AI-102 Practice Test 6 — 10 Questions

Question 1

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

Answer

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

Answer

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 2

Which THREE actions can be performed using the Azure Custom Vision service?

Accepted Answer

Export a trained model to ONNX format for offline inference.. Option B is correct because Azure Custom Vision supports exporting trained models in several formats, including ONNX, TensorFlow, CoreML, and Docker, enabling offline or edge inference. Option C is correct because Custom Vision is designed for image classification, allowing you to train a model that assigns images to labeled classes such as product types. Option D is correct because Custom Vision also supports object detection, which returns bounding boxes and labels for multiple objects within an image. Option A is not correct because extracting text from scanned receipts is an OCR task handled by Azure AI Vision (Computer Vision Read API) or Document Intelligence, not Custom Vision. Option E is not correct because identifying specific individuals via facial recognition is provided by Azure AI Face, not Custom Vision.

Answer

Extract text from scanned receipts.

Answer

Identify specific individuals in a crowd using facial recognition.

Question 3

Which TWO of the following are best practices for securing Azure AI services?

Accepted Answer

Enable diagnostic settings to audit usage and detect anomalies.. Option C is correct because enabling diagnostic settings on Azure AI services streams resource logs and metrics to destinations such as Log Analytics, Azure Storage, or Event Hubs, which supports auditing usage, monitoring for anomalies, and meeting compliance requirements. Option E is correct because managed identities let applications authenticate to Azure AI services via Microsoft Entra ID tokens, eliminating the need to store or rotate API keys in code or configuration. Option A is incorrect since publicly exposing endpoints increases the attack surface; access should be restricted with private endpoints, network ACLs, or Azure Private Link. Option B is incorrect because disabling diagnostic logging removes the audit trail needed to detect misuse and investigate incidents. Option D is incorrect because sharing API keys across applications prevents per-app revocation and least-privilege scoping, so keys should be unique, stored in Key Vault, and rotated regularly.

Answer

Expose endpoints publicly to simplify client access.

Answer

Disable diagnostic logging to reduce data exposure.

Answer

Share API keys among multiple applications for simplicity.

Question 4

A company wants to generate product descriptions for thousands of items using an Azure OpenAI GPT-4 model. They need to ensure the descriptions match a consistent brand voice. Which approach is most efficient and cost-effective?

Accepted Answer

Set a system message with brand voice guidelines and use few-shot examples. Setting a system message with brand voice guidelines and providing few-shot examples allows the GPT-4 model to consistently apply the desired tone and style across all product descriptions without retraining. This approach is efficient and cost-effective as it avoids the high compute and data preparation costs of fine-tuning, while still enabling precise control over output through in-context learning.

Answer

Write a separate prompt for each product category

Answer

Use Azure OpenAI on your data with a vector database of brand guidelines

Answer

Fine-tune a base model on existing product descriptions

Question 5

Refer to the exhibit. A developer runs this PowerShell script to call Azure OpenAI. The script fails with an authentication error. What is the most likely cause?

Accepted Answer

The script uses the wrong HTTP header; it should use 'api-key' instead of 'Authorization: Bearer'.. The script uses 'Authorization: Bearer' header, but Azure OpenAI requires the API key to be passed in the 'api-key' header. The 'Authorization: Bearer' header is used for Microsoft Entra ID token-based authentication, not for direct API key authentication. Since the script is using an API key (as indicated by the PowerShell script), the correct header is 'api-key'.

Answer

The script uses the wrong HTTP method; it should use GET.

Answer

The API version is incorrect.

Answer

The endpoint URI is missing the resource name.

Question 6

You need to generate realistic synthetic data using Azure OpenAI Service to train a machine learning model. The data must be diverse and cover edge cases. Which approach should you use?

Accepted Answer

Use prompt engineering with detailed instructions to generate varied examples.. Prompt engineering with detailed instructions allows you to explicitly control the diversity, structure, and edge-case coverage of generated synthetic data without requiring a pre-existing dataset. By crafting system messages and user prompts that specify variations in attributes, formats, and boundary conditions, you can produce a wide range of realistic examples that mimic real-world distributions, which is essential for training robust machine learning models.

Answer

Fine-tune the model on a small dataset of real examples.

Answer

Use Azure OpenAI embeddings to generate similar data points.

Answer

Set a high temperature parameter only.

Question 7

Your company uses Azure AI Content Safety to moderate user-generated content in a chat application. You need to detect and block sexual content in multiple languages. Which pre-built category should you configure?

Accepted Answer

Sexual. Azure AI Content Safety provides pre-built severity-based categories for content moderation. The 'Sexual' category is specifically designed to detect and block explicit sexual content, including text and images, across multiple languages. This makes it the correct choice for your requirement to moderate sexual content in a chat application.

Answer

Self-harm

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Hate

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Violence

Question 8

You are developing a custom chatbot using Azure AI Bot Service and Language Understanding (CLU). The chatbot needs to escalate to a human agent when the user's sentiment is negative. Which component should you use to detect sentiment?

Accepted Answer

Azure AI Language sentiment analysis. Azure AI Language sentiment analysis is the correct component because it provides pre-built sentiment detection capabilities that analyze text and return sentiment labels (positive, negative, neutral) and confidence scores. This directly meets the requirement to detect negative user sentiment in chatbot conversations, enabling escalation to a human agent when needed.

Answer

Azure Cognitive Search

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Orchestration workflow

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QnA Maker

Question 9

You are a generative AI engineer at a financial services company. The company uses Azure OpenAI Service to generate investment summaries. You have deployed a GPT-4 model with a content filter set to 'Low' for hate speech. The model frequently generates summaries that include biased language against certain demographics. You need to reduce biased outputs while maintaining the ability to generate detailed financial analysis. You cannot afford to retrain the model. You have the following options: A) Change the content filter severity to 'High' for all categories, B) Add a system message instructing the model to avoid bias and provide examples of unbiased summaries in the prompt, C) Use the Azure AI Language service to detect bias in the output and regenerate if bias is found, D) Deploy a different model like GPT-3.5 which has less bias. Which course of action should you take?

Accepted Answer

Add a system message instructing the model to avoid bias and provide examples of unbiased summaries in the prompt.. Adding a system message that explicitly instructs the model to avoid bias and providing few-shot examples of unbiased summaries is the most direct and cost-effective way to steer the model's behavior without retraining. This approach leverages prompt engineering to influence the model's output distribution while preserving the ability to generate detailed financial analysis.

Answer

Use the Azure AI Language service to detect bias in the output and regenerate if bias is found.

Answer

Change the content filter severity to 'High' for all categories.

Answer

Deploy a different model like GPT-3.5 which has less bias.

Question 10

You are implementing a generative AI solution using Azure OpenAI. You need to ensure that the model's outputs do not contain certain inappropriate words or phrases. Which feature should you configure?

Accepted Answer

Content filters. Content filters in Azure OpenAI are specifically designed to detect and block inappropriate words or phrases in both prompts and completions. They operate at the service level, applying configurable severity thresholds for categories like hate, violence, sexual content, and self-harm, ensuring model outputs adhere to policy without requiring prompt engineering or data modifications.

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System message instructions

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Grounding with your data

Answer

Max tokens limit