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Free ANC301 Exam Braindumps

Universal Containers (UC) is looking to create a dashboard for whitespace analysis. UC wants to view a particular customer and see what similar customers have bought.
Which recipe transformation is helpful for the consultant to use while creating the dataset?

  1. Timeseries Forecasting
  2. Cluster
  3. Predict Missing Values

Answer(s): B

Explanation:

Cluster transformation is a powerful tool in CRM Analytics recipes used for grouping similar records together based on shared attributes. In this scenario, Universal Containers (UC) wants to perform whitespace analysis by viewing a particular customer and comparing their purchase history with similar customers. The Cluster transformation would help in identifying groups of customers who have made similar purchases. This can then be used to provide insights into what the viewed customer might also be interested in purchasing, based on similar customer behaviors.


Reference:

CRM Analytics Recipes and Transformation



A consultant is preparing a dataset to predict customer lifetime value and is collecting data from a questionnaire that asks for demographic information. A very small number of respondents fill in the Income box, but the consultant thinks that it is an informative column even though it only represents 1% of respondents.
What should the consultant do?

  1. Fill in the missing data with an average of all incomes.
  2. Apply the predict missing values transformation in recipe nodes.
  3. Drop the field as it will be difficult to get future respondents.

Answer(s): B

Explanation:

In CRM Analytics, when dealing with incomplete data, specifically when certain respondents have not filled out fields like income, the Predict Missing Values transformation in a recipe is highly effective. This transformation allows you to predict values for missing fields based on patterns from the existing data. Since the consultant finds this field informative despite having data from only 1% of respondents, applying this transformation can estimate these missing values, which ensures that the dataset remains useful for predictive purposes without discarding important variables.


Reference:

CRM Analytics Recipes and Predict Missing Values



The CRM Analytics consultant at Universal Containers notices that some users have access to sensitive data and dashboards they should not have access to in the Manager's app.
How should the consultant fix the problem?

  1. Develop separate dashboards and datasets and put them in the Manager's app.
  2. Apply data encryption using Salesforce Shield.
  3. Create separate apps, datasets, and dashboards, and share them with the proper users.

Answer(s): C

Explanation:

To address issues with unauthorized access to sensitive data and dashboards, the best practice is to create separate apps, datasets, and dashboards for different user groups and then manage their sharing settings appropriately. This allows you to maintain data security while ensuring that users only access the data and insights that are relevant to their roles. In this scenario, applying separate apps for managers with defined sharing rules will prevent users who shouldn't have access from seeing sensitive data.


Reference:

Managing Data Access and Sharing in CRM Analytics



An CRM Analytics consultant is working with Ursa Major Solar to build a dashboard to understand customer renewals. Each subscription is captured as a Closed Won Opportunity within Salesforce and a single Account should only have one active subscription. The consultant notices the Opportunity record does NOT specify whether it is a renewal or a net new subscription.
Which data transformation should the consultant use to determine if a subscription is new or a renewal?

  1. Flatten
  2. Custom Multiple row formula
  3. Custom Formula

Answer(s): C

Explanation:

To determine whether a subscription is new or a renewal from the Opportunity records in Salesforce, the consultant should utilize a Custom Formula in the data transformation process. Here's the rationale:
Custom Formula Usage: By employing a custom formula, the consultant can create a logical expression that checks the historical data associated with each account. If an account has previous closed-won opportunities, any new opportunities can be labeled as renewals; otherwise, they are considered new subscriptions.
Data Insight: This method provides a straightforward way to derive new insights (new vs. renewal) directly from existing data without altering the data structure itself, making it a non-invasive and efficient solution.
Implementation: The custom formula can be applied in a recipe or directly within a dataflow in CRM Analytics, offering flexibility in how and where the transformation is executed.






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