe to calculate it.
How should the consultant use a recipe to calculate the average time an opportunity spends in each stage?
- A. An aggregate transformation with offset parameter to calculate the duration
- B. Flatten transformation with offset expressions to calculate the duration
- C. Custom transformation with a multiple row formula to calculate the duration
Answer: C
NEW QUESTION # 39
Acompany wants to create a timeline chart to visualize the evolution of its Closed Won opportunities.
What are the required parameters to build a lens that displays output similar to the image shown?
- A. 1 measure, 0 groupings if trellis Is disabled, or 0-2 groupings If trellis is enabled
- B. 1 measure, 1-2 groupings if trellis is disabled, or 1-4 groupings if trellis is enabled
- C. 1 measure, 1 grouping by a date field, and either 0-1 groupings groupings by a dimensionif trellis is disabled, or 0-2groupings if trellis is enabled
Answer: C
Explanation:
To create a timeline chart similar to the one shown, the following parameters are typically required:
* 1 Measure: This could be the count of Closed Won opportunities or any other relevant metric that needs to be tracked over time.
* 1 Grouping by a Date Field: This is essential to plot the timeline effectively. The date field would typically be the close date of the opportunities.
* Additional Groupings: Depending on the complexity and the detail needed, additional groupings can be added. For example, grouping by region or product line can provide more insights into the timeline. If trellis is used, it allows for the creation of multiple smaller charts within the main chart, each representing a slice of data based on the additional groupings.
This setup helps visualize the evolution of Closed Won opportunities over time, making it easy to spot trends, seasonal patterns, or other relevant insights.
NEW QUESTION # 40
Universal Containers builds a new sales dashboard and wants to makesure account managers can access the dashboard while traveling.
What should the consultant consider doing in this process?
- A. Enable mobile optimization in the analytics settings under Setup.
- B. Make sure the dashboard automatically is optimized for mobile viewing.
- C. A Set the optimal Dashboard width for the Phone layout to get a more accurate preview.
Answer: C
NEW QUESTION # 41
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?
- A. Self assign as a dashboard publisher and make the changes to the dashboard in draft mode while maintaining a previous version live.
- B. Wait for a period of least usability or the dashboard to edit it.
- C. Clone the dashboard to a new one, apply the changes, share the new dashboard with the users, and delete the old one.
Answer: A
NEW QUESTION # 42
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?
- A. Remove the Store field from the model definition, because that is the recommended action.
- B. Verifyclient expectations that Store is a strong predictor for daily sales quantity.
- C. Ignore alert; the explanation of variation is only 35%, which is below 50%,
Answer: B
Explanation:
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.
NEW QUESTION # 43
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