AI-102 Practice Test 20 — 15 Questions

Question 1

You notice a spike in errors (HTTP 429) on a specific day. What is the most likely cause?

Accepted Answer

The number of calls exceeded the rate limit for the service tier.. HTTP 429 (Too Many Requests) is a rate-limiting response that occurs when the number of API calls exceeds the allowed threshold for the service tier. In Azure AI services, each pricing tier has a specific requests-per-second (RPS) or requests-per-minute (RPM) limit, and exceeding this limit triggers a 429 error to protect backend resources.

Answer

Network connectivity issues.

Answer

Authentication tokens expired.

Answer

Invalid API keys were used.

Question 2

You are implementing a RAG (Retrieval-Augmented Generation) solution using Azure AI Search and Azure OpenAI Service. The solution is returning answers that are not relevant to the user query. What is the most likely cause?

Accepted Answer

The relevance score threshold is set too low.. A low relevance score threshold in Azure AI Search allows documents with low semantic or vector similarity to be returned as results. When these poorly matched documents are passed to Azure OpenAI Service for answer generation, the model may produce answers that are not relevant to the user query, as the retrieved context is noisy or unrelated.

Answer

The max_tokens parameter is set too high.

Answer

The chunk size is too small.

Answer

The index includes too many documents.

Question 3

You are developing an Azure AI Language solution to analyze customer support tickets. Each ticket has a subject and a description. You need to automatically classify tickets into categories (e.g., 'billing', 'technical', 'account') and extract the product name mentioned. You have a labeled dataset of 10,000 tickets with category labels and product name annotations. The solution must be cost-effective and easy to retrain as new categories emerge. You want to use a single Azure AI Language resource. Which approach should you use?

Accepted Answer

Use custom text classification for category and custom named entity recognition for product name extraction.. The most cost-effective and retrainable approach is to use custom text classification for categorizing tickets and custom named entity recognition (NER) for extracting product names. Both are part of Azure AI Language and can be trained on your labeled dataset. Custom NER allows you to define your own entity types (e.g., product name) and is more accurate than prebuilt NER for domain-specific products. Using a single Azure AI Language resource supports both features.

Answer

Use custom text classification for category and key phrase extraction for product name.

Answer

Use conversational language understanding (CLU) to handle both classification and entity extraction in a single model.

Answer

Use custom text classification for category and prebuilt named entity recognition for product name extraction.

Question 4

A company is developing a conversational AI solution using Microsoft Copilot Studio. They want the copilot to answer questions based on a knowledge base of technical documents. Which data source integration should they use?

Accepted Answer

Azure AI Search. Azure AI Search is the correct data source because it provides a search index that can be queried by Copilot Studio using the 'Azure AI Search' connector. This allows the copilot to perform semantic or keyword-based retrieval over indexed technical documents, enabling accurate question-answering from a knowledge base. Copilot Studio natively supports Azure AI Search as a data source for generative answers, making it the optimal choice for this scenario.

Answer

Azure Blob Storage

Answer

Azure SQL Database

Answer

Microsoft Lists

Question 5

You are deploying a conversational AI solution using Microsoft Copilot Studio. The solution must comply with organizational data loss prevention (DLP) policies by preventing sensitive data from being sent to the underlying Azure OpenAI model. What should you configure?

Accepted Answer

Define DLP policies in Microsoft 365 compliance center and apply to Copilot Studio. Microsoft Copilot Studio integrates with Microsoft 365 DLP policies to prevent sensitive data from being sent to the underlying Azure OpenAI model. By defining DLP policies in the Microsoft 365 compliance center and applying them to Copilot Studio, you can enforce data loss prevention rules that block or restrict the transmission of sensitive information (e.g., credit card numbers, social security numbers) to the generative AI backend. This ensures compliance with organizational security requirements without modifying the AI model itself.

Answer

Configure content filters in Azure OpenAI Studio

Answer

Enable Azure AI Content Safety in the bot's generative AI configuration

Answer

Set the temperature parameter to 0 to reduce variability

Question 6

A company uses Azure Custom Vision to classify images of defective parts. After deploying the model, the accuracy is low. The team only has 10 images per class. What is the most effective way to improve accuracy?

Accepted Answer

Add at least 50 more images per class with variations.. Azure Custom Vision relies on deep learning models that require a sufficient number of diverse training images to generalize well. With only 10 images per class, the model is severely underfit and prone to overfitting; adding at least 50 more images per class with variations in lighting, angle, and background provides the necessary data diversity to improve accuracy significantly.

Answer

Use a different classification algorithm.

Answer

Reduce the image resolution to speed up training.

Answer

Increase the number of training iterations (epochs).

Question 7

Your team has built a knowledge mining pipeline using Azure AI Search and Document Intelligence. After ingestion, you notice that some documents are not appearing in search results. What is the most likely cause?

Accepted Answer

The indexer encountered errors and marked the documents as failed. When an indexer runs, it processes each document and can encounter errors such as unsupported file formats, corrupt content, or permission issues. If a document fails during indexing, the indexer records the error and does not add that document to the index, making it invisible to search queries. This is the most direct cause of missing documents after ingestion. Other options affect search behavior but not whether documents are indexed in the first place.

Answer

The index does not have a semantic configuration

Answer

The search service has insufficient replicas

Answer

The search service is throttled due to high query volume

Question 8

Your organization uses Azure AI Document Intelligence to extract data from invoices. The extraction accuracy for total amounts is low. You have a labeled dataset of 500 invoices. You need to improve the model's accuracy for the 'total amount' field. What should you do?

Accepted Answer

Create a custom neural model and train it with the labeled dataset.. Azure AI Document Intelligence's custom neural model is specifically designed to improve extraction accuracy for fields like 'total amount' by training on labeled datasets. Unlike the prebuilt invoice model, a custom neural model learns the unique layout and variations in your invoices, directly addressing low accuracy for a specific field. Training with 500 labeled invoices provides sufficient data to fine-tune the model's extraction capabilities.

Answer

Add additional predefined models for invoice processing.

Answer

Enable OCR enhancement to improve text recognition.

Answer

Increase the confidence threshold for the total amount field.

Question 9

You are building a generative AI application that must process large volumes of PDF documents and generate summaries using Azure OpenAI. The solution must be cost-effective and handle variable workloads. Which architecture should you recommend?

Accepted Answer

Use Azure Functions with a consumption plan to trigger processing jobs and call Azure OpenAI.. Azure Functions with a consumption plan provides a serverless, event-driven architecture that scales automatically to handle variable workloads, ensuring cost-effectiveness by charging only for compute time used. This architecture is ideal for processing large volumes of PDFs, as each document can trigger a function execution that calls Azure OpenAI for summarization, without the need for always-on infrastructure.

Answer

Use Azure Kubernetes Service (AKS) with a persistent node pool of GPU nodes.

Answer

Deploy a GPU-enabled virtual machine and run the summarization jobs sequentially.

Answer

Use Azure Logic Apps to iterate through documents and call Azure OpenAI.

Question 10

You are deploying a conversational AI chatbot using Azure AI Language service. The chatbot must be able to switch between multiple intents in a single conversation without restarting the session. Which feature should you enable?

Accepted Answer

Orchestration workflow. Orchestration workflow in Azure AI Language service allows a chatbot to switch between multiple intents within a single conversation by routing requests to different language models or custom question answering knowledge bases. This enables the chatbot to handle diverse intents seamlessly without restarting the session, as each intent can be processed by the most appropriate component.

Answer

Active learning

Answer

Dynamic entity extraction

Answer

Prebuilt domain components

Question 11

Which TWO actions should you take to reduce the latency of an Azure AI Computer Vision OCR call on a large image?

Accepted Answer

Resize the image to a smaller resolution before calling the API.. Options B and D are correct. B: Resizing reduces processing time. D: Using the Read API asynchronously allows the client to poll, avoiding timeout. A: Increasing timeout doesn't reduce latency. C: Using CPU doesn't help. E: Cognitive Services container on-premises might add network latency.

Answer

Use a CPU-bound compute instance.

Answer

Increase the API timeout value.

Answer

Deploy the Cognitive Services container on-premises.

Question 12

You run the Azure CLI command 'az search indexer list --search-service mysearch --query "[].{name:name, status:status, lastResult:lastResult}"' and get the above output. Your indexer shows 5 warnings. What should you do to investigate the warnings?

Accepted Answer

Run 'az search indexer show --name myindexer' and review the 'warnings' array in the output.. The 'az search indexer list' command with the query you used returns a summary of indexer status and last result, but it does not include the detailed 'warnings' array. To investigate the 5 warnings, you need to use 'az search indexer show --name myindexer', which returns the full indexer execution history, including a 'warnings' array that lists each warning with its message and details. This allows you to understand the nature of each warning and take corrective action if needed.

Answer

Run 'az search indexer run --name myindexer' to trigger a new run.

Answer

Ignore the warnings because they are not errors.

Answer

Run 'az search indexer reset --name myindexer' to reset the indexer.

Question 13

Your Azure AI Search index stores customer support tickets. You need to implement a search feature that returns semantically similar results even if the query uses different wording. Which configuration should you enable?

Accepted Answer

Enable semantic search and configure a semantic configuration. Semantic search in Azure AI Search uses deep neural networks to understand the intent and context of a query, returning results that are semantically similar even when the wording differs. By enabling semantic search and configuring a semantic configuration, you define which fields are used for summarization and ranking, which directly addresses the requirement for meaning-based matching rather than keyword matching.

Answer

Use simple query parsing with searchMode=any

Answer

Add a synonym map with custom entries

Answer

Enable fuzzy search on the index

Question 14

A company is using Azure OpenAI to generate customer support responses. They want to ensure the model does not use any personally identifiable information (PII) in its outputs. What should they implement?

Accepted Answer

Use Azure AI Content Safety to filter PII from the output.. Azure AI Content Safety provides built-in PII detection and redaction capabilities that can automatically scan and filter sensitive information from model outputs. This is the most reliable approach because it operates as a post-processing filter, catching PII that the model might generate despite instructions. Fine-tuning, prompt engineering, and system messages are all fallible because they rely on the model's compliance rather than enforced filtering.

Answer

Fine-tune the model on anonymized data.

Answer

Use prompt engineering to instruct the model to redact PII.

Answer

Use a system message instructing the model to avoid PII.

Question 15

You run the PowerShell script above to call the Text Analytics API. The response shows a sentiment label of 'positive' with a score of 0.99. However, you expected 'negative' because the word 'excellent' was meant to be sarcastic. What is the most likely reason for this result?

Accepted Answer

The model does not detect sarcasm and interprets the text literally.. The Text Analytics API performs sentiment analysis using a machine learning model that evaluates the literal wording of the text. It does not have built-in capability to detect sarcasm, irony, or implied meaning. Therefore, the word 'excellent' is interpreted as positive regardless of the intended sarcastic tone, resulting in a high positive score.

Answer

The score threshold for negative sentiment is too high.

Answer

The language parameter is set incorrectly.

Answer

The API version does not support sentiment analysis.