AI-102 Practice Test 47 — 15 Questions

Question 1

You are developing a chat application that uses Azure OpenAI GPT-4 to answer customer questions. You need to ensure the model does not generate harmful content. Which configuration should you set?

Accepted Answer

Enable the content filter in Azure OpenAI Service.. Azure OpenAI Service includes a built-in content filter that actively scans both input prompts and generated completions to detect and block harmful content such as hate speech, violence, or self-harm. This filter operates at the service level, providing a robust safety layer that cannot be bypassed by model configuration alone. While system prompts can guide behavior, they are not a reliable safeguard against adversarial inputs or model misuse.

Answer

Use a system prompt that instructs the model to be safe.

Answer

Set the temperature parameter to 0.

Answer

Set max_tokens to a low value.

Question 2

You are deploying an agentic solution using Azure AI Agent Service. The agent needs to be invoked from a custom application using REST API calls. Which endpoint should you use to send a message to the agent?

Accepted Answer

POST /threads/{thread_id}/messages. To send a message to an existing conversation thread in Azure AI Agent Service, you must use the POST /threads/{thread_id}/messages endpoint. This adds the user's message to the specified thread, which the agent can then process in a subsequent run. The REST API requires the thread to already exist, and messages are posted directly to that thread's resource.

Answer

POST /threads/{thread_id}/runs

Answer

POST /threads

Answer

GET /agents

Question 3

You are deploying a custom named entity recognition (NER) model using Azure AI Language. The model must extract product codes that follow a specific pattern (e.g., 'PRD-12345'). You have 5,000 labeled examples. After training, the model extractor works well on development data but fails to extract product codes from new data. What is the most likely issue?

Accepted Answer

The model is overfitting to the training data.. The model performs well on development data but fails on new data, which is the classic symptom of overfitting. In Azure AI Language custom NER, overfitting occurs when the model memorizes the training examples—including noise or specific patterns—rather than generalizing to the underlying product code pattern. With 5,000 labeled examples, the dataset size is likely sufficient, but the model may have learned spurious correlations that do not hold in unseen data.

Answer

The training data size is insufficient.

Answer

The product code pattern is too complex for the model to learn.

Answer

The labeling is inconsistent across the dataset.

Question 4

You are building a knowledge mining solution to extract insights from a large set of PDF contracts. The solution must identify parties, dates, and monetary amounts. Which Azure AI service should you use as the primary extraction engine?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct choice because it is specifically designed for document analysis and extraction of structured data such as parties, dates, and monetary amounts from PDFs and images. Its prebuilt models (e.g., the 'prebuilt-invoice' or 'prebuilt-contract' model) use optical character recognition (OCR) and deep learning to extract key-value pairs and entities from contracts without requiring custom training.

Answer

Azure AI Language (custom NER)

Answer

Azure AI Search with integrated vectorization

Answer

Azure OpenAI Service with GPT-4o

Question 5

A company uses Azure AI Language Service to analyze customer feedback. They notice that the sentiment scores for negative reviews are often incorrectly labeled as neutral. Which configuration should be adjusted to improve accuracy?

Accepted Answer

Create a custom sentiment analysis model using Custom Text Classification. The Azure AI Language Service's pre-built sentiment analysis model may not capture domain-specific nuances in customer feedback, leading to misclassification of negative reviews as neutral. By creating a custom sentiment analysis model using Custom Text Classification (option C), you can train the model on your labeled data to improve accuracy for your specific use case.

Answer

Deploy the Language service in a different Azure region

Answer

Increase the confidence threshold for sentiment classification

Answer

Enable Key Phrase Extraction to preprocess the text

Question 6

You are a cloud solution architect at a legal firm. The firm needs to automate the summarization of legal documents. They have a large corpus of past case summaries and legal documents stored in Azure Blob Storage. They want to use Azure OpenAI to generate summaries for new documents. The solution must ensure that the generated summaries are accurate and do not contain hallucinated legal facts. The firm also requires that the solution be serverless and minimize operational overhead. You need to design the solution.

Option A: Use Azure OpenAI with a system message that instructs the model to be accurate. Deploy the model as a web app on Azure App Service and call it from Azure Functions triggered by new blob uploads.

Option B: Use Azure OpenAI with Retrieval-Augmented Generation (RAG) by indexing the past case summaries in Azure AI Search. Use Azure Functions to process new documents, retrieve relevant cases, and pass them as context to the model. Store summaries in Azure Cosmos DB.

Option C: Fine-tune an Azure OpenAI model on the past case summaries and deploy it as a managed endpoint. Use Azure Logic Apps to trigger summarization when new blobs are added.

Option D: Use Azure OpenAI with the chat API and provide the entire document in the prompt. Use Azure Container Instances to run a service that calls the API and writes summaries back to Blob Storage.

Which option should you choose?

Accepted Answer

Option B. It uses Retrieval-Augmented Generation (RAG) with Azure AI Search to ground the model's output in verified past case summaries, directly addressing the requirement to avoid hallucinated legal facts. The serverless architecture is achieved via Azure Functions triggered by blob uploads, minimizing operational overhead, while storing summaries in Azure Cosmos DB provides a scalable, low-latency output store.

Answer

Option A

Answer

Option D

Answer

Option C

Question 7

Match each Azure AI service to its primary function.

Accepted Answer

Computer Vision: Extracts information from images and videos. The correct matches pair each service with its primary function. Computer Vision analyzes images/videos; Language Service processes text; Speech Service handles audio; Cognitive Search provides AI search. Common confusions include swapping Speech with Computer Vision or Language with Cognitive Search.

Answer

Computer Vision: Converts speech to text and text to speech

Answer

Language Service: AI-powered search over indexed content

Question 8

You are deploying a Conversational Language Understanding (CLU) model to production. You need to monitor the model's performance and detect when retraining is needed due to concept drift. Which metric should you monitor?

Accepted Answer

Average confidence scores of predictions. Average confidence scores of predictions is the correct metric because a sustained drop in confidence indicates that the model is encountering utterances that differ from its training distribution, which is a classic sign of concept drift. Monitoring confidence scores allows you to detect when the model's predictions become less certain, triggering the need for retraining with new data.

Answer

Response time for each prediction

Answer

Number of endpoint calls

Answer

Number of utterances processed per day

Question 9

A company is building a chatbot using Azure OpenAI Service to answer customer queries. The chatbot must not generate harmful or offensive content. Which Azure AI service should be integrated to filter inappropriate content?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the correct service because it provides built-in content moderation APIs that detect and filter harmful or offensive text and images, including hate speech, violence, self-harm, and sexual content. Integrating this service with the Azure OpenAI chatbot ensures that user inputs and model outputs are screened in real time, preventing the generation of inappropriate responses.

Answer

Azure Bot Service

Answer

Azure Cognitive Search

Answer

Azure Form Recognizer

Question 10

You are an Azure AI engineer at Fabrikam Inc. The company has developed a custom vision model using Azure Custom Vision to detect defects on a manufacturing assembly line. The model is deployed as a Docker container to an on-premises edge device using Azure IoT Edge. Recently, the model's inference accuracy has decreased. The operations team reports that the edge device is running low on memory and CPU. The model was trained with images from a specific camera angle, but the camera angle has been changed slightly due to maintenance. You need to improve the model's accuracy. What should you do?

Accepted Answer

Retrain the model with new images captured from the current camera angle.. The decrease in accuracy is most likely due to the change in camera angle, which introduces a domain shift between the training images and the new inference images. Retraining the model with images captured from the current camera angle will realign the training data distribution with the production environment, directly addressing the root cause of the accuracy drop. This is a standard practice in Custom Vision when deployment conditions change.

Answer

Upgrade the edge device to have more memory and CPU.

Answer

Reduce the image resolution to lower memory usage.

Answer

Convert the model to use grayscale images.

Question 11

A healthcare startup is developing a chatbot that uses Azure OpenAI to answer patient questions. They need to ensure that the chatbot only uses information from their verified medical database and does not generate unsupported medical advice. What is the best approach?

Accepted Answer

Use Azure AI Search with vector search to retrieve relevant documents and pass them as context.. It uses Azure AI Search with vector search to retrieve only relevant, verified documents from the medical database and passes them as context to the Azure OpenAI model. This grounds the model's responses in authoritative data, preventing it from generating unsupported medical advice. The retrieval-augmented generation (RAG) pattern ensures the chatbot answers are based on the provided context rather than the model's internal knowledge.

Answer

Fine-tune a model on the medical database and deploy it.

Answer

Embed the entire medical database in the system message.

Answer

Rely on Azure OpenAI's content filtering to block unsupported advice.

Question 12

You see the exhibit in an Azure AI Search skillset definition. The custom skill is not executing as expected. What is the most likely cause?

Accepted Answer

The skill is missing the 'uri' property to specify the endpoint. The custom skill is not executing because the 'uri' property, which specifies the endpoint URL for the web API, is missing from the skill definition. Azure AI Search requires this property to route requests to the external service; without it, the skill cannot be invoked and will fail silently or return errors.

Answer

The input source path is incorrect

Answer

The JSON is malformed and will not parse

Answer

The type should be 'Microsoft.Skills.Custom.WebApiSkill' with dots

Question 13

A company is deploying a custom vision model using Azure Custom Vision. The training data contains images with varying resolutions. The model must achieve high accuracy. Which pre-processing step should be applied to the images before training?

Accepted Answer

Resize all images to the same dimensions (e.g., 224x224).. Custom Vision models use a fixed input size (e.g., 224x224 for ResNet-based architectures). Images with varying resolutions must be resized to the same dimensions before training to ensure consistent tensor shapes for the neural network. Without this step, the model cannot process the data correctly, leading to training failures or degraded accuracy.

Answer

Convert images to grayscale.

Answer

Normalize pixel values to a range of 0-1.

Answer

Apply data augmentation techniques like random cropping.

Question 14

Which THREE factors should you consider when selecting the pricing tier for an Azure AI Language resource to handle a high-volume production workload?

Accepted Answer

Transactions per second (TPS) limits. The pricing tier for Azure AI Language directly determines the Transactions Per Second (TPS) limit, which is critical for high-volume production workloads. For example, the Free tier (F0) allows only 20 TPS, while Standard (S) tiers can scale to thousands of TPS depending on the selected SKU. Exceeding the TPS limit results in throttling (HTTP 429 errors), so selecting a tier with sufficient TPS capacity is essential to maintain throughput under load.

Answer

Language support

Answer

SLA percentage

Question 15

Your organization uses Azure AI Document Intelligence to extract data from invoices. The solution must identify custom fields not present in the prebuilt models, such as 'purchase order number' located in varying positions across documents. What should you do?

Accepted Answer

Train a custom extraction model using labeled sample invoices.. Azure AI Document Intelligence (formerly Form Recognizer) allows you to train a custom extraction model using labeled sample invoices. This approach enables the model to learn custom fields like 'purchase order number' that appear in varying positions, which prebuilt models cannot handle. By providing labeled examples, the model generalizes to extract the field accurately from new documents.

Answer

Use the layout model and apply manual post-processing.

Answer

Use Azure AI Forms Recognizer with prebuilt receipt model.

Answer

Use the prebuilt invoice model with field merging.