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  1. Home
  2. Microsoft Certification
  3. AB-731 Exam
  4. Microsoft.AB-731.v2026-10-16.q88 Dumps
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Question 71

You have a large language model (LLM) that was pretrained by using public data.
You want the LLM to generate responses that incorporate frequently updated proprietary content, such as internal documents and knowledge bases.
You need to recommend a solution to ensure that the LLM uses the most current information without retraining the model.
What should you include in the recommendation?

Correct Answer: A
Retrieval-Augmented Generation (RAG) is the optimal, industry-standard approach for incorporating frequently updated proprietary data into an LLM's output. It works by connecting a pre-trained LLM to an external, dynamic knowledge base--such as company documents--using vector databases to retrieve relevant context in real-time, reducing hallucinations and eliminating the need for constant, costly retraining.
Reference:
https://shiftasia.com/community/retrieval-augmented-generation-rag-a-comprehensive-guide-to-smarter-more-accurate-ai
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Question 72

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
Answer Area
* A generative AI solution is well-suited to predict next-quarter sales trends. Answer: No
* A generative AI solution can summarize lengthy policy documents. Answer: Yes
* A generative AI solution can create product descriptions from product specifications. Answer: Yes
* No - Predicting next-quarter sales trends is primarily a forecasting/predictive analytics problem.
Microsoft differentiates predictive AI (forecasting outcomes from historical patterns) from generative AI (creating content like text, images, or code). While you can use LLMs to assist analysts (explain trends, draft narratives), the core forecasting model is typically traditional ML/time-series methods rather than generative AI as the main engine.
* Yes - Summarization is a classic, high-value generative AI capability. Given a long policy, an LLM can compress it into executive summaries, key obligations, risks, and action items, often with formatting constraints (bullets, sections, "do/don't" lists). Microsoft highlights summarization and analysis as common generative AI use cases in business contexts.
* Yes - Generative AI is well-suited to transform structured inputs (features/specs) into natural- language outputs (product descriptions). This is straightforward "content generation," where you control tone, length, and required fields (benefits, differentiators, disclaimers). Microsoft also points to generating product descriptions and similar marketing/customer-facing text as a practical generative AI scenario.
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Question 73

Your company creates a custom Azure Machine Learning model that uses a generative AI assistant.
The model initially delivers strong results. However, six months later, the model predictions become noticeably less accurate.
What is a possible cause of the issue?

Correct Answer: B
This phenomenon is known as data drift.
It is one of the top reasons model accuracy declines after deployment. In the context of a generative AI assistant, data drift occurs when the statistical properties or distributions of the input data (the prompts or context provided by users) change significantly from what the model was originally trained on.
Key Causes of Data Drift
Changing User Behavior: Users may start interacting with the assistant differently, using new slang, jargon, or evolving topics of interest.
Real-World Changes: External events (like a pandemic or economic shift) can suddenly make the model's training data outdated and irrelevant.
Data Pipeline Issues: Changes in how data is collected, such as updated sensors or modified web forms, can alter the format or scale of inputs.
Seasonality: Patterns may fluctuate based on the time of year, such as different holiday shopping behaviors.
Impact on Generative AI
For a generative assistant, data drift often manifests as:
Reduced Relevance: Outputs feel outdated or fail to address contemporary topics.
Increased Hallucinations: When faced with unfamiliar input patterns, the model may produce factually incorrect or nonsensical responses.
Loss of Quality: Outputs may become less creative, more repetitive, or exhibit biased behavior.
Reference:
https://nexla.com/ai-infrastructure/data-drift
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Question 74

Your company uses a fine-tuned generative AI solution trained on data that is representative of the general population. You discover that some of the generated responses include inappropriate or exclusionary language based on ableist assumptions. You need to prevent the inappropriate responses. Your solution must minimize costs. What should you do?

Correct Answer: B
The problem is harmful output language (inappropriate or exclusionary/ableist content). The requirement says you must prevent those responses while minimizing costs . The most cost-effective and direct control is to add a content-moderation filter (B) to screen and block (or rewrite/escalate) responses that violate your safety or inclusion standards. Moderation can be applied at the output stage (and often also at input) without retraining the model, which keeps costs and delivery time low. It also provides an immediate safety layer even if the underlying model occasionally produces biased or exclusionary phrasing.
Option A is not reliable: a newer model version might reduce issues but does not guarantee elimination of ableist language, and you still need policy enforcement. Option C (retraining on only inclusive content) can help, but it is typically expensive (data curation, re-training, re-evaluation, regression testing, re-deployment) and not the "minimize costs" path-also it can reduce coverage/utility if overly restrictive. Option D is clearly wrong because it would amplify the harmful behavior.
In practice, the lowest-cost, high-impact approach is to implement moderation thresholds and handling actions (block, warn, regenerate with constraints, human review) and then, if needed, follow up later with deeper mitigations like prompt constraints, targeted fine-tuning, red-teaming, and continuous evaluation.
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Question 75

- Select the answer that correctly completes the sentence.
An organization that runs continuous, large-scale workloads with Azure OpenAI models should choose the
__________ pricing model.

Correct Answer:

Explanation:
Provisioned (PTUs)
For continuous, large-scale workloads, the key needs are predictable throughput, consistent latency, and cost/performance stability . Azure OpenAI Provisioned Throughput Units (PTUs) are designed for this scenario because you reserve model capacity to meet sustained demand. This reduces the risk of variability that can occur with purely on-demand usage during peak periods and provides a more predictable operating model when the workload is always "on" and high volume.
Standard (On-Demand) is best when usage is variable or you're starting small (PoCs, pilots, spiky workloads), because it is flexible pay-as-you-go but can be less predictable at sustained scale. Batch API is optimized for asynchronous, non-interactive processing where you can tolerate delayed results (for example, large offline summarization jobs), not for always-on, real-time interactions at scale.
Therefore, for continuous high-volume production workloads, Provisioned (PTUs) is the best pricing model choice.
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