AI-103 Practice Test 28 — 15 Questions

Question 1

You are implementing a text classification pipeline using Azure AI Language. You want to deploy your trained custom model to a production endpoint. What is required before you can assign traffic to the model deployment in Language Studio?

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

Accepted Answer

You must successfully complete model training and create a deployment in Language Studio.. In Azure AI Language's custom text classification, the workflow requires two distinct steps before traffic can be routed: first, you must train the model (which produces a trained model artifact tied to your project), and second, you must create a deployment that hosts that trained model at an endpoint. Only after a deployment exists can you assign traffic to it in Language Studio. The [CORRECT] option captures both prerequisites — successful training and deployment creation — which are the actual gating requirements.

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You must export the model container and deploy it to an Azure Kubernetes Service (AKS) cluster.

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You must submit a support ticket to Microsoft to enable custom model endpoints.

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You must convert the model files to ONNX format.

Question 2

Your application uploads images to Azure AI Vision via URLs. One of your URLs is failing with an invalid image error, even though the image opens correctly in web browsers. What is the most likely reason?

Accepted Answer

The URL requires HTTP Basic Authentication or redirects that the vision service cannot resolve.. Azure AI Vision's URL-based image input requires the service to fetch the image directly from the provided URL. If the URL is behind HTTP Basic Authentication, requires a token, or returns a redirect (3xx) that the service cannot follow, the fetch fails and the service returns an 'invalid image' error even though a browser (which handles auth prompts and redirects) can open it fine. The fix is to make the image publicly accessible or upload the binary bytes instead.

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Azure AI Vision does not support URL-based inputs; you must upload binary bytes.

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The image file size exceeds 500 MB.

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Image URLs are restricted to PNG format only.

Question 3

You are implementing a multi-region disaster recovery strategy for an Azure OpenAI deployment. If the primary region goes down, how should you configure traffic redirection to the secondary region using Azure Front Door?

Accepted Answer

Use priority routing with the primary region set to priority 1 and the secondary region set to priority 2.. Azure Front Door priority routing assigns a priority number to each origin, and traffic is sent to the lowest-numbered (highest-priority) healthy origin first. Setting the primary region to priority 1 and the secondary to priority 2 ensures all traffic goes to the primary during normal operation and automatically fails over to the secondary only when the primary becomes unhealthy. This is the standard active-passive DR pattern.

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Use geographic routing based on the client IP location.

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Use weighted routing with a 50/50 split between regions.

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Use performance-based routing to the lowest latency node.

Question 4

You are configuring an Azure AI Language resource in the Azure portal. You need to ensure that the text analysis solution supports custom text classification and named entity recognition. Which pricing tier should you select?

Accepted Answer

Standard (S). The Standard (S) pricing tier for Azure AI Language supports custom text classification and custom named entity recognition (NER). The Free (F0) tier does not support these custom features. Therefore, you must select Standard (S).

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Premium (P1)

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Basic (B)

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Free (F0)

Question 5

You are designing a multi-agent solution in Azure AI Foundry where agents collaborate asynchronously. One agent produces an intermediate JSON payload that must be strictly validated before being consumed by the next agent. What feature should you implement?

Accepted Answer

Structured Outputs with strict JSON Schema enforcement. Structured Outputs with strict JSON Schema enforcement forces the model to emit JSON that conforms to a defined schema, which is exactly what is needed to validate an intermediate payload before the next agent consumes it. It reduces parsing failures and ensures required fields, types, and enums are respected.

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Model temperature set to 1.0

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Azure API Management XML transformation policy

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Azure AI Content Safety hate speech detection

Question 6

Your team needs to extract the most important talking points from research papers using the Azure AI Language service. You want the service to return a list of salient terms and phrases from the input document. Which API task should you execute?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the Azure AI Language service feature specifically designed to identify and return the main concepts or talking points from unstructured text. It analyzes the input document and outputs a list of salient terms and phrases that represent the key ideas, which directly matches the requirement to extract important talking points from research papers. The other options serve different purposes: PII Detection identifies personal information, Sentiment Analysis determines emotional tone, and Language Detection identifies the language of the text.

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PII Detection

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Sentiment Analysis

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Language Detection

Question 7

You are building an image search engine. You want to extract visual feature tags from user photos using Azure AI Vision 4.0. Which visual feature should you request?

Accepted Answer

features=tags. The features=tags parameter in Azure AI Vision 4.0 returns a list of content tags describing the objects, scenes, and concepts detected in an image, which is exactly what an image search engine needs for indexing and retrieval. Tags are the visual feature purpose-built for extracting searchable descriptors from photos.

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features=read

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features=caption

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features=smartCrops

Question 8

When engineering prompts for Azure OpenAI models, you want to guide the model's output format and persona effectively. Which practice represents the most reliable prompt engineering technique?

Accepted Answer

Providing clear role definition, explicit formatting instructions, and few-shot examples within the system message.. The system message is the authoritative channel for defining the model's role, persona, output format constraints, and few-shot examples. Azure OpenAI models weight system messages heavily for behavioral guidance, and combining role definition, explicit formatting rules, and few-shot examples yields the most deterministic, reliable outputs. This is the recommended pattern in Microsoft's prompt engineering guidance.

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Embedding all context and instructions exclusively inside user messages without utilizing system messages.

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Using overly complex, ambiguous phrasing to encourage the model to explore diverse latent spaces.

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Relying entirely on zero-shot inference without system messages to maximize model creativity.

Question 9

You are planning the disaster recovery strategy for an Azure AI Translator resource. In the event of a regional outage in East US, you want to ensure minimal downtime and continuity of service. Which deployment model should you select?

Accepted Answer

Deploy a primary resource in East US and a secondary resource in the paired region (West US), then implement client-side failover logic.. Azure AI Translator does not provide automatic cross-region failover or built-in geo-replication for the translation endpoint. The recommended disaster recovery pattern is to deploy the resource in two regions (ideally a paired region such as East US / West US) and implement client-side failover logic that switches the endpoint and key when the primary region is unavailable. This achieves minimal downtime without relying on features the service does not offer.

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Configure an Azure Backup vault to snapshot the AI model weights daily.

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Store all translation requests in Azure Blob Storage and replay them when the region recovers.

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Rely on Azure's native synchronous cross-region database replication for cognitive services.

Question 10

You are designing a generative AI application in Azure AI Foundry and want to categorize user queries into predefined intent classes before routing them to specific agents. Which prompt engineering technique should you apply?

Accepted Answer

Few-shot prompting with labeled classification examples. Few-shot prompting provides the model with labeled examples of each intent class, allowing it to learn the classification pattern in-context without fine-tuning. This is the standard technique for intent categorization when you have predefined classes and want consistent, schema-aligned outputs. The labeled examples anchor the model's output format and decision boundaries.

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Zero-shot random generation

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Enabling JSON output mode without schema definition

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Increasing model temperature to 1.5

Question 11

Which THREE features are critical for managing the lifecycle of an AI model in a production environment?

Accepted Answer

Model registry for version control.. Option A (Model registry for version control) is correct because a model registry tracks model artifacts, versions, lineage, and stage transitions (e.g., staging to production), which is essential for reproducibility, rollback, and governance across the model lifecycle. Option C (CI/CD integration for automated deployments) is correct because CI/CD pipelines automate testing, packaging, and deployment of models and their dependencies, enabling consistent, repeatable releases and reducing manual error during lifecycle transitions. Option E (Real-time monitoring of inference metrics) is correct because production models require continuous observation of latency, throughput, drift, and accuracy degradation so teams can trigger retraining or rollback before business impact occurs. Option B (Hardcoding all environment settings) is not appropriate because it prevents portability across environments and breaks configuration management best practices. Option D (Manual file copying via FTP) is not appropriate because it lacks versioning, auditability, and automation, making deployments error-prone and non-reproducible.

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Hardcoding all environment settings.

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Manual file copying via FTP.

Question 12

You are configuring an Azure AI Search index for use with Azure OpenAI On Your Data. The index contains technical documentation with complex nested tables. Which search configuration will yield the best retrieval performance for semantic queries?

Accepted Answer

Enable semantic search alongside vector and keyword search (hybrid search).. Hybrid search combines keyword (BM25/Lucene) scoring with vector similarity, and semantic search adds a Microsoft-hosted reranker (based on deep learning models) on top of the merged results. For complex nested tables in technical documentation, keyword matching alone misses paraphrased queries, while pure vector search can lose exact identifiers; the semantic reranker then reorders the top ~50 results for relevance. This layered approach yields the best retrieval quality for RAG grounding in Azure OpenAI On Your Data.

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Configure a pure OData filter index without vector embeddings.

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Use simple query syntax with exact phrase matching only.

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Disable all vector fields and rely exclusively on traditional Lucene scoring.

Question 13

When evaluating an AI agent, what does the 'Coherence' metric measure?

Accepted Answer

The logical flow and clarity of the generated text.. The Coherence metric in AI agent evaluation measures the logical flow and clarity of the generated text. It assesses whether the response is well-structured, easy to follow, and logically consistent. This is one of the key quality metrics for generative AI outputs.

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The cost of the API call.

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The number of sources cited.

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The speed of the response.

Question 14

You are designing a RAG solution using Azure AI Search and Azure OpenAI. Which TWO factors significantly influence the effectiveness of chunking documents during the ingestion phase? (Choose TWO)

Accepted Answer

Chunk size measured in tokens or characters. Option A (chunk size measured in tokens or characters) is correct because the size of each chunk directly determines how much context is embedded and retrieved; chunks that are too large dilute semantic relevance and may exceed the embedding model's token limit, while chunks that are too small lack sufficient context, so tuning size in tokens or characters is a primary lever for RAG quality. Option C (chunk overlap percentage between consecutive segments) is correct because overlap preserves continuity across chunk boundaries, preventing sentences or ideas split between segments from losing meaning, and the overlap ratio materially affects retrieval recall and answer coherence. Option B is incorrect because the Azure OpenAI model temperature only controls randomness during text generation at inference time and has no effect on how documents are chunked during ingestion. Option D is incorrect because Azure API Management timeout duration governs request/response time limits for API calls and does not influence chunking strategy. Option E is incorrect because virtual network subnet IP address range is a networking concern for connectivity and has no bearing on document segmentation.

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Azure OpenAI model temperature setting

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Azure API Management timeout duration

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Virtual network subnet IP address range

Question 15

You need to ensure data privacy and compliance when processing user feedback containing sensitive information. Which TWO types of sensitive data can Azure AI Language PII detection identify and redact? (Choose two.)

Accepted Answer

Phone numbers. Azure AI Language's PII detection feature is designed to identify and redact personal data entities, and phone numbers (option C) are a supported PII entity category, so they can be detected and masked during text processing. Email addresses (option D) are likewise a core supported PII entity in Azure AI Language, allowing them to be recognized and redacted to meet privacy and compliance requirements. Weather forecast coordinates (option A) are geographic data points, not personal identifiers recognized as PII entities by the service. Source code syntax errors (option B) relate to code analysis, not PII detection, and CPU utilization metrics (option E) are operational telemetry rather than sensitive personal information, so neither is identified or redacted by PII detection.

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Weather forecast coordinates

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Source code syntax errors

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CPU utilization metrics