AI-103 Practice Test 7 — 10 Questions

Question 1

Your team is implementing 'Human-in-the-loop' (HITL) for an agent that performs account updates. Which mechanism should you use to pause the agent and wait for approval?

Accepted Answer

Use a Prompt Flow 'Pause' node or custom state management logic.. Human-in-the-loop requires the agent to suspend execution, persist its state, and resume only after an external approval signal. In Azure Prompt Flow this is implemented with a Pause node (or equivalent custom state management) that halts the flow and waits for an external event or API call to continue. This preserves the agent's context and prevents the account update from executing until a human explicitly approves it.

Answer

Set a long timeout in the API call.

Answer

Increase the model's 'stop_sequences'.

Answer

Use an infinite loop in Python.

Question 2

Your development team is building a global customer support portal. Incoming tickets arrive in dozens of different languages. You need to automatically route tickets to the correct regional team based on the language of the ticket text. Which Azure AI Language feature should you call?

Accepted Answer

Language detection. Language detection is the Azure AI Language feature specifically designed to identify the language of a given text and return the detected language name and ISO 639-1 code. For routing tickets based on language, this is the exact capability needed. Sentiment analysis, NER, and key phrase extraction serve different purposes and do not identify language.

Answer

Sentiment analysis

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Named Entity Recognition (NER)

Answer

Key phrase extraction

Question 3

You are designing an architecture that processes incoming multilingual support emails using Azure AI Language. Which THREE processing steps should you include in your pipeline? (Choose three.)

Accepted Answer

Execute named entity recognition to extract relevant organizations, locations, and names.. Option C is correct because Azure AI Language's language detection endpoint must be called first to identify each email's primary language, which is essential for routing multilingual content to the appropriate language-specific models. Option A is correct because named entity recognition (NER) extracts entities such as organizations, locations, and person names from the email text, providing structured, actionable information for support workflows. Option E is correct because sentiment analysis and PII detection are supported Azure AI Language features that help gauge customer tone and identify/redact sensitive data in the email documents. Option B is not appropriate because speech-to-text acoustic models are unrelated to processing text emails and training a model per email is impractical. Option D is not appropriate because converting text to raw binary executables serves no analytical purpose and would destroy the text needed for language analysis.

Answer

Train a new speech-to-text acoustic model for every email received.

Answer

Convert all email text into raw binary executable files before analysis.

Question 4

You are automating document ingestion for a RAG pipeline. Which feature allows you to automatically split large documents into smaller chunks during indexing?

Accepted Answer

Indexers with Skillsets. In Azure AI Search, indexers with skillsets are the feature that automates document ingestion and chunking: the indexer pulls source documents, and a Text Split skill in the skillset breaks large documents into smaller chunks before they are embedded and written to the index. This is the standard mechanism for building a RAG pipeline where chunk size affects retrieval quality. The skillset can also enrich content (OCR, entity extraction, embeddings) in the same pipeline.

Answer

Prompt Flow Vector Store Lookup

Answer

Model Fine-tuning

Answer

Vector Embeddings API

Question 5

Which Azure AI Foundry feature allows you to visualize the execution path of an agentic workflow?

Accepted Answer

Prompt Flow visual designer.. Prompt Flow's visual designer (DAG view) in Azure AI Foundry renders each node (LLM call, Python tool, retrieval step) and the directed edges between them, letting you trace the exact execution path of an agentic workflow. It also surfaces per-node inputs/outputs and token usage, which is why it is the designated tool for visualizing agent orchestration. The other options are single-purpose surfaces that do not expose flow topology.

Answer

Azure OpenAI Playground.

Answer

Azure AI Search dashboard.

Answer

Model catalog view.

Question 6

You need to programmatically delete an iteration of a Custom Vision model that performed poorly. Which API method should you invoke?

Accepted Answer

DELETE /customvision/v3.3/Training/{projectId}/iterations/{iterationId}. The Custom Vision Training API exposes iteration deletion via the HTTP DELETE method on the path /customvision/v3.3/Training/{projectId}/iterations/{iterationId}. Invoking DELETE on that resource removes the specified iteration from the project. This is the standard RESTful pattern for deleting a subordinate resource identified by its parent project and iteration IDs.

Answer

PUT /customvision/v3.3/Training/{projectId}/iterations/{iterationId}

Answer

GET /customvision/v3.3/Training/{projectId}/iterations/{iterationId}/purge

Answer

POST /customvision/v3.3/Training/{projectId}/iterations/{iterationId}/delete

Question 7

You need to implement token-based authentication using Azure AD (Microsoft Entra ID) for a Python application consuming an Azure OpenAI resource. Which authorization header format must your application use to authenticate successfully?

Accepted Answer

Authorization: Bearer . Azure OpenAI and most Microsoft Entra ID-protected REST APIs expect OAuth 2.0 bearer tokens in the `Authorization: Bearer ` header. When using Microsoft Entra ID (Azure AD) token-based authentication, the application acquires an access token via MSAL or the Azure Identity SDK and passes it in this standard header format. This is the RFC 6750-compliant way to present a bearer token.

Answer

Authorization: API-Key <api_key_value>

Answer

X-MS-Token: <access_token>

Answer

Ocp-Apim-Subscription-Key: <access_token>

Question 8

You are configuring content safety filters in Azure AI Foundry for a public-facing generative AI application. You need to ensure that the application detects and blocks potential jailbreak attempts and protected material text. Which component should you configure?

Accepted Answer

Azure AI Content Safety text moderation service with jailbreak and protected material detection enabled.. Azure AI Content Safety provides dedicated detectors for jailbreak attempts (prompt injection/manipulation) and protected material text (copyrighted content), which can be configured in Azure AI Foundry to block or flag such inputs and outputs. This is the correct component for enforcing content safety policy on a public-facing generative AI app.

Answer

Azure Key Vault access policies restricting model endpoint tokens.

Answer

Azure API Management rate-limiting policies configured to drop requests containing long prompts.

Answer

Azure Monitor activity log alerts for anomalous token usage.

Question 9

You are deploying a Custom Vision object detection model as a Docker container on an Azure Kubernetes Service (AKS) cluster. During load testing, you notice high memory consumption and container restarts. Which configuration setting in the deployment manifest should you review to ensure stability?

Accepted Answer

Kubernetes resource requests and limits for memory and CPU.. High memory consumption and container restarts in Kubernetes are classic symptoms of missing or misconfigured resource requests and limits. Setting appropriate memory and CPU requests/limits in the deployment manifest ensures the scheduler places the pod correctly and prevents OOMKills.

Answer

Billing endpoint URL syntax.

Answer

Azure Blob Storage connection string persistence.

Answer

Custom Vision iteration publishing name environment variable.

Question 10

You are implementing a multi-turn conversational agent in Azure AI Foundry. As the conversation grows longer, API latency and costs increase significantly. Which strategy should you implement to manage the conversation history effectively?

Accepted Answer

Implement a sliding window history buffer combined with periodic summarization of older dialogue turns.. For long multi-turn conversations, the standard pattern is a sliding window that keeps only the most recent N turns plus a running summary of older dialogue. This bounds the token count sent to the model, controlling latency and cost, while preserving essential context through summarization. It is the recommended approach in Azure AI Foundry for managing conversation history.

Answer

Increase max_tokens to accommodate infinite history growth.

Answer

Disable the chat history array entirely and treat every turn as a standalone prompt.

Answer

Truncate the entire conversation history after every single user turn.