AI-103 Practice Exam 3 — 20 Questions

Question 1

You are designing an agentic workflow in Azure AI Foundry that requires the agent to call an external REST API. Which component should you use to define the tool interface?

Accepted Answer

Tool definition using a Python function. In Azure AI Foundry agentic workflows, tools are defined as callable functions with a name, description, and parameter schema; a Python function definition (often decorated or registered as a tool) is the standard way to expose an external REST API to the agent. The model then emits structured arguments that invoke that function. This is the correct component for defining the tool interface.

Answer

Azure AI Content Safety

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LLM output parser

Answer

Flow input schema

Question 2

You are troubleshooting an Azure AI Language asynchronous summarization job. You submitted a batch of documents using the `/analyze` endpoint with long-running operations (LRO). How should your application check the status and retrieve the results of the job?

Accepted Answer

Poll the URL provided in the 'operation-location' response header using a GET request until the status is 'succeeded'.. Azure AI Language asynchronous (LRO) jobs return an 'operation-location' header in the initial 202 Accepted response. The application must poll that URL with GET requests until the status field reads 'succeeded', then retrieve results from the same endpoint. This is the standard Azure long-running operation pattern.

Answer

Listen for incoming webhook notifications on an Azure Event Grid subscription attached to the text analytics resource.

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Query the Azure Monitor metrics API for job completion status.

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Repeatedly send synchronous POST requests to the same document endpoint until a 200 OK is returned.

Question 3

You have an Azure AI application that is hitting rate limits (429 errors). You need to determine which specific client application is causing the issue. How do you identify this?

Accepted Answer

Enable diagnostic logging to Log Analytics and run a KQL query.. Diagnostic logging to Log Analytics captures per-request telemetry including the caller identity, subscription, resource, operation name, and HTTP status code, so a KQL query filtering on ResponseCode == 429 and summarizing by CallerIpAddress or identity reveals which client is exhausting the quota. The Activity Log only records control-plane operations (resource create/update/delete), not data-plane inference calls, so it cannot show which app is making the requests.

Answer

Check the 'Activity Log'.

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Increase the resource quota in the 'Quotas' blade.

Answer

Restart the AI service.

Question 4

Which THREE of the following are components of a standard Prompt Flow DAG (Directed Acyclic Graph)?

Accepted Answer

Inputs. In a standard Prompt Flow DAG, Inputs (A) are the declared entry points that supply values (for example, a question string or a dataset column) to the flow, making them a required component. Outputs (B) are also required because they define the flow's returned values (such as the model's answer or a score) that downstream consumers or evaluations read. Tools (D) are the executable nodes of the DAG — LLM, Python, Prompt, and built-in tools — and the edges connecting them define the directed acyclic execution order. Model Training Jobs (C) are not part of a Prompt Flow DAG; training is handled by separate Azure Machine Learning job constructs, not flow nodes. Data Labeling Projects (E) are likewise a distinct Azure Machine Learning feature for annotating data and are not components of a Prompt Flow DAG.

Answer

Model Training Jobs

Answer

Data Labeling Projects

Question 5

You are evaluating an agentic workflow in Azure AI Foundry that performs multi-step reasoning. The agent occasionally enters an infinite loop, repeatedly calling the same tool with identical arguments. Which architectural pattern or configuration should you apply to prevent this behavior?

Accepted Answer

Configure a maximum iteration step limit in the agent orchestration loop. Agentic workflows in Azure AI Foundry (and similar frameworks like Semantic Kernel or AutoGen) require a bounded orchestration loop; configuring a maximum iteration or step limit causes the orchestrator to terminate or escalate when the agent exceeds the threshold, preventing infinite tool-calling loops. This is the standard architectural safeguard for multi-step reasoning agents. It directly addresses the symptom of repeated identical tool calls.

Answer

Increase the vector store chunk overlap size

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Enable Azure API Management caching for the tool endpoint

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Set the model frequency penalty to 2.0

Question 6

You are configuring a secure enterprise architecture where Azure AI Vision calls must not traverse the public internet. You create a private endpoint for your Azure AI Services resource in your Virtual Network. Which DNS configuration step is required for client applications within the VNet to resolve the private endpoint correctly?

Accepted Answer

Configure Azure Private DNS zones corresponding to the cognitive services DNS zone and link it to your VNet.. When you create a private endpoint for an Azure AI Services resource, the private IP is associated with the service's public DNS name (for example, <resource>.cognitiveservices.azure.com). For clients in the VNet to resolve that name to the private IP instead of the public IP, you must create an Azure Private DNS zone named privatelink.cognitiveservices.azure.com and link it to the VNet, with an A record mapping the resource to the private endpoint IP.

Answer

Add the endpoint IP to the local hosts file of every Azure AI Vision instance.

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Configure public DNS records on your corporate DNS server to point to the public IP.

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Modify the routing table to drop all DNS traffic.

Question 7

Which THREE attributes are critical for a successful custom model training set?

Accepted Answer

High-quality images. Option A (High-quality images) is correct because custom model training relies on OCR and layout analysis, so clear, well-lit, high-resolution scans or photos with minimal skew, blur, and noise give the model reliable visual features to learn from. Option B (Consistent labeling across all documents) is correct because supervised training requires ground-truth labels to be applied with the same schema and conventions throughout the dataset; inconsistent labels introduce contradictory signals that degrade accuracy and confuse the model. Option C (Representative samples of all document layouts) is correct because the model must generalize to every form, template, and layout it will encounter in production, so the training set must include examples covering the full variety of structures, fonts, and field placements. Option D is not required because documents need not come from a single printer; in fact, variety in printers, scanners, and capture conditions improves robustness. Option E is not required because there is no fixed minimum of 1,000 pages per model; the needed volume depends on document complexity and diversity, and quality and representativeness matter more than a rigid page count.

Answer

All documents from the same printer

Answer

At least 1,000 pages per model

Question 8

Your company requires that all Azure AI service endpoints are accessible only from within a specific Azure Virtual Network (VNet) and that public internet access is completely disabled. How should you configure the Azure AI resource networking settings?

Accepted Answer

Disable public network access on the Networking blade and create a Private Endpoint associated with your VNet and subnet.. Disabling public network access and creating a Private Endpoint associated with the VNet and subnet ensures the Azure AI resource is reachable only through private IP addresses inside the virtual network. This fully removes public internet exposure, which matches the requirement.

Answer

Set the routing preference to Microsoft network routing and enable service endpoints on the default subnet.

Answer

Configure the firewall to allow only the corporate public IP address range and keep public network access enabled.

Answer

Deploy an Azure Application Gateway in front of the Azure AI multi-service account endpoint.

Question 9

You are designing an enterprise RAG solution using Azure AI Search and Azure OpenAI. You need to ensure data security and compliance across different user permission tiers. Which TWO actions should you implement? (Choose two.)

Accepted Answer

Ensure all data transfers between Azure AI Search and Azure OpenAI utilize TLS 1.2 or higher.. Option A is correct because Azure AI Search and Azure OpenAI communicate over HTTPS endpoints, and enforcing TLS 1.2 or higher protects data in transit against interception or downgrade attacks, which is a baseline requirement for enterprise security and compliance. Option B is correct because Azure AI Search supports document-level access control via security filters (for example, using the user's group IDs in a filterable field or the built-in ACL/rbacScope fields), ensuring each permission tier only retrieves documents its users are authorized to see, which is essential in a multi-tenant RAG solution. Option C is wrong because storing unencrypted authentication tokens in embedding metadata exposes credentials to anyone querying the index and violates token-handling best practices. Option D is wrong because granting admin privileges to all users removes least-privilege enforcement and does not address retrieval latency in a legitimate way. Option E is wrong because disabling managed identities and embedding plaintext connection strings in source code leaks secrets and discards Azure's keyless, identity-based authentication model.

Answer

Store unencrypted user authentication tokens directly inside the embedding vector metadata.

Answer

Grant every user application admin privileges to bypass retrieval latency checks.

Answer

Disable managed identities and store plaintext connection strings in the application source code.

Question 10

You are analyzing product reviews and need to identify specific product features mentioned by users along with whether users expressed positive or negative opinions about those specific features. Which advanced feature of sentiment analysis should you enable?

Accepted Answer

Opinion mining. Opinion mining, also called aspect-based sentiment analysis, is the advanced sentiment analysis feature that identifies specific aspects (product features) mentioned in text and determines the sentiment expressed toward each one. It returns aspect-sentiment pairs, which is exactly what is needed to know whether users felt positively or negatively about particular product features. This makes opinion mining the correct choice.

Answer

Extractive summarization

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Key phrase extraction

Answer

Named Entity Recognition

Question 11

You are building a custom text classification solution using Azure AI Language in Language Studio. You have a dataset where each document can belong to multiple categories simultaneously (e.g., a news article can be tagged as both 'Technology' and 'Finance'). What classification type should you configure?

Accepted Answer

Multi-label classification. Multi-label classification is designed for scenarios where a single document can be assigned more than one category at the same time. In Azure AI Language, multi-label projects let you apply multiple labels to each document during training and return all applicable labels at inference. This matches the requirement that a news article can be tagged as both 'Technology' and 'Finance'.

Answer

Custom named entity recognition

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Single-label classification

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Extractive summarization

Question 12

You are implementing opinion mining as part of sentiment analysis in Azure AI Language. An incoming review states, 'The room was spacious, but the staff was rude.' What does opinion mining return for this sentence?

Accepted Answer

Aspect targets ('room', 'staff') along with their corresponding evaluations ('spacious', 'rude') and individual sentiments.. Opinion mining in Azure AI Language returns aspect-based sentiment analysis, identifying aspect targets (e.g., 'room', 'staff') and their corresponding evaluations (e.g., 'spacious', 'rude') with individual sentiment polarities. For the given review, it would return 'room' as positive and 'staff' as negative, providing granular insights beyond document-level sentiment.

Answer

A binary classification indicating whether the review is spam or genuine.

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A single neutral sentiment score for the entire document.

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A translated version of the review in English.

Question 13

You are testing a Custom Vision model via the REST API. You receive an HTTP 400 Bad Request error with the message 'InvalidProjectId'. What is the most likely cause?

Accepted Answer

The project ID GUID in the request URL is incorrect or does not exist in this Custom Vision account.. The Custom Vision REST API returns HTTP 400 with 'InvalidProjectId' when the project ID GUID supplied in the request URL path (e.g., /customvision/v3.0/training/projects/{projectId}/...) cannot be resolved to a project in the account tied to the submitted training or prediction key. This is a client-side request error, meaning the GUID is malformed, belongs to a different Custom Vision resource/account, or the project was deleted. Because the error is specifically named 'InvalidProjectId', the failure is at the identifier-validation stage, before any image payload is parsed.

Answer

The image file format is invalid.

Answer

The subscription has reached its monthly quota limit.

Answer

The prediction key header was omitted.

Question 14

Which HTTP status code does the Azure AI Vision Read API return upon successfully initiating an asynchronous OCR analysis job?

Accepted Answer

202 Accepted. The Read API is asynchronous: when you POST an image or URL to the Read endpoint, it returns 202 Accepted with an Operation-Location header. You then poll that URL until the job completes and returns 200 OK with results.

Answer

204 No Content

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200 OK

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201 Created

Question 15

You notice your Azure AI agent is producing offensive content. You need to configure a guardrail that intercepts the model output before it is sent to the user. Which service should you integrate?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the dedicated Azure service that provides guardrails for harmful content, including hate, violence, sexual, and self-harm categories, and can be applied to both prompts and completions. Integrating it on the output path lets you intercept and block offensive model responses before they reach the user. This is the purpose-built service for content moderation in Azure AI workloads.

Answer

Azure AI Search Filters

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Prompt Template Validation

Answer

Azure Monitor

Question 16

When configuring Azure OpenAI Custom Model Evaluation in Azure AI Foundry, which TWO metrics can be computed using LLM-as-a-judge evaluators? (Choose two.)

Accepted Answer

Coherence. Options D and E are correct because Azure AI Foundry's LLM-as-a-judge evaluators are designed to assess the quality of generative AI outputs using a language model as the grader, and Coherence (D) and Relevance (E) are two of the standard quality metrics they compute — Coherence measures how logically well-structured and fluent the response is, while Relevance measures how directly the response addresses the user's query or context. These metrics are part of the built-in evaluation metric set (alongside others such as Fluency, Groundedness, and Similarity) that can be run against a model deployment during custom model evaluation. Options A, B, and C are incorrect because CPU utilization percentage, DNS propagation delay, and disk IOPS are infrastructure and networking performance counters, not language-quality judgments, and they cannot be produced by an LLM-as-a-judge evaluator.

Answer

CPU utilization percentage

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DNS propagation delay

Answer

Disk input/output operations per second (IOPS)

Question 17

You are developing a custom extraction model using Azure AI Document Intelligence Studio. Your training dataset consists of multi-page invoices with varying structures, and some fields appear only on the final page. How should you structure your tagging configuration?

Accepted Answer

Tag the fields on whatever page they appear across the sample documents in Document Intelligence Studio.. In Document Intelligence Studio, you tag fields on the pages where they actually appear across your sample documents — the labeling tool is page-aware and stores the page number alongside each field's bounding region. This lets the trained model learn that, for example, 'Total' appears on page 1 in some invoices and page 3 in others. You do not need to split documents or build orchestration models just because fields appear on different pages.

Answer

Create a separate custom model for the final page and chain them using an orchestration model.

Answer

Split every multi-page invoice into individual single-page documents before uploading.

Answer

Define all late-appearing fields as optional metadata parameters in the training configuration JSON file.

Question 18

Which Azure AI Foundry feature helps identify drift in model performance after deployment?

Accepted Answer

Model Monitoring. Model Monitoring in Azure AI Foundry continuously evaluates deployed models against production data, tracking data drift, prediction drift, and performance metrics like accuracy and latency. It is the built-in feature designed specifically to detect post-deployment degradation. The other options either describe manual or unrelated capabilities that do not provide ongoing drift detection for deployed models.

Answer

Data Drift Analysis via Compute

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Prompt Flow Tracing

Answer

Model Catalog

Question 19

You are designing a customer service chatbot using Azure OpenAI. You need to ensure that the model refuses to answer questions outside the scope of your company's products. Where should you define this boundary instruction?

Accepted Answer

In the system message. The system message is correct because it is the designated channel for persistent, high-priority instructions that define the model's persona, scope, and refusal behavior across all turns of a conversation. Placing the boundary instruction there ensures it applies before and throughout the interaction, and it is the standard mechanism for scoping a chatbot to a domain. This is the architecturally correct location for guardrails that must not be overridden by user input.

Answer

In the temperature setting

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In the user prompt prefix

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In the max_tokens parameter configuration

Question 20

Your organization is running an Azure AI Search service and needs to restrict management operations (such as index creation and deletion) to administrators while allowing standard client applications to perform search queries only. How should you configure access?

Accepted Answer

Use Admin API keys for management operations and Query API keys for client search queries.. Azure AI Search issues two key types: admin keys (primary and secondary) that grant full control including index creation, deletion, and configuration, and query keys that only permit read operations against indexes. Assigning admin keys to administrators and query keys to client applications enforces least privilege at the data-plane level without needing Azure RBAC.

Answer

Enable IP firewall rules to block index creation.

Answer

Configure CORS settings in the Azure portal.

Answer

Assign the Contributor role to client applications.