AI-102 Practice Test 41 — 15 Questions

Question 1

A company uses Azure OpenAI to generate product descriptions. They notice that the model occasionally produces descriptions that include false claims about product features. The company needs to reduce the frequency of these inaccuracies without changing the training data. Which parameter adjustment would be most effective?

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

Accepted Answer

Decrease the temperature parameter. Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to generate creative but factually incorrect statements. This directly addresses the need to reduce false claims without modifying training data, as lower temperature forces the model to rely on its most probable (and typically more accurate) token predictions.

Answer

Increase the top_p parameter

Answer

Increase the max_tokens parameter

Answer

Increase the frequency_penalty parameter

Question 2

Refer to the exhibit. You have created a Text Analytics resource and retrieved its keys. You want to use the key1 to call the Sentiment Analysis API from a Python application. Which endpoint URL should you use?

Accepted Answer

https://mytextanalytics.cognitiveservices.azure.com/text/analytics/v3.1/sentiment. The Sentiment Analysis API for Azure Cognitive Services Text Analytics uses the endpoint pattern `https://<resource-name>.cognitiveservices.azure.com/text/analytics/v3.1/sentiment`. This is the standard REST API endpoint for sentiment analysis in version 3.1, which requires the `/text/analytics/v3.1/sentiment` path appended to the custom resource domain.

Answer

https://mytextanalytics.cognitiveservices.azure.com/sentiment/v3.1

Answer

https://mytextanalytics.api.cognitive.microsoft.com/text/analytics/v3.1/sentiment

Answer

https://mytextanalytics.cognitiveservices.azure.com/analyze

Question 3

You are designing an Azure AI solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must handle up to 1000 invoices per hour with a maximum latency of 5 seconds per invoice. You need to choose the appropriate pricing tier and resource configuration. What should you do?

Accepted Answer

Use the S0 tier and configure autoscaling if needed. The S0 tier (Standard) for Azure AI Document Intelligence supports up to 15 transactions per second (TPS), which equates to 54,000 invoices per hour—far exceeding the 1,000 per hour requirement. With a maximum latency of 5 seconds per invoice, the S0 tier's default throughput is sufficient without needing autoscaling, though autoscaling can be configured for burst scenarios. The Free tier (F0) is limited to 20 transactions per minute (1,200 per hour) and is not designed for production workloads, making S0 the correct choice.

Answer

Create a Cognitive Services multi-service account to increase throughput

Answer

Use the Free tier and implement a retry policy

Answer

Deploy the Document Intelligence resource in multiple regions

Question 4

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 5

A retail company uses Azure Computer Vision to analyze customer traffic in stores. They process images from security cameras using the OCR API to detect product labels. Recently, the OCR accuracy has decreased for images with poor lighting. Which pre-processing step should the company implement to improve OCR accuracy?

Accepted Answer

Adjust brightness and contrast of images using image processing.. Poor lighting directly reduces the contrast between text and background, which is critical for OCR accuracy. Adjusting brightness and contrast improves the signal-to-noise ratio of the text regions, making character edges more distinct for the Azure Computer Vision OCR engine. This pre-processing step compensates for the lighting deficiency without altering the fundamental image content that the API relies on.

Answer

Convert images to grayscale before sending to OCR API.

Answer

Increase the image resolution before calling OCR API.

Answer

Reduce image size to decrease noise.

Question 6

A university is developing an app for students to take photos of handwritten notes and convert them to digital text. The app must support multiple languages including English and Spanish. The solution should use a pre-built AI service. Which Azure service should you use?

Accepted Answer

Azure AI Vision Read API (OCR). Azure AI Vision OCR API supports multiple languages and handwritten text. Custom Vision does not support OCR. Azure AI Language requires text input. Azure AI Document Intelligence is for structured documents.

Answer

Azure AI Document Intelligence with a custom model

Answer

Azure AI Language with custom entity recognition

Answer

Custom Vision with a custom handwriting recognition model

Question 7

A retail company uses the Computer Vision Image Analysis API to generate tags for product images in their e-commerce catalog. They want to automatically tag images with product categories such as 'electronics', 'clothing', and 'home goods'. The prebuilt tags often misclassify items. For example, a smartphone is tagged as 'communication device' instead of 'electronics'. You need to improve the tagging accuracy for the company's specific product categories without building a completely new model. What should you do?

Accepted Answer

Train a Custom Vision classification model with images labeled with the company's product categories.. Custom Vision allows training a model with custom categories, improving tagging accuracy for specific product categories. Custom Vision works with the Image Analysis API by using the same underlying computer vision capabilities. Option B is incorrect because Dense Captioning provides descriptions but does not allow custom categories. Option C is incorrect because increasing the confidence threshold only filters out low-confidence tags but does not add custom categories. Option D is incorrect because the brands feature identifies brand names, not product categories.

Answer

Use the Dense Captioning feature to generate detailed descriptions and parse them for categories.

Answer

Increase the confidence threshold for tags to reduce false positives.

Answer

Use the 'brands' feature to identify product brands and map them to categories.

Question 8

A financial services company is building a computer vision solution to automatically extract data from scanned checks. The solution must recognize handwritten amounts, printed account numbers, and signature presence. The company has a large dataset of labeled check images. They need high accuracy and the ability to retrain with new data. Which Azure service should they use?

Accepted Answer

Azure AI Document Intelligence (Form Recognizer) with a custom model trained on check images. Azure AI Document Intelligence (Form Recognizer) is optimized for document extraction, supports custom models, and handles handwriting and printed text. Custom Vision is for object detection. Azure AI Vision OCR is for general text extraction. Azure AI Language is for text analytics.

Answer

Azure AI Vision OCR with a custom dataset using Custom Vision

Answer

Azure AI Language with custom entity recognition

Answer

Azure AI Vision Image Analysis with a custom model

Question 9

You are a developer for a healthcare startup. They are building a mobile app that allows users to take photos of prescription labels and extract medication names, dosages, and frequencies. The app must run on iOS and Android devices. The solution should use a pre-built AI service with minimal custom code. What should you recommend?

Accepted Answer

Use Azure AI Vision OCR API to extract text from the label images. Azure AI Vision OCR provides pre-built text extraction from images. Custom Vision requires custom training. Form Recognizer is for forms. Azure AI Language is for text analytics, not image text extraction.

Answer

Use Azure AI Document Intelligence (Form Recognizer) to analyze labels

Answer

Train a Custom Vision object detection model to locate and read text

Answer

Use Azure AI Language to extract entities from the label text

Question 10

A company uses the Computer Vision Image Analysis API to generate captions for images. The captions are often too generic. How can they improve the descriptiveness of captions?

Accepted Answer

Use the Dense Captioning feature.. The Dense Captioning feature in the Computer Vision Image Analysis API generates one-sentence descriptions for each of up to 10 regions detected in an image, providing more specific and detailed captions than the single generic caption. This directly addresses the problem of captions being too generic by breaking the image into meaningful areas and describing each one individually.

Answer

Use the Object Detection API instead.

Answer

Train a custom model with Custom Vision.

Answer

Increase the confidence threshold for captions.

Question 11

A financial services company uses Azure AI Language to analyze customer support transcripts. They want to identify the main topics discussed in each conversation and generate a summary of the key points. The solution must minimize development effort and use prebuilt functionality. You need to recommend the appropriate Azure AI Language features. What should you use?

Accepted Answer

Key phrase extraction and conversation summarization.. Conversation summarization is a prebuilt feature that generates summaries of conversations, and key phrase extraction identifies main topics. Both are available in Azure AI Language without custom training. Option B correctly combines these two features to identify main topics and generate a summary. Option A is wrong because custom NER requires custom labeling and is not necessary for identifying main topics. Option C is wrong because entity linking is not used for summarization or topic identification. Option D is wrong because sentiment analysis does not provide summarization or topic extraction.

Answer

Custom Named Entity Recognition (NER) and conversation summarization.

Answer

Entity linking and conversation summarization.

Answer

Sentiment analysis and key phrase extraction.

Question 12

An agent uses Azure OpenAI with function calling to perform actions. The agent is not executing functions correctly. Which THREE factors should the team check to diagnose the issue?

Accepted Answer

The token limit is too low, truncating the function definitions.. If the token limit is too low, the model may not receive the full function definitions, causing it to omit or misinterpret available functions. This truncation prevents the model from correctly selecting or formatting function calls, leading to execution failures.

Answer

The temperature parameter is set too high.

Answer

The model version is outdated.

Question 13

You are deploying a question answering solution using Azure AI Language. The solution must be able to provide answers from a set of frequently asked questions (FAQs) in PDF format. What should you do?

Accepted Answer

Create a custom question answering project and add the PDF as a source.. Azure AI Language's custom question answering feature allows you to directly upload a PDF as a knowledge source. The service automatically extracts Q&A pairs from the document, enabling the solution to answer questions based on the FAQ content without needing additional search or cognitive skill pipelines.

Answer

Use Azure AI Search with cognitive skills.

Answer

Use Azure OpenAI with a system prompt containing the PDFs.

Answer

Use the pre-built question answering in Azure AI Language.

Question 14

You are designing a solution to analyze customer call transcripts using Azure AI Language. The solution must extract key phrases, detect sentiment per utterance, and identify the customer's intent (e.g., 'cancel subscription', 'technical support'). The data is stored in Azure Blob Storage and processed in near real-time. Which combination of Azure AI Language features and processing pattern should you use?

Accepted Answer

Use the conversation summarization API (with utterance-level sentiment and key phrase extraction) and an orchestration workflow model that routes to a custom conversational language understanding project for intent detection.. It combines the conversation summarization API (which provides utterance-level sentiment and key phrase extraction) with an orchestration workflow model that routes to a custom conversational language understanding (CLU) project for intent detection. This pattern supports near real-time processing of call transcripts from Azure Blob Storage, meeting the requirements for per-utterance sentiment, key phrase extraction, and intent identification.

Answer

Use custom text classification to classify each utterance into intent categories and use the sentiment analysis API on the entire transcript.

Answer

Use the prebuilt key phrase extraction API to identify important terms and the prebuilt sentiment analysis API for overall transcript sentiment, then map intents via a rules-based approach.

Answer

Use the prebuilt conversational language understanding model for intent detection and Azure AI Language sentiment analysis API for utterance-level sentiment, processing each utterance independently via Azure Functions.

Question 15

You are an Azure AI engineer at Contoso Ltd. The company has an Azure AI solution that uses Azure AI Language to analyze customer feedback. The solution is deployed in the East US region and uses the S0 pricing tier. Recently, the volume of feedback has increased significantly, causing the service to throttle requests. The application logs show HTTP 429 (Too Many Requests) errors during peak hours. The development team has already implemented retry logic with exponential backoff, but the errors persist. You need to recommend a solution to handle the increased load without changing the application code. The solution must minimize cost. What should you do?

Accepted Answer

Upgrade the Azure AI Language resource to a higher pricing tier (e.g., S1) or create additional resources and load balance.. Upgrading to a higher pricing tier (e.g., S1) increases the transactions-per-second (TPS) limit, directly addressing the HTTP 429 throttling errors without requiring code changes. Creating additional resources and load-balancing also distributes the request volume, but upgrading the existing resource is the simplest and most cost-effective approach when retry logic already fails.

Answer

Use Azure Front Door to cache responses and reduce load on the service.

Answer

Switch the Azure AI Language resource to the Free tier to reduce costs.

Answer

Move the service to a different Azure region with higher capacity.