Question 1

Which method offers a swift approach to evaluating your data and assessing its quality within Salesforce?

Options :

A :

Running a specific report on data discrepancies.

B : Crafting a comprehensive data modeling strategy.

C : Using an AppExchange app specialized in data quality.

Answer: C

Question 2

What distinguishes a predictive AI model from a generative AI model?

Options :

A : Both use ML learning for the input as well as the output.

B : Both use ML for the input, but generative AI creates new content.

C : They use ML for input but require specific data types.

Answer: B

Question 3

A job recruitment platform uses an AI model to rank applicants for employers. The AI model, trained on past employment data, considers qualifications, experience, and whether the applicant had taken any extended career breaks.What is a primary ethical challenge when giving significant weight to extended career breaks?

Options :

A : The model may undervalue recent graduates or those transitioning careers

B : Emphasizing extended career breaks might inadvertently penalize applicants who took time off for personal reasons, like maternity or caregiving, perpetuating biases against certain demographics

C : The model might not adequately rank individuals with unconventional job experiences

Answer: B

Question 4

After analyzing a dataset of employee records, Alex discovers that several employees have their date of birth listed as ’00/00/0000′.

Which element of data quality is Alex observing as compromised in the dataset?

Options :

A : Accuracy

B : Completeness

C : Validity

Answer: C

Question 5

Mara, a data analyst, is trying to harness an AI tool within Salesforce to predict the next quarter’s product returns. However, after deploying the model, she finds the predicted return rates substantially off from the actual figures.

What could be a major data quality issue contributing to this discrepancy?

Options :

A : The dataset incorporates return data from different seasons, capturing a diverse range of products

B : The dataset contains many records where the reasons for returns are either missing or ambiguously stated

C : The model was trained using data from both online and in-store purchases

Answer: B

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