試験AB-100 トピック2 問題97 スレッド
Microsoft AB-100のリアル試験問題集
問題 #: 97
トピック #: 2
問題 #: 97
トピック #: 2
A company has an Al agent that automates the review of customer feedback stored in a cloud database.
You plan to generate monthly reports from the agent ' s output to provide insights into customer sentiment and guide product development and marketing.
You need to ensure that the data ingested by the agent is clean and suitable for the intended use.
What should you do to prepare the data?
You plan to generate monthly reports from the agent ' s output to provide insights into customer sentiment and guide product development and marketing.
You need to ensure that the data ingested by the agent is clean and suitable for the intended use.
What should you do to prepare the data?
おすすめの解答：C 解答を投票する
The requirement is to make sure the data ingested by the agent is clean and suitable for the intended use , which is producing monthly sentiment insights to guide product development and marketing .
The best answer is C. Identify and address biased data.
Why C is correct:
* For sentiment analysis and reporting, biased data can distort conclusions and produce misleading recommendations
* Data preparation should include checking for skew, unfair representation, missing segments, and other quality issues that affect downstream decisions
* This aligns with responsible AI and sound analytics practice
Why the other options are not correct:
* A. Ensure that the size of the database does not exceed 100 GB is unrelated to data quality or suitability
* B. Translate the data into a single language might help in some implementations, but it is not universally required and is not the primary data-quality action here
* D. Sort the database by customer last name has no relevance to model readiness or report quality
The best answer is C. Identify and address biased data.
Why C is correct:
* For sentiment analysis and reporting, biased data can distort conclusions and produce misleading recommendations
* Data preparation should include checking for skew, unfair representation, missing segments, and other quality issues that affect downstream decisions
* This aligns with responsible AI and sound analytics practice
Why the other options are not correct:
* A. Ensure that the size of the database does not exceed 100 GB is unrelated to data quality or suitability
* B. Translate the data into a single language might help in some implementations, but it is not universally required and is not the primary data-quality action here
* D. Sort the database by customer last name has no relevance to model readiness or report quality
石原** 2026-07-08 12:03:55
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