AI-103 Practice Test 9 — 10 Questions

Question 1

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)

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

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 2

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 3

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

Answer

Cross-Origin Resource Sharing (CORS) headers

Answer

Model rate limit quotas

Question 4

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 5

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 6

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 7

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 8

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 9

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 10

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