AI-102 Practice Test 44 — 15 Questions

Question 1

You are building a solution to automatically tag images uploaded to an Azure Storage blob container using Azure AI Vision. The solution must process images as soon as they are uploaded. Which service should you use to trigger the image analysis?

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

Accepted Answer

Azure Event Grid with an Azure Function trigger. Azure Event Grid is the correct choice because it provides a serverless event-driven architecture that can react to blob storage events (e.g., BlobCreated) in near real-time. By configuring an Event Grid subscription on the storage account, you can trigger an Azure Function that uses Azure AI Vision to analyze the image as soon as it is uploaded, without polling or scheduled checks.

Answer

Azure Functions with a timer trigger

Answer

Azure Batch with a job schedule

Answer

Azure Logic Apps with a recurrence trigger

Question 2

You need to translate a large volume of documents from English to French while preserving the original formatting. Which Azure service should you use?

Accepted Answer

Azure AI Translator (Document Translation). Azure AI Translator's Document Translation feature is specifically designed to translate entire documents while preserving the original structure, layout, and formatting (e.g., tables, headers, bullet points). It supports batch processing of large volumes of documents and maintains the source file's format in the translated output, making it the correct choice for this requirement.

Answer

Azure AI Language

Answer

Custom Translator in Azure AI Translator

Answer

Azure OpenAI Service with GPT-4

Question 3

You need to analyze videos stored in Azure Blob Storage to detect objects and generate timestamps. Which Azure service should you use?

Accepted Answer

Azure Video Indexer. Azure Video Indexer (D) is the correct choice because it is specifically designed to analyze videos, extracting insights such as object detection, scene segmentation, and timestamps. It uses AI models to process video content stored in Azure Blob Storage and generates a timeline of detected objects, making it ideal for this scenario.

Answer

Azure Custom Vision

Answer

Azure Form Recognizer

Answer

Azure Computer Vision

Question 4

A company is deploying a solution using Azure AI Vision to analyze images of products on a retail website. They need to ensure that the image analysis is performed within a specific geographic boundary for data residency compliance. What should they configure?

Accepted Answer

Deploy the Azure AI Vision resource in the desired Azure region. Azure AI Vision resources are regional Azure resources, meaning all data processing and storage occur within the Azure region where the resource is deployed. By deploying the resource in the desired geographic region, you ensure that image analysis and any derived data remain within that boundary, satisfying data residency compliance requirements. This is the fundamental mechanism for controlling data location in Azure AI services.

Answer

Create a private endpoint for the Vision resource

Answer

Enable multi-region replication on the Vision resource

Answer

Use the Free tier of Azure AI Vision

Question 5

Your Azure AI Search index is experiencing high query latency. You have enabled semantic search and custom scoring profiles. You need to reduce latency without degrading search quality. Which action should you take?

Accepted Answer

Increase the number of replicas.. Increasing the number of replicas distributes query load across multiple copies of the index, allowing parallel processing of search requests. This directly reduces query latency without altering the search logic, scoring profiles, or semantic enrichment, thus preserving search quality.

Answer

Remove custom scoring profiles.

Answer

Reduce the number of partitions.

Answer

Disable semantic search.

Question 6

You are responsible for an Azure AI solution that uses Custom Vision to classify manufacturing defects. The model must achieve high recall to avoid missing defects. The current model has high precision but low recall. Which action should you take?

Accepted Answer

Lower the probability threshold for the defect class. Lowering the probability threshold for the defect class means the model will classify an image as defective even when its confidence score is lower. This increases the number of true positives (defects caught), directly improving recall at the cost of potentially more false positives. In Custom Vision, the default probability threshold is 50%, and adjusting it downward is the standard technique to prioritize recall over precision.

Answer

Add more images of non-defective items to the training set

Answer

Increase the number of training iterations

Answer

Increase the probability threshold for the defect class

Question 7

You are deploying a chatbot using Azure OpenAI Service with a custom dataset indexed in Azure AI Search. Users report that the chatbot frequently responds with 'I don't know' for questions that the dataset should cover. What is the most likely cause?

Accepted Answer

The confidence threshold in the retrieval configuration is set too low, filtering out relevant chunks.. When the confidence threshold is set too low, Azure AI Search filters out retrieved chunks that don't meet the minimum confidence score, even if those chunks are relevant. This causes the chatbot to respond with 'I don't know' because no sufficiently confident context is passed to the Azure OpenAI model for answer generation. The issue is specifically in the retrieval configuration, not in the model's generation parameters.

Answer

The search scope is limited to a small number of documents.

Answer

The temperature setting in the model deployment is set too high.

Answer

The chunk size in the index is too large, causing irrelevant chunks to be retrieved.

Question 8

Which TWO Azure AI Search features should you enable to improve the relevance of search results for a knowledge mining solution that supports natural language queries?

Accepted Answer

Semantic ranking. Semantic ranking (B) is correct because it uses Microsoft's language understanding models to re-rank the top results from the initial BM25 retrieval, promoting results that are semantically relevant to natural language queries rather than just keyword matches. Scoring profiles (D) are correct because they let you boost or demote documents based on weighted fields, functions (e.g., magnitude, freshness, distance), and parameters, directly tuning relevance for the knowledge mining scenario. Synonyms (A) only expand query terms with equivalent expressions and do not provide semantic re-ranking or weighted relevance boosting. Search mode 'all' (C) merely requires all query terms to match, which is a stricter boolean behavior that can reduce recall rather than improve relevance. Filters (E) restrict the result set by criteria such as OData expressions but do not affect ranking or relevance scoring.

Answer

Synonyms

Answer

Search mode 'all'

Answer

Filters

Question 9

You are training an Azure Custom Vision object detection model to locate pallets in warehouse photos. Your training set contains 500 images, but only 40 images include pallets, while the rest are empty aisles. The model performs poorly, often missing pallets. You need to improve detection while keeping training time reasonable. What should you do first?

Accepted Answer

Add more labeled images that contain pallets in varied conditions and balance the dataset.. Object detection models learn from the distribution of labeled examples. When pallet images are only 8 percent of the set, the model is biased toward the dominant empty-aisle class and misses pallets. Adding more varied, labeled pallet images and balancing classes improves the positive signal. Threshold tuning, domain changes, or simple retraining do not correct the underlying data imbalance.

Answer

Enable the 'General' domain and retrain with the same dataset without changes.

Answer

Switch the project domain to a compact domain to speed up training.

Answer

Increase the probability threshold so fewer false positives are returned.

Question 10

You are creating an Azure AI Search index that will store documents enriched with key phrases and sentiment scores. You need to define the index fields to store these enriched values. The key phrases should be searchable and retrievable, and the sentiment score should be filterable and sortable. Which field definitions should you use?

Accepted Answer

keyPhrases: Collection(Edm.String) with searchable=true, retrievable=true; sentimentScore: Edm.Double with filterable=true, sortable=true, retrievable=true.. Key phrases are a collection of strings, so Collection(Edm.String) is correct. They must be searchable and retrievable to enable full-text search and to return them in results. Sentiment score is a numeric value, so Edm.Double is suitable. It must be filterable and sortable, and retrievable to be included in results. The combination of these attributes satisfies the scenario's requirements.

Answer

keyPhrases: Collection(Edm.String) with searchable=true, filterable=true; sentimentScore: Edm.Double with filterable=true, sortable=true.

Answer

keyPhrases: Edm.String with searchable=true, retrievable=true; sentimentScore: Edm.Int32 with filterable=true, sortable=true, retrievable=true.

Answer

keyPhrases: Collection(Edm.String) with filterable=true, retrievable=true; sentimentScore: Edm.Double with searchable=true, retrievable=true.

Question 11

You are designing a solution that uses Azure AI Vision to analyze images uploaded by users. You need to extract text and also generate a descriptive caption for each image. You want to use the Image Analysis API. Which two capabilities should you enable? (Choose two.)

Accepted Answer

Read. The Read capability extracts text, and the Caption capability generates a descriptive sentence for the image. Together they satisfy both requirements. Tags, Objects, and Brands provide different types of analysis that are not needed here. Enabling Read and Caption allows a single API call to return both OCR results and a caption, streamlining the solution.

Answer

Tags

Answer

Brands

Answer

Objects

Question 12

A security company uses Azure AI Face API to analyze surveillance footage. They need to detect faces in low-light images and obtain face bounding boxes, but they do not need to identify individuals. They also want to minimize cost and avoid unnecessary features. Which Face API operation should they call?

Accepted Answer

Face - Detect with returnFaceId=false and returnFaceLandmarks=false. To detect faces without identification, the Face - Detect operation should be called with returnFaceId and returnFaceLandmarks set to false. This returns bounding boxes and avoids the cost of generating face IDs. Identify and Verify are for matching or comparing identities, which are not needed here, and enabling face IDs or landmarks adds unnecessary expense.

Answer

Face - Detect with returnFaceId=true and returnFaceLandmarks=true

Answer

Face - Verify with two face IDs

Answer

Face - Identify with a person group

Question 13

You are deploying an Azure AI solution that uses Azure AI Language and Azure AI Vision. You need to ensure that the solution can be deployed repeatedly to multiple environments with consistent configuration and that secrets are managed securely. (Choose two.)

Accepted Answer

Use Azure Key Vault to store the Azure AI service keys and reference the secrets from the ARM template.. To achieve repeatable deployments with consistent configuration, use infrastructure as code such as ARM templates. To manage secrets securely, store keys in Azure Key Vault and reference them from the templates. Combining these two practices allows the same deployment process to be used across environments while keeping secrets out of source control and deployment definitions.

Answer

Store the Azure AI service keys in the ARM template parameters file.

Answer

Use the Azure portal to manually create the Azure AI resources in each environment.

Answer

Store the Azure AI service keys in a Git repository and use a pipeline to inject them during deployment.

Question 14

You are developing a generative AI feature with Azure OpenAI Service. The feature must generate a structured JSON object that conforms to a specific schema so it can be consumed by a downstream application without post-processing. What should you use to constrain the model output to the schema?

Accepted Answer

Structured outputs with a JSON schema supplied in the request.. Structured outputs in Azure OpenAI allow a JSON schema to be provided with the request so the model's response is constrained to conform to that schema. This guarantees valid, parseable JSON with the expected fields, eliminating brittle post-processing and making the output directly consumable by the downstream application.

Answer

A higher frequency_penalty to discourage extra tokens.

Answer

Setting n to a value greater than 1 to generate multiple candidate responses.

Answer

A system message that politely asks the model to return JSON.

Question 15

A media company needs to automatically generate descriptive captions for thousands of archived photographs stored in Azure Blob Storage. The solution must be fully managed and require no model training. Which Azure AI Vision capability should they use?

Accepted Answer

Image captioning in Azure AI Vision. Image captioning in Azure AI Vision is a prebuilt feature that generates natural language descriptions for images without any training. The media company can process each stored photograph and receive a caption, directly meeting the scenario's need for automated, managed captioning. Other options either require training, target documents, or focus only on faces, so they do not provide the required broad image description.

Answer

Custom Vision classification model

Answer

Face API person identification

Answer

Azure AI Document Intelligence prebuilt receipt model