AI-102 Practice Test 42 — 15 Questions

Question 1

A company builds a knowledge mining solution using Azure AI Search with a custom skillset that includes an OCR skill. They want to ensure that images embedded in PDFs are processed. What should they configure?

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

Accepted Answer

Set the 'imageAction' parameter to 'generateNormalizedImages'. The 'imageAction' parameter in Azure AI Search's OCR skill controls whether images embedded in documents (including PDFs) are extracted and processed. Setting it to 'generateNormalizedImages' ensures that images within PDFs are normalized and passed to the OCR skill for text extraction, which is essential for processing embedded images.

Answer

Set the 'defaultLanguageCode' to 'en'

Answer

Set the 'textExtractionAlgorithm' to 'printed'

Answer

Set the 'lineEnding' parameter to 'space'

Question 2

You are developing a custom text classification model using Azure AI Language. You have labeled 2000 documents across 10 categories. You need to evaluate the model's performance before deploying to production. Which THREE metrics should you examine?

Accepted Answer

Recall. Recall is correct because it measures the proportion of actual positive instances correctly identified by the model, which is critical in custom text classification to ensure that relevant documents are not missed. In Azure AI Language, recall helps assess how well the model captures all instances of each category, especially when class distribution is imbalanced across the 10 categories.

Answer

Word Error Rate

Answer

BLEU Score

Question 3

You deploy a Custom Vision object detection model to classify vehicles. The model works well in good lighting but fails in low-light conditions. What is the most appropriate action?

Accepted Answer

Add images with different lighting conditions to the training set. The core issue is a data distribution mismatch: the model was trained primarily on well-lit images and lacks exposure to low-light examples. Adding images with diverse lighting conditions directly addresses this by enriching the training dataset, enabling the model to learn robust features for low-light scenarios. This aligns with the fundamental principle that Custom Vision models are only as good as the training data they receive.

Answer

Increase the probability threshold

Answer

Increase the number of training iterations

Answer

Use a domain-specific model for vehicles

Question 4

Which TWO Azure AI services can be used to perform optical character recognition (OCR) on images? (Choose two.)

Accepted Answer

Azure AI Document Intelligence Read model. Azure AI Document Intelligence Read model (option A) is correct because its Read model is specifically designed to extract printed and handwritten text from documents and images, returning lines, words, and page layout via the Analyze Document operation. Azure AI Vision OCR (Read API) (option E) is also correct because the Read API in Azure AI Vision performs OCR on images and PDFs, returning extracted text and bounding boxes asynchronously. Azure Video Indexer (option B) focuses on video/audio insights such as transcription and face tracking, not general image OCR. Azure AI Custom Vision (option C) is an image classification and object detection service, not an OCR text extractor. Azure AI Face API (option D) detects and analyzes faces (landmarks, attributes), and does not perform optical character recognition.

Answer

Azure Video Indexer

Answer

Azure AI Custom Vision

Answer

Azure AI Face API

Question 5

You are deploying a chat application using Azure OpenAI. The application should only answer questions based on a specific set of internal documents. Which THREE features should you use?

Accepted Answer

Azure AI Search index with the internal documents. Azure AI Search indexes allow you to ingest internal documents and perform vector or hybrid search over them. When integrated with Azure OpenAI, the search results are used as grounding context for the model, ensuring responses are based solely on your data.

Answer

Content filters to block out-of-domain questions

Answer

Fine-tuning the model on the internal documents

Question 6

You need to deploy a generative AI model that can be used by multiple applications within your organization. The model must support real-time inference with low latency. Which Azure service should you use?

Accepted Answer

Azure OpenAI Service. Azure OpenAI Service provides managed access to powerful generative AI models like GPT-4, which are optimized for real-time inference with low latency through provisioned throughput units (PTUs) and regional deployment options. This service is specifically designed for generative AI workloads, offering REST API endpoints that support streaming responses and sub-second latency for single-turn interactions, making it ideal for multiple applications requiring consistent, low-latency responses.

Answer

Azure AI Search

Answer

Azure Machine Learning real-time endpoint

Answer

Azure Functions

Question 7

Which TWO Azure AI services can be used to extract text from images as part of a knowledge mining pipeline?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (option B) is correct because its Read and Layout models perform OCR on documents and images, extracting printed and handwritten text along with structure for downstream knowledge mining enrichment. Azure AI Computer Vision (option C) is correct because its Read OCR feature (Image Analysis / Read API) extracts printed and handwritten text from images, a core skill in Azure AI Search cognitive skillsets. Azure AI Language (option A) is wrong because it handles text analytics such as entity recognition, sentiment, and key phrase extraction, not image OCR. Azure AI Video Indexer (option D) is wrong because it targets video and audio insights like transcription and face tracking, not still-image text extraction. Azure AI Custom Vision (option E) is wrong because it trains image classification and object detection models, not text recognition.

Answer

Azure AI Language

Answer

Azure AI Video Indexer

Answer

Azure AI Custom Vision

Question 8

You are deploying a Custom Vision object detection model to an Azure Container Instance for real-time inference. The model must respond within 500 ms. The default container runs on CPU. What should you do to meet the latency requirement?

Accepted Answer

Export the model as a Dockerfile with GPU support and deploy to a GPU-enabled ACI.. The default Custom Vision container runs on CPU, which is insufficient for real-time object detection inference within 500 ms. Exporting the model as a Dockerfile with GPU support and deploying to a GPU-enabled Azure Container Instance (ACI) leverages NVIDIA CUDA-accelerated inference, dramatically reducing latency to meet the sub-500 ms requirement.

Answer

Increase the number of CPU cores in the container instance.

Answer

Deploy the model to Azure Functions with a Premium plan.

Answer

Use the Cognitive Services Computer Vision container instead.

Question 9

You deploy a custom vision model using Azure AI Custom Vision. After deployment, you notice the model has high accuracy on training data but low accuracy on new images. What is the most likely cause?

Accepted Answer

The model is overfitted to the training data. High accuracy on training data but low accuracy on new images is the classic symptom of overfitting, where the model has memorized the training examples (including noise and irrelevant patterns) rather than learning generalizable features. In Azure AI Custom Vision, this typically occurs when the training dataset is too small, too homogeneous, or lacks sufficient variation, causing the model to fail on unseen data.

Answer

The training time was too short

Answer

The training dataset has too few images

Answer

The wrong domain was selected during training

Question 10

You are deploying an Azure AI solution that uses Azure OpenAI Service. The solution must be deployed in a way that minimizes latency for users in Asia. However, the company's data residency policy requires data to stay in the United States. What should you do?

Accepted Answer

Deploy the service in a US region and use Azure Front Door with caching to reduce latency.. It satisfies both requirements: data residency (deploying in a US region keeps data within the United States) and latency reduction for Asian users. Azure Front Door with caching stores frequently accessed model responses at edge locations closer to users in Asia, minimizing round-trip time without moving the origin data.

Answer

Use Azure CDN to cache the model responses in Asia.

Answer

Deploy the Azure OpenAI Service in an Asian region and use Azure Front Door to route traffic.

Answer

Deploy the service in multiple regions globally and use Traffic Manager for routing.

Question 11

You are developing a chatbot for a retail company using Azure AI Language's custom question answering. The chatbot must provide answers from a knowledge base of 500 FAQ documents. Users often ask the same question in different wording, and the chatbot fails to return an answer for paraphrased queries. What is the most effective solution?

Accepted Answer

Enable active learning in the project settings and periodically publish the updated knowledge base.. Enabling active learning in Azure AI Language's custom question answering allows the system to learn from user interactions. It suggests alternative phrasings for existing QnA pairs, which helps answer paraphrased queries without manual effort. Option A is wrong because Direct Line Speech channel is for voice interactions, not improving answer coverage. Option C is wrong because simply adding more documents does not address the paraphrasing issue; it may increase redundancy but not handle different wording of the same question. Option D is wrong because manually adding alternate phrases is time-consuming and not scalable; active learning automates this process by identifying and suggesting variations based on user queries.

Answer

Use Azure AI Bot Service's Direct Line Speech channel to improve accuracy.

Answer

Increase the number of FAQ documents in the knowledge base.

Answer

Manually add alternate question phrases to the knowledge base for each QnA pair.

Question 12

You are deploying an Azure AI Language service solution for a multilingual customer support chatbot. The solution must support real-time translation between English, Spanish, and French. Which Azure resource should you provision?

Accepted Answer

Azure AI Translator. Azure AI Translator is the correct resource because it provides real-time text translation across multiple languages, including English, Spanish, and French, via a dedicated REST API. The scenario specifically requires translation between languages, not speech recognition or generative AI, making Azure AI Translator the precise service for this task.

Answer

Azure AI Speech

Answer

Azure OpenAI Service

Answer

Azure AI Language

Question 13

Your organization uses Microsoft Entra ID for identity management. You are building an AI solution that uses Azure AI Vision to analyze images. The solution must use managed identities to authenticate to the Vision resource. Which RBAC role should you assign to the managed identity?

Accepted Answer

Cognitive Services User. The Cognitive Services User role (B) is the correct RBAC role because it grants the minimum required permissions for a managed identity to call Azure AI Vision APIs (e.g., analyze image, OCR) without allowing any write or management operations. This role is specifically designed for accessing Azure Cognitive Services endpoints, and it aligns with the principle of least privilege for authentication via managed identities.

Answer

Owner

Answer

Reader

Answer

Contributor

Question 14

A news organization wants to automatically summarize long articles into short, coherent summaries. The solution must preserve the original meaning and key points. Which Azure AI service should be used?

Accepted Answer

Azure AI Language - Extractive Summarization. Azure AI Language's Extractive Summarization is specifically designed to generate concise summaries by extracting the most important sentences from a document while preserving the original meaning and key points. This service uses natural language processing to rank sentences based on relevance and coherence, making it ideal for summarizing long articles without altering the original content.

Answer

Azure AI Document Intelligence

Answer

Azure AI Language - Key Phrase Extraction

Answer

Azure AI Translator

Question 15

You are deploying a computer vision model using Azure AI Custom Vision with a small dataset of 200 images per class. The model shows high accuracy on training data but low accuracy on test data. Which action should you take to reduce overfitting?

Accepted Answer

Increase the training dataset size with more varied images. Increase the training dataset size with more varied images. Overfitting occurs when the model learns noise from a small dataset. Adding more varied images helps the model generalize. Option A (increase learning rate) is wrong because it may cause divergence or unstable training, not directly reduce overfitting. Option B (reduce image size) is wrong because it can lose important features and may even increase overfitting. Option C (increase training epochs) is wrong because it can actually increase overfitting by allowing the model to memorize more noise.

Answer

Increase the learning rate

Answer

Reduce the image size to lower resolution

Answer

Increase the number of training epochs