AI-103 Practice Test 2 — 10 Questions

Question 1

Your company wants to track and allocate Azure AI service costs by department. Which TWO mechanisms can assist with this requirement? (Choose two.)

Accepted Answer

Using Azure Cost Management filters and cost allocation rules to generate chargeback reports.. Option A is correct because Azure Cost Management supports filters (by resource group, tag, service, subscription, etc.) and cost allocation rules that let you split shared costs across departments, enabling accurate chargeback/showback reports for Azure AI services. Option E is correct because applying department-specific tags such as Department: Marketing to Azure AI resources lets Cost Management group and filter spend by tag, which is the standard way to attribute AI service costs to each department. Option B is incorrect because a single untagged, unsegmented subscription provides no dimension to separate or allocate costs by department. Option C is incorrect because deactivating Azure Cost Management removes the very reporting and allocation capability needed for cost tracking. Option D is incorrect because the Free (F0) tier only affects pricing/limits of certain AI services and does not provide any cost tracking or allocation mechanism.

Answer

Sharing a single Azure subscription across all departments without using tags or resource groups.

Answer

Deactivating Azure Cost Management to reduce management overhead.

Answer

Provisioning all AI resources in the Free (F0) tier.

Question 2

Which TWO factors directly influence the embedding quality and retrieval relevance in an Azure AI Search vector retrieval pipeline? (Choose two.)

Accepted Answer

The choice of embedding model (e.g., text-embedding-3-large vs text-embedding-3-small) and its dimensionality.. Option B is correct because the embedding model determines the semantic quality of the vectors stored in the Azure AI Search index: text-embedding-3-large produces higher-fidelity, higher-dimensional vectors (3072 dimensions) than text-embedding-3-small (1536 dimensions), directly affecting how well semantic similarity matches user intent during vector search. Option E is correct because chunking strategy and chunk size control the granularity of the indexed content; chunks that are too large dilute the embedding with unrelated content, while chunks that are too small lose context, so both the segmentation method (e.g., fixed-size, sentence-aware, or document-layout-aware) and the chunk length materially affect retrieval relevance. Options A and C are incorrect because background color and font family are visual formatting attributes that are not captured by text embedding models and have no bearing on vector similarity. Option D is incorrect because the end user's browser version affects client-side rendering and request handling, not the embedding quality or the relevance scoring performed by Azure AI Search.

Answer

The background color of the source documents in blob storage.

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The font family used in the original PDF documents.

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The client browser version used by the end user.

Question 3

You are using an Azure AI model that supports versioning. You want to perform an A/B test by routing 10% of traffic to a new version. Where do you configure this?

Accepted Answer

The 'Traffic' configuration in the Azure AI endpoint.. Azure AI endpoints expose a 'Traffic' configuration where you assign percentage weights to deployed model versions, enabling A/B testing by routing a fraction of requests to a new version. Setting 10% to the new version and 90% to the current one achieves the desired split. This is native to the endpoint and requires no external load balancer.

Answer

Azure App Service routing rules.

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Azure Load Balancer backend pools.

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Azure Traffic Manager DNS settings.

Question 4

As part of responsible AI practices in Azure AI Foundry, which TWO actions should you perform to evaluate and mitigate model fairness and safety risks before deployment? (Choose two)

Accepted Answer

Run automated evaluations in Azure AI Foundry to measure metrics such as groundedness, relevance, and protected material presence.. Option B is correct because Azure AI Foundry's automated evaluation flows let you quantify safety and quality metrics—groundedness, relevance, coherence, fluency, and protected-material/hate-and-unfairness scores—so you can objectively assess fairness and safety risks before deployment. Option D is correct because adversarial testing (red teaming) in Azure AI Foundry systematically probes the model with prompt injections, jailbreak attempts, and other malicious inputs to surface unintended behaviors and safety gaps that must be mitigated prior to release. Option A is wrong because internal-only audiences do not exempt an application from responsible AI security and safety reviews; risk assessments apply regardless of user population. Option C is wrong because removing system prompts strips guardrails and grounding instructions, increasing unsafe or off-topic outputs rather than enabling meaningful evaluation. Option E is wrong because hardcoding responses is not a fairness/safety evaluation technique and would defeat the purpose of testing a generative model's behavior.

Answer

Bypass security reviews if the application is intended for internal enterprise employees only.

Answer

Remove all system prompts to allow the model complete autonomy during evaluation runs.

Answer

Hardcode responses for all possible user inputs to eliminate model non-determinism entirely.

Question 5

You are using Custom Vision and want to programmatically publish a trained iteration so that prediction endpoints can use it. Which API operation should you call?

Accepted Answer

POST /customvision/v3.3/Training/{projectId}/iterations/{iterationId}/publish. Publishing a trained Custom Vision iteration so prediction endpoints can consume it is done by calling POST /customvision/v3.3/Training/{projectId}/iterations/{iterationId}/publish. The publish operation makes the iteration available at the prediction URL, optionally under a named prediction resource. This is the required step before any prediction calls can target that iteration.

Answer

GET /customvision/v3.3/Training/{projectId}/iterations/{iterationId}/make-default

Answer

PUT /customvision/v3.3/Prediction/{projectId}/iterations/{iterationId}/activate

Answer

PATCH /customvision/v3.3/Training/{projectId}/projects/{projectId}

Question 6

You are provisioning an Azure AI services account and want to ensure that all telemetry and monitoring metrics are collected. Which built-in monitoring feature is automatically enabled on Azure AI resources without requiring additional agent installation?

Accepted Answer

Azure Monitor platform metrics. Azure Monitor platform metrics are collected automatically for Azure AI services (Cognitive Services, Azure OpenAI, AI Search, etc.) with no agent, SDK, or diagnostic extension required. They appear in the resource's Metrics blade within seconds to minutes and cover request counts, latency, errors, and token usage. This makes them the only truly zero-configuration monitoring feature among the choices.

Answer

Log Analytics Windows security agent

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Application Insights SDK code instrumentation

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Azure Diagnostics extension for Linux

Question 7

You are developing a mobile application that captures business card photos and extracts contact information. Which Azure AI Vision feature is specifically designed to extract printed and handwritten text lines and words?

Accepted Answer

Read. The Read feature in Azure AI Vision is specifically designed for OCR, extracting printed and handwritten text lines and words from images. It is optimized for text-heavy images like business cards, documents, and receipts. This makes it the correct choice for capturing contact information from business card photos.

Answer

Image Tagging

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Dense Captions

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Brand Detection

Question 8

Your enterprise team is deploying multiple Azure AI Custom Vision resources across different development stages (Dev, Test, Prod). You need to ensure that billing costs for the Dev environment are billed to a specific cost center. Which Azure feature should you use to categorize these costs?

Accepted Answer

Azure resource tags. Azure resource tags are key-value pairs applied to resources and resource groups that flow into the Azure Cost Management and Billing data, enabling cost categorization, filtering, and chargeback/showback by dimensions like CostCenter, Environment, or Project. Tagging the Dev Custom Vision resources with a CostCenter tag lets finance attribute those costs to the correct cost center.

Answer

Azure role-based access control (RBAC)

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Azure Service Health alerts

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Azure Firewall rules

Question 9

Which TWO image file formats are natively supported by Azure AI Vision for image analysis? (Choose two.)

Accepted Answer

JPEG. Azure AI Vision's Image Analysis API natively accepts JPEG (option D) and PNG (option E) as image payloads, since these are the standard raster formats the service decodes for feature extraction such as captioning, OCR, and object detection. JPEG is supported because it is the most common compressed photographic format, and PNG is supported because it preserves lossless detail and transparency for graphics and text-heavy images. The remaining options do not belong: MP3 (A) and WAV (C) are audio codecs, and MKV (B) is a video container format, none of which are image file formats accepted by the Image Analysis endpoint.

Answer

MP3

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MKV

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WAV

Question 10

You are optimizing an enterprise RAG application in Azure AI Foundry for high accuracy and minimal latency. Which THREE advanced retrieval techniques should you consider implementing? (Choose THREE)

Accepted Answer

Semantic ranking to re-rank top search results using deep learning models. Option A (Semantic ranking) is correct because Azure AI Search's semantic ranker applies deep-learning language models to re-rank the top BM25/vector results, promoting the most contextually relevant passages and directly improving RAG answer accuracy. Option C (Hybrid search combining full-text BM25 and vector embeddings) is correct because fusing keyword and dense-vector retrieval via Reciprocal Rank Fusion captures both exact term matches and semantic similarity, which raises recall and precision over either method alone. Option E (Query expansion and re-writing) is correct because enriching the user prompt with synonyms and related terms broadens the candidate set and aligns the query with the indexed content, improving retrieval quality for ambiguous or short queries. Option B does not belong because random sentence selection without indexing provides no relevance signal or deterministic retrieval, guaranteeing poor accuracy. Option D does not belong because disabling vector search and relying on random string matching discards semantic retrieval entirely, which destroys both accuracy and the ability to handle natural-language queries.

Answer

Using random sentence selection without indexing

Answer

Disabling vector search entirely to rely solely on random string matching