AI-102 Practice Test 37 — 15 Questions

Question 1

You are the Azure AI engineer for a large e-commerce company. The company uses Azure Computer Vision to automatically tag product images uploaded by sellers. The system has been running smoothly for months. However, after a recent update to the Computer Vision API, you notice that certain images of clothing items are being tagged with incorrect labels, such as 'shoe' for a shirt. The images are clear and well-lit. You have confirmed that the image format (JPEG) is supported and the size is within limits. The issue occurs consistently for clothing items with similar colors. Other product categories work fine. You suspect the issue is related to the API version. What should you do first?

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

Accepted Answer

Check the API version used in the application code and compare with the latest version.. The issue began after a Computer Vision API update, and the problem is specific to certain clothing images with similar colors, indicating a potential regression or behavioral change in the API version. Checking the API version used in the application code against the latest version is the first logical troubleshooting step to identify if a breaking change or bug was introduced. This aligns with Azure AI best practices: always verify API version compatibility before modifying thresholds or retraining models.

Answer

Increase the image size limit.

Answer

Switch to a custom model trained on clothing items.

Answer

Reduce the confidence threshold to 50% to see if more tags appear.

Question 2

You need to generate a poem using Azure OpenAI. The poem should be about nature and have a cheerful tone. Which parameter should you adjust to influence the tone?

Accepted Answer

system message. The system message (D) is the correct parameter to influence the tone of a generated poem because it acts as a high-level instruction that sets the behavior, persona, and style of the model. By including a directive like 'You are a cheerful poet writing about nature,' you directly control the tone without altering randomness or output length.

Answer

top_p

Answer

temperature

Answer

max_tokens

Question 3

Refer to the exhibit. You are reviewing a content safety policy for an Azure AI Foundry deployment. The policy rate limits to 20 requests per minute. A user submits 50 requests in one minute. How many requests are allowed?

Accepted Answer

20. The content safety policy enforces a rate limit of 20 requests per minute. When a user submits 50 requests in one minute, the rate limiter allows only the first 20 requests and blocks the remaining 30. This is a standard token-bucket or sliding-window rate-limiting behavior in Azure AI Foundry, where exceeding the limit results in HTTP 429 (Too Many Requests) for excess requests.

Answer

50

Answer

100

Answer

None, all are blocked.

Question 4

Which THREE are required when planning to use Azure OpenAI Service for a generative AI application that must comply with responsible AI principles?

Accepted Answer

Implement content filters to block harmful outputs.. Option B is correct because Azure OpenAI Service provides configurable content filters (categories such as hate, violence, sexual, and self-harm, with severity thresholds) that are a core responsible AI control for blocking harmful model outputs. Option C is correct because human-in-the-loop review is a responsible AI requirement for high-impact or critical decisions, ensuring a human validates or overrides model output before it affects users. Option E is correct because data governance policies for training data address privacy, consent, provenance, and bias concerns, which are foundational to responsible AI compliance. Option A is not required: a 1000-token cap is an arbitrary cost/latency constraint, not a responsible AI principle, and token limits are set per model/deployment as needed. Option D is not required: rate limiting is an availability and abuse-mitigation control, not a responsible AI requirement, and it does not by itself ensure fair, safe, or accountable AI behavior.

Answer

Restrict the model to a maximum of 1000 tokens.

Answer

Enable rate limiting to prevent abuse.

Question 5

You are designing a solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must handle high throughput and process documents in batch. Which TWO configuration options should you use?

Accepted Answer

Provision a standard (S0) tier Document Intelligence resource. Option C is correct because the standard (S0) tier is the production-grade Document Intelligence pricing tier that supports high request volumes and higher transactions-per-second throughput, whereas the free (F0) tier is limited to low-volume testing and would throttle a high-throughput batch workload. Option D is correct because the asynchronous document analysis API (e.g., POST to /documentModels/{modelId}:analyze followed by polling the Operation-Location header) is specifically designed for large-scale, batch processing of many documents without blocking on each request, which is required for high throughput. Option A is wrong because the synchronous API processes one document per blocking call and does not scale well for batch, high-throughput scenarios. Option B is wrong because training a custom model addresses extraction accuracy for specialized document layouts, not throughput or batch processing capability. Option E is wrong because storing output in Blob Storage without API calls bypasses Document Intelligence entirely, so no extraction would occur.

Answer

Use the synchronous API for each invoice

Answer

Train a custom model for invoice extraction

Answer

Store output directly in Azure Blob Storage without API calls

Question 6

Which TWO monitoring metrics should you track to ensure the health and performance of an Azure AI Search service used for a customer-facing product catalog?

Accepted Answer

Throttled search queries count.. Option A, throttled search queries count, is correct because throttling directly indicates that the service is hitting its query-per-second (QPS) capacity limits, which degrades the customer-facing catalog experience and signals a need to scale replicas or partitions. Option D, search latency (average and P99), is correct because latency is the primary performance indicator for a user-facing search experience; tracking both average and P99 reveals tail-latency problems that average alone would hide. Option B is not among the correct answers because indexer execution history and duration relates to data ingestion pipelines, not the query-time health of a customer-facing catalog. Option C is not correct because storage used in GB is a capacity metric that does not reflect query performance or service health for end users. Option E is not correct because counting only successful search requests gives no insight into failures, throttling, or latency, and a rising success count can mask underlying performance degradation.

Answer

Indexer execution history and duration.

Answer

Storage used in GB.

Answer

Number of successful search requests.

Question 7

Which THREE of the following are capabilities of Azure AI Content Safety?

Accepted Answer

Sexual content detection. Azure AI Content Safety provides built-in AI classifiers that detect harmful content across four categories: sexual, hate, violence, and self-harm, so options A (sexual content detection), B (hate speech detection), and C (self-harm detection) are all correct capabilities of the service. These categories are exposed through the Analyze Text and Analyze Image APIs, which return severity scores (0-7) for each category, enabling applications to filter or moderate harmful content. Option D (sentiment analysis) is not part of Content Safety; sentiment analysis is a feature of Azure AI Language. Option E (PII detection) is also not part of Content Safety; PII detection is provided by Azure AI Language's Personally Identifiable Information extraction capability.

Answer

Sentiment analysis

Answer

Personally identifiable information (PII) detection

Question 8

Which TWO Azure services are used together to build a custom question-answering solution?

Accepted Answer

Azure AI Language (Custom Question Answering). Azure AI Language (Custom Question Answering) is correct because it provides the natural-language processing layer that ingests FAQ documents, URLs, and structured sources to create a knowledge base and returns precise answers to user questions. Azure AI Search is correct because it is the underlying retrieval engine that indexes the knowledge base content and performs the semantic/keyword search that Custom Question Answering relies on to match questions to answers. Together they form the standard architecture for a custom question-answering solution: AI Language builds and manages the knowledge base, while AI Search stores and queries the indexed content. Azure AI Computer Vision is for image analysis (OCR, object detection), Azure AI Speech handles speech-to-text/text-to-speech, and Azure AI Translator performs language translation, none of which are required components for building the question-answering knowledge base and retrieval pipeline.

Answer

Azure AI Computer Vision

Answer

Azure AI Speech

Answer

Azure AI Translator

Question 9

Which TWO actions should you take when designing an Azure AI solution that uses Microsoft Foundry to ensure responsible AI practices?

Accepted Answer

Implement a human-in-the-loop review for critical decisions. Option A is correct because implementing a human-in-the-loop review for critical decisions ensures that high-impact or sensitive outcomes are validated by a person before action is taken, which is a core responsible AI safeguard against harmful or erroneous automated decisions. Option C is correct because running an AI fairness assessment on the model systematically evaluates performance across demographic groups and helps detect and mitigate bias, directly supporting Microsoft's responsible AI principles of fairness and inclusiveness. Option B does not belong because optimizing for maximum throughput is a performance and cost concern, not a responsible AI practice, and can even conflict with safety if it bypasses safeguards. Option D does not belong because retaining all training data indefinitely raises privacy, data minimization, and compliance risks rather than promoting responsible AI. Option E does not belong because removing explainability metrics reduces transparency and accountability, which is the opposite of responsible AI design.

Answer

Optimize the model for maximum throughput

Answer

Store all training data indefinitely for auditability

Answer

Remove all explainability metrics to simplify the model

Question 10

Refer to the exhibit.

{
  "content_filters": [
    {
      "type": "hate",
      "action": "block",
      "severity": "high"
    },
    {
      "type": "sexual",
      "action": "block",
      "severity": "medium"
    },
    {
      "type": "self_harm",
      "action": "block",
      "severity": "low"
    }
  ]
}

You deploy an Azure OpenAI model with the above content filter configuration. A user submits a prompt that the system rates as "hate" at severity level "medium". What happens?

Accepted Answer

The prompt is allowed because the severity is below the threshold.. The content filter configuration blocks 'hate' content only at severity level 'high'. Since the user's prompt was rated as 'hate' at severity 'medium', it falls below the configured threshold and is allowed. Azure OpenAI content filters evaluate severity levels (low, medium, high) and apply the configured action only when the detected severity meets or exceeds the specified threshold.

Answer

The prompt is blocked because hate content is detected.

Answer

The prompt is blocked because the severity is medium.

Answer

The prompt is allowed because the hate filter is not configured for medium.

Question 11

A company is using Azure Cognitive Service for Language to analyze customer support transcripts. They want to identify custom categories (e.g., 'billing', 'technical support') using a custom text classification model. After training and deploying the model, they receive many false positives for the 'billing' category. What is the best first step to improve model accuracy?

Accepted Answer

Review the training data for the 'billing' category and correct any mislabeled examples.. False positives for a specific category like 'billing' most often stem from mislabeled or ambiguous training examples in that category. By reviewing and correcting the training data for 'billing', you directly address the root cause of the model's confusion, which is the most effective first step in custom text classification model improvement.

Answer

Add more training data to all categories to improve overall model performance.

Answer

Use a different Azure AI service, such as key phrase extraction, to identify billing-related content.

Answer

Increase the confidence threshold for the 'billing' category to reduce false positives.

Question 12

A developer is using Azure Cognitive Service for Language to perform sentiment analysis on customer reviews. The service returns sentiment labels (positive, negative, neutral) and confidence scores. For a particular review, the service returns 'positive' with a confidence score of 0.55. The developer wants to ensure that only high-confidence results are used. What should the developer do?

Accepted Answer

Configure a minimum confidence threshold of 0.75 in the application logic.. The developer must implement a confidence threshold in the application logic to filter out low-confidence results. The Azure Cognitive Service for Language returns confidence scores between 0 and 1 for each sentiment label, and the developer can set a minimum threshold (e.g., 0.75) to ensure only high-confidence predictions are used. This approach does not require retraining the model or modifying the input text, as the threshold is applied post-inference in the client code.

Answer

Use the Text Analytics for Health API instead.

Answer

Retrain the sentiment analysis model with additional labeled data.

Answer

Adjust the input text by removing ambiguous phrases.

Question 13

You are developing a generative AI solution that uses Azure OpenAI Service. You need to control the creativity of the generated responses. Which parameter should you adjust?

Accepted Answer

temperature. The temperature parameter directly controls the randomness of token selection in the model's output. Lower values (e.g., 0.2) make the model more deterministic and focused, while higher values (e.g., 0.8) increase creativity and variability. This is the primary parameter for adjusting creativity in Azure OpenAI Service.

Answer

top_p

Answer

max_tokens

Answer

frequency_penalty

Question 14

Refer to the exhibit. You are configuring an Azure OpenAI Service deployment for document summarization. The current parameters produce summaries that are often too verbose. You need to make the summaries more concise while maintaining factual accuracy. Which parameter change should you make?

Accepted Answer

Decrease max_tokens to 100. Decreasing max_tokens to 100 directly limits the maximum length of the generated summary, forcing the model to produce shorter output. This addresses the verbosity issue without altering the model's factual accuracy, as max_tokens controls output length, not content selection or creativity.

Answer

Increase top_p to 1.0

Answer

Increase frequency_penalty to 0.5

Answer

Increase temperature to 0.7

Question 15

A healthcare organization needs to redact personally identifiable information (PII) from patient records before using them for research. They have large volumes of unstructured text in multiple languages. Which Azure AI service should they use?

Accepted Answer

Azure AI Language (PII detection feature). Azure AI Language's PII detection feature is specifically designed to identify and redact personally identifiable information from unstructured text. It supports multiple languages and can handle large volumes of text, making it the correct choice for redacting PII from patient records before research use.

Answer

Azure AI Content Safety

Answer

Azure AI Document Intelligence (formerly Form Recognizer)

Answer

Azure AI Translator