AI-103 Practice Test 1 — 10 Questions

Question 1

You are using Azure AI Vision Read API in your application. You want to ensure that the extracted text blocks return word-level bounding polygons rather than just line-level bounding boxes. What parameter or response structure should you examine?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Examine the 'words' array within each returned 'lines' object in the JSON response payload.. The Azure AI Vision Read API returns OCR results as a hierarchy of pages → lines → words, and each word object contains a bounding polygon (boundingPolygon) with four (x,y) points. To get word-level polygons, you examine the 'words' array nested inside each 'lines' object in the JSON response — no query parameter is needed.

Answer

Add ?wordLevel=true to the query string.

Answer

Call the legacy OCR endpoint instead.

Answer

Set the model-version parameter to v2.0.

Question 2

You need to extract data from a highly varied set of tax forms where the layout changes frequently. Which approach is most efficient?

Accepted Answer

Use a Neural custom model. A Neural custom model in Azure AI Document Intelligence is trained on your own labeled data and can generalize to layout variations, making it ideal for frequently changing tax forms. It learns the structure and key-value pairs from examples rather than relying on fixed templates. This provides the most efficient extraction for highly varied documents.

Answer

Use a Template model

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Use Read API

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Use General Document model

Question 3

An agent is designed to summarize meeting transcripts but keeps including internal meeting notes in the final summary. What is the most effective way to address this?

Accepted Answer

Update the system prompt to clarify which sections of the transcript to include.. The system prompt is the primary control surface for defining an agent's scope, tone, and what content it should include or exclude. When an agent incorrectly includes internal meeting notes, the root cause is usually ambiguous instructions about which transcript sections are in-scope, so clarifying the inclusion/exclusion rules in the system prompt directly fixes the behavior. This is a prompt engineering fix, not a model parameter fix, because the issue is about instruction-following scope rather than randomness or creativity.

Answer

Lower the temperature.

Answer

Switch to a different model.

Answer

Add a 'stop_sequence' to the API call.

Question 4

You are implementing a RAG pipeline where documents are chunked before embedding generation. You notice that semantic sentences are frequently split across chunk boundaries, resulting in degraded retrieval quality. Which chunking strategy should you implement to resolve this?

Accepted Answer

Sliding window chunking with token overlap. Sliding window chunking with token overlap is correct because it preserves semantic continuity by carrying a portion of the previous chunk's tokens into the next chunk. This overlap ensures that sentences or ideas spanning a boundary appear intact in at least one chunk, so the embedding captures the full semantic unit. It directly addresses the root cause of degraded retrieval: fragmented context at chunk edges.

Answer

Fixed-character chunking with zero overlap

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Paragraph-only splitting with maximum size limits of 10,000 tokens

Answer

Random token slicing

Question 5

You are developing an Azure AI Search index for a RAG application. You need to ensure that the search results include semantic relevance scores to improve the quality of the model's grounding. Which feature must you enable?

Accepted Answer

Enable Semantic Ranker on the search service. Semantic Ranker is the Azure AI Search feature that applies a Microsoft-hosted language model to re-rank the top results from an initial retrieval pass, producing semantic relevance scores (semantic captions and answers) that improve grounding quality for RAG. Enabling it on the search service (Standard tier or higher) and then configuring it on the index/query is what surfaces the semantic score. Without Semantic Ranker, results are ranked only by BM25 or vector similarity, not by deep semantic understanding.

Answer

Use Custom Analyzers

Answer

Enable Knowledge Store

Answer

Enable Hybrid Search with vectorization

Question 6

Which TWO components are essential for creating an effective system prompt?

Accepted Answer

A clear definition of the agent's persona and objective.. Option A is correct because an effective system prompt must establish the agent's persona and objective, giving the model a clear role, tone, and goal that shape all subsequent responses. Option D is correct because system prompts need explicit constraints and instructions for edge cases or disallowed topics, which set behavioral boundaries and safety guardrails for the agent. Options B, C, and E are not essential: authorized-user lists are typically enforced by authentication and access-control systems rather than the prompt, the base model's full training dataset is not something the prompt author supplies, and data center hardware details are irrelevant to prompt design.

Answer

A list of all users authorized to use the agent.

Answer

The full training dataset of the base model.

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A list of all hardware components in the data center.

Question 7

You are deploying a model that requires significant GPU resources. You want to ensure the deployment only scales when the average CPU load exceeds 70%. Which feature of Azure AI managed endpoints do you configure?

Accepted Answer

The 'Endpoints' deployment configuration 'autoscaling' settings.. Azure AI managed online endpoints support autoscaling rules configured on the deployment, where you define a metric (such as average CPU utilization) and a threshold (for example, 70%) that triggers scale-out or scale-in. This is configured in the deployment's 'autoscaling' settings under the endpoint configuration, allowing the deployment to add or remove instances based on load.

Answer

The 'Load Balancer' settings in the VNET.

Answer

Azure Front Door health probe settings.

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The 'Compute' instance restart policy.

Question 8

You are managing Azure OpenAI deployments and need to monitor token consumption and error rates. Which TWO Azure Monitor features should you use to analyze this telemetry? Each correct answer presents part of the solution.

Accepted Answer

Azure Monitor Metrics Explorer to graph real-time metrics like Total Calls and Latency.. Option A is correct because Azure Monitor Metrics Explorer lets you plot near real-time platform metrics for Azure OpenAI, including Total Calls, Token Usage (prompt/completion tokens), and Latency, which directly addresses monitoring token consumption and error rates at a glance. Option C is correct because Azure OpenAI diagnostic settings can route request/response logs and metrics to a Log Analytics workspace, where Kusto Query Language (KQL) queries enable deep analysis of per-request token counts, status codes, and error patterns over time. Together, Metrics Explorer provides fast visual monitoring while Log Analytics with KQL provides granular, queryable telemetry. Option B is incorrect because Azure DevOps commit history tracks source code changes, not Azure OpenAI runtime telemetry. Option D is incorrect because Microsoft 365 compliance center content search is for eDiscovery of Microsoft 365 content, not Azure Monitor telemetry. Option E is incorrect because Azure Bastion session monitoring relates to secure RDP/SSH access to VMs, not OpenAI token or error metrics.

Answer

Azure DevOps commit history dashboard

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Microsoft 365 compliance center content search

Answer

Azure Bastion session monitor

Question 9

Your team needs to receive an email alert if the error rate for your AI endpoint exceeds 5% for more than 15 minutes. What should you create?

Accepted Answer

An Azure Monitor Alert rule with an Action Group.. Azure Monitor Alert rules evaluate metrics or log queries against thresholds and trigger Action Groups, which can send emails, SMS, webhooks, or invoke Logic Apps and Functions. To alert when error rate exceeds 5% for over 15 minutes, you create a metric or log-search alert rule with the appropriate threshold and evaluation window, then attach an Action Group configured with an email receiver. This is the native, supported mechanism for metric-based alerting in Azure.

Answer

An Azure Service Health notification.

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A scheduled Azure Function that polls the API.

Answer

A Log Analytics query saved as a report.

Question 10

You are integrating Azure AI Search with Azure OpenAI for grounded chat generation. Users report that the generated answers are accurate, but the citations returned to the user point to raw internal file paths rather than user-friendly web URLs. How should you resolve this?

Accepted Answer

Store public URL equivalents in index metadata fields and configure citation mapping in your orchestration layer. Citations in grounded chat are generated from metadata fields in the Azure AI Search index; to return user-friendly URLs, you must store the public URL in a retrievable metadata field (e.g., 'url' or 'source_url') and configure your orchestration layer to map that field into the citation output. Azure OpenAI's 'on your data' feature and custom RAG pipelines both rely on index metadata for citation rendering. This is a data-modeling and orchestration fix, not a model or safety fix.

Answer

Enable Azure AI Content Safety citation filtering

Answer

Decrease the search result top_k parameter to 1

Answer

Modify the Azure OpenAI system prompt to guess web URLs based on file names