AI-102 Practice Questions 2 — 50 Questions

Question 1

You are testing an Azure OpenAI model with the parameters shown in the exhibit. The model generates very short responses. Which parameter should you modify to allow longer responses?

Accepted Answer

Increase max_tokens. The max_tokens parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. When responses are very short, increasing max_tokens allows the model to produce longer completions up to the specified limit. The other parameters affect randomness, diversity, or probability distribution, not the length cap.

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Increase frequency_penalty

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Increase top_p

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Increase temperature

Question 2

Refer to the exhibit. You are troubleshooting an Azure OpenAI API call that is returning incomplete responses. The response stops mid-sentence. Which parameter should you adjust?

Accepted Answer

Increase max_tokens to 1000.. The `max_tokens` parameter controls the maximum number of tokens the model can generate in a single response. When a response stops mid-sentence, it typically means the token limit was reached before the model could complete its output. Increasing `max_tokens` to 1000 provides more room for the model to finish its generation, resolving the truncation issue.

Answer

Remove the stop parameter.

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Increase temperature to 1.0.

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Increase top_p to 1.0.

Question 3

A developer uses the Azure OpenAI API to generate code. They want to ensure that the generated code is in Python. Which parameter should they set?

Accepted Answer

system message. The system message parameter in the Azure OpenAI API is used to set the behavior and context of the assistant, including specifying the desired output format or language. By setting the system message to something like 'You are a helpful assistant that always generates Python code,' the developer can instruct the model to produce Python code consistently. This is the correct parameter for guiding the model's response style and content.

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temperature

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max_tokens

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top_p

Question 4

Which THREE considerations are important when planning to use Azure OpenAI Service in a production environment?

Accepted Answer

Configure content filtering to block harmful outputs. Azure OpenAI Service includes built-in content filtering to detect and block harmful outputs such as hate, violence, or self-harm. This is a critical safety and compliance requirement for production deployments, as it helps meet responsible AI principles and regulatory obligations. Without configuring content filters, the service could generate inappropriate responses that violate usage policies or legal standards.

Answer

Train a custom model from scratch using your own data

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Deploy the service within a virtual network (VNet)

Question 5

Refer to the exhibit. An administrator runs this Azure CLI command to deploy a GPT-4 model in Azure AI Foundry. The command fails with an error that the deployment name already exists. What should the administrator do to resolve the issue?

Accepted Answer

Use a different deployment name or delete the existing deployment.. The error message indicates that a deployment with the same name already exists in the Azure AI Foundry workspace. In Azure AI Foundry, deployment names must be unique within a workspace. The correct resolution is to either choose a different deployment name or delete the existing deployment before re-running the command. This aligns with the Azure CLI behavior where resource names (including AI model deployments) must be unique per scope.

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Specify a different resource group.

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Remove the --sku-name parameter.

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Use a different model version.

Question 6

A developer uses the Azure OpenAI SDK to generate code snippets. The generated code sometimes contains security vulnerabilities. What is the most effective way to mitigate this risk?

Accepted Answer

Include a system message that instructs the model to avoid insecure coding patterns.. A system message sets the behavioral context for the model at inference time, instructing it to avoid insecure coding patterns without requiring retraining. This is the most direct and effective mitigation as it leverages the model's instruction-following capability to reduce vulnerabilities in generated code, aligning with Azure OpenAI's content filtering and safety system guidance.

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Set the temperature parameter to 0 to make the output deterministic.

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Post-process the generated code using a static code analysis tool.

Answer

Fine-tune the model on a dataset of secure code examples.

Question 7

You are designing an Azure AI solution for a global e-commerce company. The solution must: (1) Translate product descriptions into 12 languages in real-time. (2) Detect sentiment in customer reviews for each language. (3) Extract key product attributes (e.g., color, size) from unstructured review text. (4) Store results in a centralized database for analytics. The solution must minimize latency and cost. You plan to use Azure AI services. Which combination of services should you use?

Accepted Answer

Azure AI Translator for translation, Azure AI Language for sentiment and entity extraction, and Azure AI Search to index the results.. Azure AI Translator provides real-time translation into 12 languages, Azure AI Language handles both sentiment analysis and entity extraction (for attributes like color and size) from unstructured text, and Azure AI Search indexes the results for low-latency analytics. This combination minimizes latency by using dedicated services for each task and avoids unnecessary overhead from services like Speech or Computer Vision that are not required for text-only processing.

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Azure AI Language for translation and sentiment, and Azure AI Computer Vision for attribute extraction.

Answer

Azure AI Speech for translation, Azure AI Language for sentiment, and Azure AI Search for storage.

Answer

Azure AI Translator for translation, Azure AI QnA Maker for attribute extraction, and Azure SQL Database for storage.

Question 8

You are using Azure AI Foundry to fine-tune a GPT-3.5 model on a dataset of customer service conversations. The fine-tuning job fails with an error indicating that the training data format is invalid. What is the most likely issue?

Accepted Answer

The training data is not in JSONL format with the correct structure.. Azure AI Foundry requires fine-tuning data to be in JSONL format with a specific structure: each line must be a JSON object containing a 'messages' array with 'role' and 'content' fields for system, user, and assistant turns. The error indicates the training data format is invalid, and the most likely cause is that the data is not in this required JSONL structure, as JSONL is the only accepted format for GPT-3.5 fine-tuning in Azure OpenAI Service.

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The training data is in CSV format instead of JSON.

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The training data contains only one conversation example.

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The training data does not include the assistant's responses.

Question 9

Refer to the exhibit. You submit this request to Azure AI Language's conversational language understanding (CLU) for the 'FlightBooking' project. The model correctly identifies the intent as 'BookFlight' and extracts entities: 'Seattle' as FromCity, 'New York' as ToCity, and 'June 15th' as Date. What is the next step for the application?

Accepted Answer

Call a separate booking API with the extracted entities to complete the reservation.. After CLU extracts the intent and entities, the application must use those entities to call a separate booking API to complete the reservation. CLU itself does not perform bookings; it only provides language understanding. Option B is incorrect because the CLU response cannot directly book a flight. Option C is incorrect because the intent is already clearly identified as 'BookFlight' and entities are extracted, so no rephrasing is needed. Option D is incorrect because sending another request to CLU would not confirm booking; confirmation is handled by the booking API.

Answer

Use the CLU response to directly book the flight via the Azure AI Language service.

Answer

Prompt the user to rephrase the request because the intent is ambiguous.

Answer

Send another request to CLU to confirm the booking details.

Question 10

You are developing a chatbot for a retail company using Azure AI Language's custom question answering. The chatbot must provide answers from a knowledge base of 500 FAQ documents. Users often ask the same question in different wording, and the chatbot fails to return an answer for paraphrased queries. What is the most effective solution?

Accepted Answer

Enable active learning in the project settings and periodically publish the updated knowledge base.. Enabling active learning in Azure AI Language's custom question answering allows the system to learn from user interactions. It suggests alternative phrasings for existing QnA pairs, which helps answer paraphrased queries without manual effort. Option A is wrong because Direct Line Speech channel is for voice interactions, not improving answer coverage. Option C is wrong because simply adding more documents does not address the paraphrasing issue; it may increase redundancy but not handle different wording of the same question. Option D is wrong because manually adding alternate phrases is time-consuming and not scalable; active learning automates this process by identifying and suggesting variations based on user queries.

Answer

Use Azure AI Bot Service's Direct Line Speech channel to improve accuracy.

Answer

Increase the number of FAQ documents in the knowledge base.

Answer

Manually add alternate question phrases to the knowledge base for each QnA pair.

Question 11

You are building a generative AI solution using Azure AI Foundry. The solution must meet compliance requirements that require all model inputs and outputs to be auditable for a minimum of one year. What should you enable?

Accepted Answer

Diagnostic settings to capture request and response logs and store them in a storage account.. Enabling diagnostic settings for the Azure AI Foundry resource allows you to capture detailed request and response logs for all model interactions. By routing these logs to a storage account, you retain the data for the required one-year audit period, meeting compliance needs for full traceability of inputs and outputs.

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Azure Monitor alerts for unusual activity.

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Azure Monitor metrics for the Azure AI Foundry resource.

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Azure Monitor workbooks to visualize usage.

Question 12

You are using Azure OpenAI Service to generate product descriptions. The output is often too verbose. You need to reduce the length of generated text without changing the model. Which parameter should you adjust?

Accepted Answer

Max tokens. Max tokens controls the total length of the generated response by capping the number of tokens (words/subwords) the model can output. Reducing this value directly truncates the output, making descriptions shorter without altering the model or its behavior. Other parameters influence randomness or repetition but do not enforce a strict length limit.

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Frequency penalty

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Temperature

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Top-p (nucleus sampling)

Question 13

You are developing a generative AI application that uses Azure OpenAI Service to summarize large documents. The application experiences high latency when processing requests. You need to reduce the latency without changing the model. What should you do?

Accepted Answer

Reduce the max_tokens parameter in the API request. Reducing the max_tokens parameter limits the length of the generated response, which directly reduces the processing time required by the Azure OpenAI Service to produce the output. Since latency is caused by the model generating a long sequence of tokens, capping the output tokens decreases the number of autoregressive decoding steps, thereby lowering response time without altering the underlying model.

Answer

Increase the temperature parameter

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Increase the top_p parameter

Answer

Increase the max_tokens parameter

Question 14

You are reviewing the ARM template snippet for an Azure AI Foundry hub. After deployment, you notice that the hub cannot connect to the storage account. What is the most likely cause?

Accepted Answer

The hub uses a managed network that blocks outbound traffic to the storage account. The most likely cause is that the hub uses a managed network which, by default, blocks outbound traffic to the storage account unless explicitly allowed via outbound rules or private endpoints. Azure AI Foundry hubs enforce network isolation for security, and if the storage account is not configured as an approved destination, the hub cannot establish the required connections for data access.

Answer

The location 'eastus' does not support AI Foundry hubs

Answer

The storage account name contains invalid characters

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The key vault is not configured with a firewall rule

Question 15

A legal firm is using Azure AI Language to analyze contracts. They need to extract key clauses, parties involved, and dates. The solution must be customizable to their specific contract types. Which TWO Azure AI Language features should they use?

Accepted Answer

Prebuilt NER for Legal. Prebuilt NER for Legal is correct because it provides out-of-the-box entity extraction tailored to legal documents, including parties, dates, and key clauses, without requiring custom training. This feature is specifically designed for legal use cases, making it ideal for a firm that needs to quickly extract standard legal entities from contracts.

Answer

Conversation summarization

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Key phrase extraction

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Entity linking

Question 16

A company uses Azure OpenAI to generate code snippets. They notice that the model sometimes produces code that uses deprecated APIs. They want to minimize this without retraining the model. What should they do?

Accepted Answer

Add a system message instructing the model to use only current, non-deprecated APIs.. Adding a system message in Azure OpenAI allows you to set high-level instructions that guide the model's behavior without retraining. By explicitly instructing the model to use only current, non-deprecated APIs, you leverage the system prompt's ability to influence output style and content, effectively reducing deprecated API usage in generated code snippets.

Answer

Fine-tune the model on a dataset of recent code.

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Set the temperature parameter to 0 to reduce randomness.

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Provide a few-shot example of correct code in the prompt.

Question 17

Which TWO features of Azure AI Content Safety can help you moderate user-generated content in a social media application?

Accepted Answer

Self-harm content detection.. Self-harm content detection (A) is a feature of Azure AI Content Safety that specifically identifies text or images related to self-harm, which is a critical category for moderating user-generated content in social media to prevent harm and comply with safety policies. Hate speech severity detection (B) is another core feature that classifies hate speech into severity levels (e.g., low, medium, high), enabling nuanced moderation of offensive content.

Answer

PII redaction.

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Groundedness detection.

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Prompt injection detection.

Question 18

You are designing a computer vision solution for a retail chain to detect shelf stockouts using store camera feeds. Videos are processed in near real-time. Which combination of Azure services should you use to minimize latency and cost?

Accepted Answer

Use Azure Video Analyzer for Media (formerly Video Indexer) with an Azure IoT Edge module processing video at the edge.. Azure Video Analyzer for Media (formerly Video Indexer) with an Azure IoT Edge module enables near real-time processing at the edge, minimizing latency by avoiding round trips to the cloud, and reduces cost by processing video locally and only sending relevant insights (e.g., stockout detections) to the cloud. This architecture is ideal for retail chains with store camera feeds that require low-latency, cost-effective stockout detection.

Answer

Use Azure Video Indexer to analyze videos and send results to Azure SQL Database.

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Use Azure Custom Vision to detect stockouts in video frames.

Answer

Use Azure Media Services to transcode video and then run Custom Vision on key frames.

Question 19

You are deploying a generative AI solution using Azure OpenAI Service. You need to monitor the usage and costs associated with the service. What should you use?

Accepted Answer

Azure Monitor. Azure Monitor is the correct choice because it provides detailed metrics, logs, and alerts for Azure OpenAI Service usage, including token consumption, request counts, and latency. This data is essential for tracking costs and usage patterns, as Azure OpenAI charges based on tokens processed. Azure Monitor integrates directly with the service to surface these operational metrics.

Answer

Microsoft Purview

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Azure Advisor

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Azure Cost Management

Question 20

Refer to the exhibit. You are configuring a system message for an Azure OpenAI deployment. The assistant is still generating harmful code despite the instruction. Which additional measure should you implement?

Accepted Answer

Enable Azure AI Content Safety with a custom blocklist for harmful code.. Azure AI Content Safety provides a dedicated content filtering layer that can block harmful code generation at the inference level, regardless of the system message. A custom blocklist allows you to define specific patterns (e.g., code snippets for malware) that the model is prohibited from outputting, enforcing safety beyond prompt instructions.

Answer

Fine-tune the model on safe code examples.

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Lower the temperature parameter to 0.

Answer

Add more examples to the prompt.