AI-102 Practice Test 13 — 15 Questions

Question 1

You are building a solution to extract customer feedback from PDF documents stored in Azure Blob Storage. The solution must extract key phrases and sentiment scores, but you cannot use any pre-built models from Azure AI Language. What should you use?

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

Accepted Answer

Train custom models in Azure AI Language for key phrase extraction and sentiment analysis. Azure AI Language supports training custom models for named entity recognition (NER) to extract key phrases and custom text classification to predict sentiment labels. You can first extract text from PDFs using Azure AI Document Intelligence or other OCR services, then pass the text to the custom Language models. This approach meets the requirement without using any pre-built Azure AI Language models. Options A and C rely on pre-built APIs and are disallowed. Option D only provides OCR and cannot perform semantic analysis.

Answer

Use the sentiment analysis capability in Azure AI Language

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Use Azure AI Language's pre-built key phrase extraction API

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Use Azure AI Document Intelligence with the pre-built read model

Question 2

You are planning an Azure AI solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must handle large volumes of documents with varying layouts. Which pricing tier should you choose to ensure scalability and high throughput?

Accepted Answer

Standard (S0). The Standard (S0) tier is correct because Azure AI Document Intelligence requires the S0 tier for production workloads that demand high throughput and scalability across varying document layouts. The S0 tier supports unlimited transactions per second (TPS) with auto-scaling, whereas lower tiers impose strict rate limits or lack the capacity to handle large volumes of invoices with diverse formats.

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Premium (P0)

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Free (F0)

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Basic (B0)

Question 3

You are designing a knowledge mining solution for a manufacturing company that needs to extract information from equipment maintenance manuals. The manuals are in multiple languages (English, French, German). You need to ensure that the extracted content is searchable in English only. Which approach should you use?

Accepted Answer

Use the Text Translation skill to translate all content to English during indexing.. You can use the Text Translation skill to translate content to English during indexing, and then index only the translated text. Option A would not translate. Option B only detects language. Option D uses two skills unnecessarily.

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Use the Entity Recognition skill to extract entities and then index entities only.

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Use the Language Detection skill to identify language and then index all content as-is.

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Use the Key Phrase Extraction skill to extract key phrases and then index them.

Question 4

You are responsible for managing costs for Azure AI services used by multiple teams. You notice that costs are higher than expected. Which two actions should you take to reduce costs without impacting performance?

Accepted Answer

Set up budgets and alerts in Cost Management.. Setting up budgets and alerts in Cost Management (Option A) allows you to monitor spending and receive notifications, enabling proactive cost control without impacting performance. Evaluating and choosing the appropriate pricing tier based on usage patterns (Option C) ensures you are not over-provisioned, reducing costs while maintaining performance. Both actions reduce costs without impacting performance.

Answer

Migrate all services to a higher tier for better performance.

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Deactivate unused resources to save costs.

Question 5

A company is building a chatbot using Azure Language Service and wants to ensure that the chatbot can understand user intents and extract entities from user utterances. The chatbot must be able to handle multiple intents in a single utterance and must support pre-built entities such as numbers and dates. Which action should the developer take to configure the Language service accordingly?

Accepted Answer

Enable the 'Multiple intents' setting in the Language service project.. The Azure Language Service's Conversational Language Understanding (CLU) project includes a 'Multiple intents' setting that, when enabled, allows the model to predict more than one intent per utterance. This is essential for handling compound user inputs where the user expresses multiple goals in a single sentence.

Answer

Upgrade the Language service tier from Standard to Custom.

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Configure the project to use Orchestration workflow.

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Set the project language to 'Multilingual' to enable entity recognition.

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Define list entities for numbers and dates.

Question 6

Which TWO Azure AI services can be used to build a conversational AI system that handles multi-turn dialogues with context?

Accepted Answer

Azure AI Language. Azure AI Language provides conversational language understanding (CLU) capabilities that enable you to build a model capable of understanding multi-turn dialogues by maintaining context across user utterances. Azure OpenAI Service offers advanced language models like GPT-4, which can handle multi-turn conversations with context through prompt engineering and conversation history, making it suitable for building conversational AI systems.

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Azure AI Search

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Azure AI Content Safety

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Azure AI Bot Service

Question 7

A company uses Azure AI Language Service with Custom Entity Recognition to extract invoice fields. The model correctly extracts invoice numbers but fails to extract dates in the format 'dd/mm/yyyy'. The training data includes dates in 'mm/dd/yyyy' format. What is the most likely issue?

Accepted Answer

The training data does not contain examples with the 'dd/mm/yyyy' format. Custom Entity Recognition in Azure AI Language Service learns patterns from labeled training data. Since the training data only contains dates in 'mm/dd/yyyy' format, the model has not seen any examples of 'dd/mm/yyyy' and therefore cannot generalize to that format. The model relies on the exact token sequences and date structures present in the training set, so missing format variations directly cause extraction failures.

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The dates exceed the maximum entity length

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The language detection is incorrectly identifying the locale

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The model is overfitting to invoice numbers

Question 8

You are troubleshooting a Copilot Studio agent that uses a Power Automate flow to look up customer information from a CRM system. The flow runs successfully when tested manually, but when the agent triggers it, the flow fails with an authentication error. What is the most likely cause?

Accepted Answer

The flow uses the agent's identity instead of the user's identity, and the agent lacks CRM access.. When a Copilot Studio agent triggers a Power Automate flow, the flow runs in the context of the agent's identity (the service principal or bot registration) rather than the user who is interacting with the agent. If the flow uses the agent's identity to authenticate with the CRM system, and that identity has not been granted the necessary permissions (e.g., read/write access to customer records), the authentication will fail. Manual tests succeed because they run under the developer's or tester's identity, which already has CRM access.

Answer

The flow connection was deleted after the manual test.

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The user must sign in again before triggering the flow.

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The CRM connector requires additional permissions that were not granted.

Question 9

You are designing a generative AI solution using Azure OpenAI Service with your own data indexed in Azure AI Search. Which THREE components are essential for the retrieval-augmented generation (RAG) pattern?

Accepted Answer

Data ingestion pipeline to Azure AI Search. A data ingestion pipeline is essential to load and index your data into Azure AI Search, enabling the retrieval step in RAG. Without this pipeline, the search index would have no data to query, breaking the retrieval-augmented generation pattern.

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Azure Functions for orchestration

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Azure API Management for rate limiting

Question 10

You are developing a knowledge mining solution for a legal firm that needs to process thousands of legal contracts stored as PDFs in Azure Blob Storage. The solution must extract clauses, parties, and dates using a custom model. You are using Microsoft Foundry with Azure AI Search and Azure AI Document Intelligence. The custom model must be trained on labeled contract data. After training, you deploy the model and integrate it into the AI Search enrichment pipeline. The pipeline must also perform OCR for scanned contracts. You have configured the following:

- A custom classification model in Document Intelligence for document types.
- A custom extraction model in Document Intelligence for clauses, parties, and dates.
- An Azure AI Search index with fields: clause, party, date.
- A skillset with a Document Intelligence skill pointing to the custom extraction model.

During testing, the pipeline runs successfully for digital PDFs but fails for scanned PDFs. The error indicates that OCR is not being applied. What should you do to fix the issue?

Accepted Answer

Modify the Document Intelligence skill configuration to enable OCR processing.. Scanned PDFs contain images of text, so OCR must be performed to extract text before the custom extraction model can process it. Azure AI Document Intelligence's custom extraction model can perform OCR internally if the skill configuration has OCR enabled. By default, the Document Intelligence skill in the skillset does not automatically enable OCR for scanned documents; you must set the `enableOcr` property to `true` in the skill configuration. Option A is incorrect because retraining the model with scanned images would not enable OCR; the model still needs text input. Option B is incorrect because recreating the index does not address the missing OCR step. Option D is incorrect because adding a separate OCR skill is not necessary; Document Intelligence can handle OCR internally when configured correctly.

Answer

Retrain the custom extraction model with scanned document images.

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Delete and recreate the index with a different field mapping.

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Add an OCR skill to the skillset before the Document Intelligence skill.

Question 11

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.

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Azure Custom Vision

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Azure Form Recognizer

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Azure Computer Vision

Question 12

Drag and drop the steps to create a custom question answering project in Azure Language Service into the correct order.

Accepted Answer

Step 1: Create Azure resource, Step 2: Access Language Studio, Step 3: Create project, Step 4: Add QnA pairs, Step 5: Train and deploy. First, create the Azure resource. Then access Language Studio, create the project, add QnA pairs, and finally train and deploy.

Answer

Step 1: Access Language Studio, Step 2: Create Azure resource, Step 3: Create project, Step 4: Add QnA pairs, Step 5: Train and deploy

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Step 1: Create Azure resource, Step 2: Create project, Step 3: Access Language Studio, Step 4: Add QnA pairs, Step 5: Train and deploy

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Step 1: Add QnA pairs, Step 2: Create project, Step 3: Create Azure resource, Step 4: Access Language Studio, Step 5: Train and deploy

Question 13

You are deploying an agentic solution that must comply with data residency requirements. The agent processes personal data from users in the European Union. Which THREE actions should you take to ensure compliance?

Accepted Answer

Deploy the agent service in an Azure region within the European Union. Deploying the agent service in an Azure region within the European Union ensures that personal data is processed and stored in a location that meets EU data residency requirements. Azure's regional deployment guarantees that data does not leave the specified geographic boundary, which is essential for compliance with regulations like GDPR.

Answer

Disable logging for the agent to avoid storing personal data

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Configure the agent's data storage to use a globally redundant storage account

Question 14

A company uses Azure Document Intelligence to extract data from tax forms. They need to improve accuracy for a specific field. Which TWO actions should they take?

Accepted Answer

Label more examples of the specific field in the training set. Labeling more examples of the specific field in the training set directly provides the custom model with additional ground-truth annotations for that field. This increases the model's ability to learn the variations in handwriting, formatting, and layout for that field, which is the most effective way to improve extraction accuracy for a targeted field in Azure Document Intelligence custom models.

Answer

Increase the batch size in the analysis request

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Reduce the image resolution to 200 DPI

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Use the prebuilt-tax.us model

Question 15

You work for a manufacturing company that uses Azure AI services to automate quality inspection on a production line. You have a Custom Vision object detection model that identifies defects on metal parts. The model was trained on images captured under ideal lighting conditions. However, when deployed in the factory, the model's accuracy drops significantly due to inconsistent lighting and glare. You need to improve the model's robustness without collecting new images from the factory floor. What should you do?

Accepted Answer

Apply data augmentation techniques such as brightness, contrast, and blur adjustments to the existing training images.. Using data augmentation techniques like brightness and contrast adjustments, rotation, and noise injection can simulate various lighting conditions and improve robustness. Option A is wrong because increasing training iterations may overfit to the existing data. Option C is wrong because higher resolution does not address lighting variation. Option D is wrong because changing the model type does not address the data issue.

Answer

Increase the number of training iterations to force the model to learn more features.

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Use higher resolution images for training.

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Change the model type from object detection to classification.