AI-103 Practice Test 4 — 25 Questions

Question 1

You are evaluating a Custom Vision classification model. You want to programmatically test the model against a validation dataset without using the portal UI. Which endpoint should your script call?

Accepted Answer

POST /customvision/v3.3/Prediction/{projectId}/classify/iterations/{publishedName}/image. To programmatically test a published Custom Vision classification model, you call the prediction endpoint POST /customvision/v3.3/Prediction/{projectId}/classify/iterations/{publishedName}/image. This endpoint accepts an image and returns the predicted tags with confidence scores for the specified published iteration. It is the correct REST path for classification predictions outside the portal.

Answer

POST /computervision/imageanalysis:analyze

Answer

POST /customvision/v3.3/Training/{projectId}/images

Answer

GET /vision/v3.2/models

Question 2

You are managing a multi-tenant SaaS application that uses Azure AI Vision. Each tenant has their own isolated data, and you want to track costs per tenant. How should you provision Azure AI services to achieve granular cost attribution?

Accepted Answer

Deploy a dedicated Azure AI Services resource per tenant and use Azure Cost Management for billing attribution.. Deploying a dedicated Azure AI Services resource per tenant is the only approach that provides true isolation and granular cost attribution, because Azure Cost Management tracks costs at the resource level. Each tenant's resource appears as a separate line item in cost analysis, enabling per-tenant billing. This also satisfies the data-isolation requirement mentioned in the scenario.

Answer

Use a single global Azure AI Services resource and parse tenant IDs from request user-agent headers for billing.

Answer

Store tenant IDs in Custom Vision project tags.

Answer

Use Azure Blob Storage cost allocation tags to cover AI API costs.

Question 3

You are implementing a grounded RAG solution in Azure AI Foundry. Users report that the model occasionally hallucinates answers even when relevant documents are retrieved by Azure AI Search. What is the most effective prompt engineering strategy to mitigate this grounding failure?

Accepted Answer

Add strict prompt instructions stating that the model must rely solely on the retrieved context chunks and explicitly output a refusal statement if the answer is not present.. The most effective prompt engineering mitigation for grounding failures is to explicitly instruct the model to answer only from the retrieved context and to refuse when the answer is absent. This constrains the model's generation to the provided evidence and reduces hallucination by giving it a clear escape hatch rather than forcing an answer.

Answer

Remove the system message entirely so the model can freely blend internal training weights with search results.

Answer

Increase the temperature parameter to 1.5 to encourage creative synthesis of the retrieved data.

Answer

Disable chunking in Azure AI Search so the entire document is passed as a single massive token block.

Question 4

You are conducting a security review of an Azure AI Search service and Azure OpenAI integration. You discover potential security misconfigurations. Which THREE remediation actions should you take to align with Azure security benchmarks? Each correct answer presents part of the solution.

Accepted Answer

Enable diagnostic logging and configure alerts for unauthorized access attempts and HTTP 401/403 errors.. Option B is correct because enabling diagnostic logging and configuring alerts for unauthorized access attempts and HTTP 401/403 errors provides the audit trail and detection capability required by Azure security benchmarks to identify and respond to authentication and authorization failures against Azure AI Search and Azure OpenAI. Option C is correct because disabling public network access once private endpoints are fully implemented enforces network isolation, ensuring traffic reaches the services only through the private endpoint (private link) rather than the public internet, which is a core Azure security benchmark recommendation. Option E is correct because rotating static API keys regularly limits the blast radius of leaked keys, and transitioning clients to Microsoft Entra ID managed identities eliminates long-lived shared secrets entirely by using token-based, identity-driven authentication. Option A does not belong because granting Contributor to anonymous external users violates least privilege and exposes the services to unauthorized modification. Option D does not belong because embedding admin API keys in client-side single-page applications exposes full administrative credentials to anyone inspecting the browser, which is a critical secret-management anti-pattern.

Answer

Grant Contributor permissions to anonymous external users.

Answer

Expose admin API keys in client-side single-page applications for direct access.

Question 5

When establishing a governance strategy for Azure AI resources across multiple cloud teams, which THREE administrative tasks should an administrator perform? (Choose three.)

Accepted Answer

Apply Role-Based Access Control (RBAC) following the principle of least privilege.. Option A is correct because applying Role-Based Access Control (RBAC) with least privilege ensures each cloud team member receives only the permissions needed for their role, such as Reader, Contributor, or specific custom roles, reducing the blast radius of misconfiguration or compromise across Azure AI resources. Option B is correct because Azure Policy can enforce organizational standards such as naming conventions and allowed regions by evaluating resource properties during creation and updates, and it can deny noncompliant deployments or flag them for remediation, which is essential for consistent multi-team governance. Option C is correct because organizing subscriptions under Azure Management Groups creates a hierarchy where policies and RBAC assignments inherit down to child subscriptions, giving administrators a single governance control plane across multiple cloud teams without configuring each subscription individually. Option D is incorrect because disabling Azure Activity Logs would remove the administrative audit trail needed for compliance, security monitoring, and incident investigation, directly undermining governance. Option E is incorrect because assigning the Owner role to all developers violates least privilege and grants excessive control over access and billing, increasing risk rather than establishing sound governance.

Answer

Disable Azure Activity Logs to prevent administrative audit trails.

Answer

Assign the Owner role to all developers to streamline resource creation.

Question 6

You are building an advanced agentic solution in Azure AI Foundry that integrates with external line-of-business systems. Which THREE architectural patterns or features should you incorporate to ensure robustness and safety? (Choose THREE)

Accepted Answer

Human-in-the-loop review for high-impact tool actions. Option A (Human-in-the-loop review for high-impact tool actions) is correct because agentic solutions that call external line-of-business systems can perform destructive or financially significant operations, so gating high-impact tool invocations behind human approval provides a safety control that prevents irreversible mistakes. Option D (Exponential backoff and jitter retry logic for handling API rate limits (HTTP 429)) is correct because external LOB APIs commonly throttle callers and return HTTP 429, and retrying with exponentially increasing delays plus randomized jitter avoids thundering-herd retries and improves robustness against transient throttling. Option E (Structured Outputs with strict JSON Schema validation for tool payloads) is correct because enforcing a strict JSON Schema on tool inputs and outputs guarantees that the agent emits well-formed, type-safe arguments that the downstream LOB system can parse, reducing malformed calls and injection-style errors. Option B is wrong because executing arbitrary bash commands on production servers without validation is a severe security and reliability risk, not a robustness pattern. Option C is wrong because disabling logging and monitoring removes observability, auditability, and the ability to detect failures or misuse, which directly undermines safety and robustness.

Answer

Allowing the agent to execute any bash command directly on production servers without validation

Answer

Disabling all logging and monitoring to maximize execution speed

Question 7

You are building a chat application with Azure OpenAI. To improve perceived latency for users, you want to stream the model's response token-by-token to the client interface. Which API parameter or client SDK feature should you enable?

Accepted Answer

Enable streaming in the chat completion request (stream=True). Setting stream=True in the chat completion request enables server-sent events (SSE) that deliver tokens incrementally as the model generates them, dramatically improving perceived latency. The client SDKs expose this as a streaming iterator or callback, letting the UI render partial responses immediately.

Answer

Set max_tokens to 1

Answer

Configure Azure AI Search with hybrid search

Answer

Enable semantic caching

Question 8

You are deploying an Azure OpenAI model in Azure AI Foundry and need to restrict access so that only specific virtual networks within your enterprise can invoke the model endpoint. Which feature should you configure?

Accepted Answer

Azure Private Link with private endpoints. Azure Private Link with private endpoints creates a private IP address for the Azure OpenAI endpoint inside your virtual network, so traffic never traverses the public internet. By disabling public network access and allowing only specific VNets via private endpoints, you restrict model invocation to those enterprise networks. This is the correct feature for network-level access restriction to an Azure AI Foundry model endpoint.

Answer

API key rotation policies

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Cross-Origin Resource Sharing (CORS) headers

Answer

Model rate limit quotas

Question 9

You are auditing your Azure subscription and find several unused Azure AI Content Safety resources that are incurring base charges. You want to remove the resources permanently. Which operation should you perform?

Accepted Answer

Delete the Azure AI Content Safety resource from the Azure portal.. Deleting the Azure AI Content Safety resource removes it permanently from the subscription, which stops all billing including any base or provisioned charges. Since the requirement is to remove unused resources permanently, deletion is the only operation that eliminates the resource and its associated costs.

Answer

Remove the role assignments from the resource.

Answer

Stop the Azure AI Content Safety resource state.

Answer

Disable the keys associated with the resource.

Question 10

You are configuring text summarization using the Azure AI Language REST API. You need to generate a summary that consists of a few distinct sentences pulled directly from the source document without altering their wording. Which summarization kind should you specify?

Accepted Answer

Extractive summarization. Extractive summarization produces a summary composed of sentences taken verbatim from the source document, preserving original wording. This exactly matches the requirement to pull distinct sentences without altering them. Abstractive summarization would rephrase and generate new text, which violates the constraint.

Answer

Key phrase extraction

Answer

Abstractive summarization

Answer

Custom single-label classification

Question 11

You are designing a text analysis solution using Azure AI Language. Which TWO features are available as prebuilt capabilities without requiring custom model training? (Choose two.)

Accepted Answer

Language detection. Azure AI Language provides it as a prebuilt capability that identifies the language of input text using the ISO 639-1 code, requiring no training data or custom model. Option C (Sentiment analysis) is also correct because it is a prebuilt feature that returns sentiment labels (positive, negative, neutral, mixed) and confidence scores out of the box. In contrast, option A (Custom named entity recognition) and option D (Custom text classification) are custom features that require labeled training data and a trained project before use. Option E (Custom summarization models) is not a prebuilt capability; summarization is offered as prebuilt abstractive/extractive summarization, and any custom summarization model would require training, so it does not fit the 'no custom training' requirement.

Answer

Custom named entity recognition

Answer

Custom text classification

Answer

Custom summarization models

Question 12

When designing agentic workflows in Azure AI Foundry, what are THREE key characteristics or components of agentic design compared to traditional single-turn prompt completion? (Choose THREE)

Accepted Answer

Tool use and external API invocation capabilities. Option A (Tool use and external API invocation capabilities) is correct because agentic workflows in Azure AI Foundry extend beyond text generation by letting agents call tools, functions, and external APIs (for example via function calling or Azure AI Foundry tool connections) to retrieve data or perform actions. Option C (Iterative reasoning and self-reflection loops) is correct because agents can evaluate intermediate results, critique their own output, and re-plan across multiple reasoning steps rather than producing one final completion. Option D (Autonomous planning and task decomposition) is correct because agentic design centers on breaking a high-level goal into subtasks and deciding the order of execution dynamically, often orchestrated by the model itself. Option B is incorrect because hardcoding every conditional path is the opposite of agentic design, which relies on model-driven decisions and orchestration rather than fully procedural branching. Option E is incorrect because single stateless prompt completions without memory describe traditional single-turn completion, whereas agentic workflows typically maintain state, context, and memory across turns.

Answer

Hardcoding every conditional execution path in procedural code

Answer

Executing single stateless prompt completions without memory

Question 13

You are building an app to analyze customer feedback. You want to extract named entities such as people, locations, and organizations. Which Azure AI Language feature should you use?

Accepted Answer

Named Entity Recognition (NER). Named Entity Recognition (NER) is the Azure AI Language feature specifically designed to identify and categorize entities like people, locations, organizations, and more from unstructured text. It returns both the entity text and its type (e.g., Person, Location, Organization), which directly matches the requirement to extract named entities from customer feedback. Other features like sentiment analysis or key phrase extraction do not provide entity type classification.

Answer

Language detection

Answer

Sentiment analysis

Answer

Key phrase extraction

Question 14

Which TWO criteria are typically used to evaluate the grounding of an LLM-based application?

Accepted Answer

Relevance. Option A, Relevance, is correct because grounding evaluation checks whether the model's response actually addresses the user's query using the retrieved context, so relevance measures how well the answer aligns with the question and the supplied source material. Option D, Groundedness, is correct because it directly measures whether the generated statements are supported by, or entailed by, the provided reference documents rather than being hallucinated or drawn from the model's parametric memory. Together, relevance and groundedness are the two standard quality dimensions for assessing retrieval-augmented or otherwise grounded LLM applications. The unmarked options do not belong: Latency (B) and Token cost (C) are operational efficiency metrics, not grounding quality criteria, and Model version number (E) is a configuration identifier that says nothing about whether an answer is faithful to its sources.

Answer

Latency

Answer

Token cost

Answer

Model version number

Question 15

You are designing an information extraction solution using Azure AI Document Intelligence. Which TWO features are supported by Custom Neural models? Each correct selection represents a complete solution element.

Accepted Answer

Extraction of key-value pairs and fields defined during training. Option A is correct because Custom Neural models are trained on labeled samples to extract the specific key-value pairs and fields you define during training, returning structured JSON with confidence scores for those fields. Option C is correct because Custom Neural models support table extraction, including documents with variable layouts, by learning the structure from your labeled training data. Option B is incorrect because zero-shot extraction of unseen document types without training data is not a Custom Neural capability; that is handled by prebuilt models or the newer query-field/zero-shot features, not by training a Custom Neural model. Option D is incorrect because voice-to-text transcription is a speech service capability, not a Document Intelligence extraction feature. Option E is incorrect because automatic translation into 100+ languages is provided by Azure AI Translator, not by Custom Neural models.

Answer

Zero-shot extraction of completely unseen document types without any training data

Answer

Real-time voice-to-text transcription of document contents

Answer

Automatic language translation of extracted text into 100+ languages

Question 16

You are developing an application that uses the Azure AI Language REST API for text analysis. Which TWO requirements must be met when constructing HTTP requests to the service? (Choose two.)

Accepted Answer

Set the 'Content-Type' header to 'application/json'.. Option C is correct because the Azure AI Language REST API expects request bodies in JSON format, so the 'Content-Type' header must be set to 'application/json' for the service to correctly parse the payload. Option E is correct because the service authenticates requests using the 'Ocp-Apim-Subscription-Key' header, which must contain a valid API subscription key for the request to be authorized. Option A is incorrect because the API uses JSON, not XML, for request payloads. Option B is incorrect because authentication does not use a master account password in the query string; the subscription key is passed in the header. Option D is incorrect because the service does not rely exclusively on HTTP Basic Authentication with username and password; it uses the subscription key header.

Answer

Format all request payloads as XML documents.

Answer

Include the master account password in the request query string.

Answer

Authenticate exclusively using HTTP Basic Authentication with username and password.

Question 17

Which component in Azure AI Foundry allows you to visualize and debug the execution path of a model call, including input/output mapping?

Accepted Answer

Prompt Flow. Prompt Flow in Azure AI Foundry is the orchestration and authoring tool that lets you build flows as DAGs of nodes (LLM, Python, prompt, tool) and visually inspect each node's inputs/outputs, execution time, and token usage. It is purpose-built for debugging and tracing the execution path of a model call, including input/output mapping between nodes.

Answer

Compute Instance

Answer

Model Benchmarks

Answer

Deployment Endpoint

Question 18

You are designing an enterprise text analysis solution with high availability and security requirements. Which THREE practices should you implement? (Choose three.)

Accepted Answer

Implement exponential backoff retry logic in client applications to handle rate limiting (429 errors).. Option B is correct because Azure AI services enforce rate limits and return HTTP 429 (Too Many Requests) responses; implementing exponential backoff retry logic lets client applications gracefully retry after the indicated delay, which is essential for high availability under load. Option C is correct because using managed identities (via Microsoft Entra ID) eliminates hardcoded subscription keys, providing secure, credential-free authentication that supports key rotation and least-privilege access. Option E is correct because private endpoints (Azure Private Link) restrict traffic to your virtual network, removing public internet exposure and meeting enterprise network security requirements. Option A is not appropriate because storing subscription keys in plain text in a source code repository exposes secrets and violates security best practices. Option D is not appropriate because disabling TLS would remove encryption in transit, degrading security and violating compliance requirements rather than improving a legitimate design goal.

Answer

Store subscription keys in plain text configuration files inside the source code repository.

Answer

Disable TLS encryption on HTTP client requests to improve API throughput.

Question 19

You are creating a Custom Vision project and need to select a domain. Your project involves classifying images of various food dishes. Which domain should you select to optimize model accuracy for food classification?

Accepted Answer

Food. The Food domain in Custom Vision is a specialized model pretrained on food imagery, so it produces higher accuracy for classifying dishes than a generic domain. Selecting the domain that matches the image category is the recommended way to optimize model performance in Custom Vision.

Answer

Landmark

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General

Answer

Retail

Question 20

A user asks a question that triggers a 'Blocked' response from Azure AI Content Safety, even though the query seems harmless. What is the best step to investigate this?

Accepted Answer

Check the Content Safety logs in the Azure AI Foundry portal.. When Azure AI Content Safety returns a 'Blocked' result for a seemingly benign prompt, the correct diagnostic step is to inspect the Content Safety logs and severity scores in the Azure AI Foundry portal. These logs show which category (hate, violence, sexual, self-harm) and which severity level triggered the block, letting you determine whether a threshold is misconfigured or a specific phrase is being flagged. This is a read-only investigation that preserves the safety posture.

Answer

Change the system prompt to ignore safety.

Answer

Increase the model temperature.

Answer

Disable Content Safety for the deployment.