Your company plans to use an AI-powered solution to analyze customer feedback for insights related to future product designs. You need to mitigate the privacy risks associated with the solution. What is the best approach to achieve the goal? Select the BEST answer.

• A.Anonymize the source data to remove customer Personally Identifiable Information (PII) from the feedback.
• B.Delete all the feedback data immediately after analysis.
• C.Ensure that feedback is attributable to the customer so that it can be deleted upon request.
• D.Limit privacy reviews to the final stage of the AI project lifecycle.

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- Select the answer that correctly completes the sentence.
The primary goal of generative AI is __________.

Correct Answer:

Explanation:
to create new content, such as text, images, or code.
Generative AI is defined by its ability to produce new outputs -content that did not previously exist in exactly that form-based on patterns learned from large datasets. That content can be text (emails, summaries, policies), images (design mockups, marketing visuals), code (snippets, scripts), audio, and more. Therefore, the correct completion is "to create new content, such as text, images, or code." The other options describe different AI categories. "Analyze trends and classify data sources" is primarily analytical/classification work, typically associated with traditional machine learning models (for example, clustering, categorization, fraud classification). "Make predictions based on historical data" is predictive AI (forecasting demand, predicting churn, estimating failure probability). While generative AI can assist those workflows by explaining results or drafting narratives, its primary purpose is not classification or forecasting-it is content synthesis.
In practical business value terms, this is why generative AI is commonly deployed for productivity tasks like drafting and rewriting content, summarizing long documents, generating customer communications, creating knowledge assistants, and producing structured outputs (tables, bullet lists, JSON) from unstructured prompts.
The model's differentiator is its ability to transform instructions and context into coherent, human-like content.

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Your company is developing an AI-powered customer support agent.
You need to ensure that the solution follows Microsoft responsible AI principles.
Which two actions should you perform? Select the two BEST answers. Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

• A.Retain all customer conversations.
• B.Test the agent to ensure that responses are inclusive and culturally sensitive.
• C.Enable the agent to operate independently.
• D.Ensure that the agent can be used for multiple purposes.
• E.Provide a clear disclaimer that users are interacting with an AI solution.

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An organization is deploying generative AI solutions and wants to ensure systems are explainable, auditable, and accountable to stakeholders. Why is this focus critical when implementing AI?

• A.It automates infrastructure provisioning
• B.It improves GPU processing efficiency
• C.It eliminates the need for compliance reviews
• D.It ensures AI systems operate transparently and can be trusted

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HOTSPOT - Select the answer that correctly completes the sentence.
You use __________ to train a model that will forecast product demand based on historical sales data.

Correct Answer:

Explanation:
Azure Machine Learning
Forecasting product demand from historical sales data is a predictive analytics / machine learning use case. It typically requires selecting an appropriate forecasting approach (for example, regression, tree-based methods, or time-series models), preparing and splitting historical data, training and validating the model, tuning hyperparameters, and then deploying the model for ongoing inference. The Microsoft service designed to support that end-to-end ML lifecycle is Azure Machine Learning, which is why it correctly completes the sentence.
Azure Machine Learning provides the tooling and infrastructure to: manage datasets, run training jobs on scalable compute, track experiments, compare model performance, register models, and operationalize them through managed endpoints and pipelines. This makes it well-suited for iterative forecasting work, where you may retrain on new data regularly, monitor drift, and update models as product lines, promotions, or seasonality patterns change.
The other options do not directly fit "train a model" for forecasting. Azure AI Search is an indexing/retrieval service used to search and ground generative AI responses, not for training predictive models. Azure OpenAI provides access to large language and multimodal models for generative tasks (drafting, summarizing, Q & A) and is not the primary platform for building classical forecasting models. Microsoft Foundry is a broader platform experience for building and governing AI apps and agents, but the specific service for training a forecasting model on historical sales data is Azure Machine Learning.

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