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[Hardware] Latest Analytics-Con-201 Exam Simulator | Analytics-Con-201 Examinations Actual

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【Hardware】 Latest Analytics-Con-201 Exam Simulator | Analytics-Con-201 Examinations Actual

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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions (Q97-Q102):NEW QUESTION # 97
A company realizes it has a lot of rich information around its cases, but unfortunately, most of this isunstructured/textual data. Thecompany is exploring how to include some of this information in its case prioritization.
Which option within CRM Analytics should a consultant leverage?
  • A. Detect Sentiment transformation in Recipes
  • B. Cluster transformation in Recipes
  • C. Bucket transformation in Recipes
Answer: A
Explanation:
For a company with a wealth of unstructured textual data in their cases, the "Detect Sentiment" transformation within CRM Analytics Recipes is a crucial tool. This transformation analyzes the sentiment of the text data- whether it's positive, neutral, or negative-and this insight can be highly valuable in case prioritization processes. Here's why this transformation is useful:
* Insight into Customer Sentiments: By detecting sentiment, the company can prioritize cases based on the urgency and emotional tone expressed in the text, which might indicate customer dissatisfaction or urgency.
* Automation and Efficiency: Automatically categorizing cases based on sentiment can streamline workflows and ensure that critical cases are handled promptly.
* Enhanced Customer Service: Responding to negative sentiments swiftly can improve customer satisfaction and potentially mitigate issues before they escalate.

NEW QUESTION # 98
A consultant is building a CRM Analytics dashboard for Universal
Containers. The consultant has enabled data sync to increase the
speed of datasets refreshing.
How often will the data on the dashboard be refreshed?
  • A. When the dashboard viewer clicks the Refresh button
  • B. When data sync runs to completion, and then dataflow/recipe runs to completion
  • C. When dataflow/recipe runs to completion, and then data syne runs to completion
Answer: B

NEW QUESTION # 99
After the initial creation of a model, the first model insight explains
93% of the variation of the outcome variable. This is unusually high.
What is the most likely reason for this?
  • A. The model contains too many outlier values.
  • B. The outcome variable may be causing data leakage.
  • C. The dataset contains multiple dominant values.
Answer: B

NEW QUESTION # 100
A system administrator and a CRM Analytics consultant are working together on deploying arecipe/dataflow and a dataset to another org. Prior to this deployment, a package was deployed with all the custom fields used in the dataflow and dataset.
While running the recipe/dataflow in the target environment, the consultant encounters multiple errors related to these custom fields.
How should this be resolved?
  • A. Check whether the consultant has been provided access to these custom fields on the target org.
  • B. Check whether the Analytics Integration user has been provided access to these custom fields on the target org.
  • C. Check whether the system administrator has been provided access to these custom fields on the target org.
Answer: B

NEW QUESTION # 101
A team of CRM Analytics developers has been working on an existing recipe to add new derived fields. The edited version has been failing ever since, and management is requesting that the dashboard show refreshed data while they work on the edits.
How can the developers add new fields while keeping the dataset refreshed?
  • A. A recipe for the new fields and when that is successful, add it to the existing recipe as a join node.
  • B. Refresh the dataset after working hours to avoid the edited version from failing.
  • C. Clone the existing recipe to add fields and roll back the original recipe to the last working version.
Answer: C
Explanation:
When faced with the need to continue refreshing data while developing new features in a recipe, the best practice is:
* Clone the Existing Recipe: By cloning the recipe, developers can experiment with adding new fields and transformations without affecting the production data flow. This allows for testing and development in a sandbox-like environment.
* Roll Back to a Stable Version: Rolling back the original recipe to the last stable version ensures that the production dashboards continue to receive refreshed data, maintaining business operations without disruption.
This approach not only ensures data continuity but also provides a safe environment to address any issues that may arise from new developments.

NEW QUESTION # 102
......
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