AI-103 Practice 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?

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

Answer

Use Read API

Answer

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

Answer

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.

Answer

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.

Answer

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

Answer

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.

Answer

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

Question 11

Your company wants to track and allocate Azure AI service costs by department. Which TWO mechanisms can assist with this requirement? (Choose two.)

Accepted Answer

Using Azure Cost Management filters and cost allocation rules to generate chargeback reports.. Option A is correct because Azure Cost Management supports filters (by resource group, tag, service, subscription, etc.) and cost allocation rules that let you split shared costs across departments, enabling accurate chargeback/showback reports for Azure AI services. Option E is correct because applying department-specific tags such as Department: Marketing to Azure AI resources lets Cost Management group and filter spend by tag, which is the standard way to attribute AI service costs to each department. Option B is incorrect because a single untagged, unsegmented subscription provides no dimension to separate or allocate costs by department. Option C is incorrect because deactivating Azure Cost Management removes the very reporting and allocation capability needed for cost tracking. Option D is incorrect because the Free (F0) tier only affects pricing/limits of certain AI services and does not provide any cost tracking or allocation mechanism.

Answer

Sharing a single Azure subscription across all departments without using tags or resource groups.

Answer

Deactivating Azure Cost Management to reduce management overhead.

Answer

Provisioning all AI resources in the Free (F0) tier.

Question 12

Which TWO factors directly influence the embedding quality and retrieval relevance in an Azure AI Search vector retrieval pipeline? (Choose two.)

Accepted Answer

The choice of embedding model (e.g., text-embedding-3-large vs text-embedding-3-small) and its dimensionality.. Option B is correct because the embedding model determines the semantic quality of the vectors stored in the Azure AI Search index: text-embedding-3-large produces higher-fidelity, higher-dimensional vectors (3072 dimensions) than text-embedding-3-small (1536 dimensions), directly affecting how well semantic similarity matches user intent during vector search. Option E is correct because chunking strategy and chunk size control the granularity of the indexed content; chunks that are too large dilute the embedding with unrelated content, while chunks that are too small lose context, so both the segmentation method (e.g., fixed-size, sentence-aware, or document-layout-aware) and the chunk length materially affect retrieval relevance. Options A and C are incorrect because background color and font family are visual formatting attributes that are not captured by text embedding models and have no bearing on vector similarity. Option D is incorrect because the end user's browser version affects client-side rendering and request handling, not the embedding quality or the relevance scoring performed by Azure AI Search.

Answer

The background color of the source documents in blob storage.

Answer

The font family used in the original PDF documents.

Answer

The client browser version used by the end user.

Question 13

You are using an Azure AI model that supports versioning. You want to perform an A/B test by routing 10% of traffic to a new version. Where do you configure this?

Accepted Answer

The 'Traffic' configuration in the Azure AI endpoint.. Azure AI endpoints expose a 'Traffic' configuration where you assign percentage weights to deployed model versions, enabling A/B testing by routing a fraction of requests to a new version. Setting 10% to the new version and 90% to the current one achieves the desired split. This is native to the endpoint and requires no external load balancer.

Answer

Azure App Service routing rules.

Answer

Azure Load Balancer backend pools.

Answer

Azure Traffic Manager DNS settings.

Question 14

As part of responsible AI practices in Azure AI Foundry, which TWO actions should you perform to evaluate and mitigate model fairness and safety risks before deployment? (Choose two)

Accepted Answer

Run automated evaluations in Azure AI Foundry to measure metrics such as groundedness, relevance, and protected material presence.. Option B is correct because Azure AI Foundry's automated evaluation flows let you quantify safety and quality metrics—groundedness, relevance, coherence, fluency, and protected-material/hate-and-unfairness scores—so you can objectively assess fairness and safety risks before deployment. Option D is correct because adversarial testing (red teaming) in Azure AI Foundry systematically probes the model with prompt injections, jailbreak attempts, and other malicious inputs to surface unintended behaviors and safety gaps that must be mitigated prior to release. Option A is wrong because internal-only audiences do not exempt an application from responsible AI security and safety reviews; risk assessments apply regardless of user population. Option C is wrong because removing system prompts strips guardrails and grounding instructions, increasing unsafe or off-topic outputs rather than enabling meaningful evaluation. Option E is wrong because hardcoding responses is not a fairness/safety evaluation technique and would defeat the purpose of testing a generative model's behavior.

Answer

Bypass security reviews if the application is intended for internal enterprise employees only.

Answer

Remove all system prompts to allow the model complete autonomy during evaluation runs.

Answer

Hardcode responses for all possible user inputs to eliminate model non-determinism entirely.

Question 15

You are using Custom Vision and want to programmatically publish a trained iteration so that prediction endpoints can use it. Which API operation should you call?

Accepted Answer

POST /customvision/v3.3/Training/{projectId}/iterations/{iterationId}/publish. Publishing a trained Custom Vision iteration so prediction endpoints can consume it is done by calling POST /customvision/v3.3/Training/{projectId}/iterations/{iterationId}/publish. The publish operation makes the iteration available at the prediction URL, optionally under a named prediction resource. This is the required step before any prediction calls can target that iteration.

Answer

GET /customvision/v3.3/Training/{projectId}/iterations/{iterationId}/make-default

Answer

PUT /customvision/v3.3/Prediction/{projectId}/iterations/{iterationId}/activate

Answer

PATCH /customvision/v3.3/Training/{projectId}/projects/{projectId}

Microsoft Certified: Azure AI Apps and Agents Developer Associate AI-103 practice test

Three ways to study

Study AI-103 by domain

Study AI-103 by topic

Text Analysis practice questions

Plan And Manage AN Azure AI Solution practice questions

Implement Generative AI And Agentic Solutions practice questions

Computer Vision practice questions

Information Extraction practice questions

AI-103 fundamentals practice questions

AI-103 scenario practice questions

AI-103 troubleshooting practice questions

Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103) (AI-103) practice questions