AI-102 Practice Test 3 — 25 Questions

Question 1

You are an AI engineer at a healthcare company. The company uses Azure Cognitive Services to process medical records. They have a Computer Vision resource deployed in the East US region. Recently, they implemented a custom vision model for detecting specific anomalies in X-ray images. The model was trained using the Custom Vision portal and exported as a TensorFlow model. They deployed the model to an Azure Kubernetes Service (AKS) cluster using a Docker container. The container runs the model and exposes a REST API endpoint for inference. The endpoint is used by a web application that is also hosted in the same AKS cluster. The web application is experiencing high latency when making inference requests. The latency spikes up to 10 seconds during peak hours. The AKS cluster has autoscaling enabled based on CPU metrics. The container's resource limits are set to 1 CPU core and 2 GB memory. The model's inference time on a single image is approximately 500 ms on the development machine. The team has not changed the model or the application code recently. The number of concurrent users has increased by 50% in the last month. What should you do to reduce inference latency?

Accepted Answer

Increase the CPU limit for the container to 4 cores.. Option D is correct because the inference latency is caused by CPU saturation during peak hours. The container is limited to 1 CPU core, and with a 50% increase in concurrent users, the single core becomes a bottleneck, causing inference times to spike. Increasing the CPU limit to 4 cores allows the model to process multiple requests in parallel, reducing queue wait times and overall latency.

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Increase the memory limit for the container to 8 GB.

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Deploy additional replicas of the container and use a load balancer.

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Optimize the model by quantizing it to reduce inference time.

Question 2

A developer wants to integrate a pre-built AI model that can extract key information from invoices, such as vendor name, invoice date, and total amount. Which Azure AI service should they use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract structured data from documents like invoices, including fields such as vendor name, invoice date, and total amount. It uses pre-built models trained on common document types, making it ideal for this use case without requiring custom training.

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Azure AI Language

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Azure OpenAI Service

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Azure Cognitive Search

Question 3

A hospital uses Azure Cognitive Service for Language to extract medical entities from clinical notes. The extraction accuracy for medication names and dosages is low. The engineer needs to improve performance without adding new training data. Which solution should the engineer implement?

Accepted Answer

Use custom entity recognition with a prebuilt healthcare entity component.. Option B is correct because the engineer can use custom entity recognition with a prebuilt healthcare entity component, which leverages the existing Text Analytics for Health model's pre-trained entities (including medication names and dosages) without requiring additional training data. This approach combines the prebuilt healthcare model's high accuracy for medical entities with custom entity recognition to fine-tune extraction for specific clinical notes, improving performance without adding new annotated data.

Answer

Add more training data with annotated entities.

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Retrain the Text Analytics for Health model with additional labeled data.

Answer

Increase the confidence threshold for entity extraction.

Question 4

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?

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

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Increase the max_tokens parameter

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Increase the frequency_penalty parameter

Question 5

A company is designing a solution that uses Azure Cognitive Services for text analytics. The solution must meet the following requirements: 
- Detect sentiment in customer feedback. 
- Extract key phrases from the feedback. 
- Identify the language of the feedback automatically. 
- Ensure that the solution can scale to handle thousands of requests per second. 
Which TWO actions should the company take?

Accepted Answer

Use the Text Analytics API for sentiment analysis and key phrase extraction.. Option B is correct because the Text Analytics API (part of Azure Cognitive Services for Language) provides built-in capabilities for sentiment analysis, key phrase extraction, and language detection within a single API call. This eliminates the need for separate services and directly satisfies the requirements for detecting sentiment, extracting key phrases, and identifying language automatically.

Answer

Deploy a separate Language Detection service for language identification.

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Use the Free pricing tier to reduce costs.

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Use Azure Functions to aggregate results from multiple API calls.

Question 6

A company uses Azure Content Moderator to moderate text in a chat application. They want to automatically reject messages that contain profanity or personal data. Which API should they use?

Accepted Answer

Text Moderation API. The Text Moderation API (D) is the correct choice because it is specifically designed to scan text content for profanity, personally identifiable information (PII), and other unwanted text patterns. This API returns a moderation score and a list of detected terms, enabling automated rejection of messages that violate the defined policies.

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Review API

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Video Moderation API

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Image Moderation API

Question 7

A company uses Azure Content Moderator to moderate user-generated content. They need to ensure that content moderation workflows comply with regional regulations. Which TWO actions should they take?

Accepted Answer

Deploy Content Moderator resources in the required geographic regions to meet data residency requirements.. Deploying Azure Content Moderator resources in the required geographic regions ensures that user-generated content is processed and stored within specific data boundaries, directly addressing data residency regulations. This is a fundamental compliance requirement because Azure resources are region-bound, and data does not leave the selected region unless explicitly configured otherwise.

Answer

Use the free tier to reduce costs while meeting compliance needs.

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Enable geo-tagging on the Content Moderator API to automatically apply region-specific moderation.

Answer

Configure the API to automatically reject any content that violates regional laws.

Question 8

An image was submitted to Azure Content Moderator's Image Moderation API. The application uses a threshold of 0.5 for Category1 (adult) to trigger a review. Based on the exhibit, what should the application do with this image?

Accepted Answer

Escalate the image to a human reviewer because Category1 score exceeds the threshold.. The application's threshold for Category1 (adult) is 0.5, and the exhibit shows a Category1 score of 0.6, which exceeds this threshold. Even though ReviewRecommended is false, the application's custom threshold overrides the default recommendation, requiring escalation to a human reviewer for manual judgment. This aligns with Azure Content Moderator's design where you can set your own thresholds to trigger reviews regardless of the API's built-in recommendation.

Answer

Auto-approve the image because ReviewRecommended is false.

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Auto-reject the image because no offensive terms were found.

Answer

Log the inconsistency and continue processing without action.

Question 9

You are the AI engineer at a global e-commerce company that allows users to upload product images and descriptions. You use Azure Content Moderator to automatically moderate images for adult and racy content, and text for profanity and personal data. Recently, you noticed that some product descriptions containing profanity in French are not being flagged. Your Content Moderator text moderation API call includes the language parameter set to 'eng'. The profanity list appears to be English-only. You have a requirement to support French and Spanish in addition to English. You also need to ensure that false positives for legitimate product descriptions are minimized. You cannot use a custom term list because the profanity terms are dynamic. What should you do?

Accepted Answer

Set the language parameter to 'auto-detect' in the text moderation API request.. Option C is correct because setting the language parameter to 'auto-detect' allows the Azure Content Moderator text moderation API to automatically identify the language of the input text and apply the corresponding built-in profanity list (including French and Spanish). This meets the requirement to support multiple languages without using a custom term list, and it minimizes false positives by using the appropriate language-specific moderation model rather than a generic English-only list.

Answer

Disable the language parameter so that the API defaults to all languages.

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Create separate API calls for each language and specify the language code in the request.

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Add French and Spanish profanity terms to a custom term list and use the list in the API call.

Question 10

A development team is using Azure Cognitive Service for Language to extract key phrases from customer reviews. They notice that some reviews are not being processed, and the API returns a 400 error code. What is the most likely cause?

Accepted Answer

One of the reviews exceeds the maximum character limit for a single document.. The Azure Cognitive Service for Language key phrase extraction API enforces a maximum document size of 5,120 characters per document. When a single review exceeds this limit, the API returns a 400 Bad Request error because the request payload violates the service's input constraints. This is the most common cause of 400 errors in batch text analysis operations.

Answer

The reviews contain characters that are not valid UTF-8.

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The request contains more than 5 documents.

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The reviews are written in a language not supported by the service.

Question 11

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. Option A is correct because 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

Answer

Use the prebuilt-tax.us model

Question 12

A company builds a knowledge mining solution using Azure AI Search with a custom skillset that includes an OCR skill. They want to ensure that images embedded in PDFs are processed. What should they configure?

Accepted Answer

Set the 'imageAction' parameter to 'generateNormalizedImages'. Option C is correct because the 'imageAction' parameter in Azure AI Search's OCR skill controls whether images embedded in documents (including PDFs) are extracted and processed. Setting it to 'generateNormalizedImages' ensures that images within PDFs are normalized and passed to the OCR skill for text extraction, which is essential for processing embedded images.

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Set the 'defaultLanguageCode' to 'en'

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Set the 'textExtractionAlgorithm' to 'printed'

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Set the 'lineEnding' parameter to 'space'

Question 13

Which THREE components are required to build a custom named entity recognition (NER) model in Azure Cognitive Service for Language?

Accepted Answer

A set of labeled documents for training.. Option C is correct because a custom NER model in Azure Cognitive Service for Language requires a set of labeled documents for training. These labeled documents define the entities and their spans within text, which the model uses to learn patterns for extraction. Without labeled data, the model cannot be trained to recognize custom entities.

Answer

A QnA Maker knowledge base for entity definitions.

Answer

A LUIS application to handle entity extraction.

Question 14

A development team is using the Azure Cognitive Service for Language to perform sentiment analysis on social media posts. They notice that the returned sentiment scores are often neutral for posts that are clearly positive or negative. What is the most likely reason?

Accepted Answer

The posts are too short, causing the sentiment detection to default to neutral.. The Azure Cognitive Service for Language sentiment analysis API has a minimum text length requirement for reliable scoring. When input text is very short (e.g., a few words or a single sentence), the model lacks sufficient context to confidently assign a positive or negative score, so it defaults to a neutral score (often around 0.5). This is a documented behavior of the API, not a limitation of social media language support.

Answer

The service does not support sentiment analysis for social media language.

Answer

The service is not configured to detect mixed sentiment.

Answer

The posts are in a language that is not supported by the sentiment analysis API.

Question 15

A company is building a knowledge mining solution using Azure AI Search. They need to extract key phrases from a large set of documents in multiple languages. Which skill should they add to the skillset?

Accepted Answer

Key Phrase Extraction skill. The Key Phrase Extraction skill is the correct choice because it is specifically designed to identify and extract the most important phrases from text, which directly supports the requirement to extract key phrases from documents. Azure AI Search's built-in Key Phrase Extraction skill leverages natural language processing to analyze text and return a list of key phrases, making it the appropriate skill for this knowledge mining solution.

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Sentiment Analysis skill

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Language Detection skill

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Entity Recognition skill

Question 16

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.. Option C is correct because 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 17

A company uses Azure Cognitive Search to index customer support emails. They need to implement a custom skill that extracts the sentiment of the email body and also identifies the primary product mentioned. The custom skill is a Python function deployed as an Azure Function. They want to ensure the skill can process multiple documents concurrently and handle errors gracefully. Which THREE configurations should they apply?

Accepted Answer

Set the inputs of the skill to include the 'text' field from the enriched document and the 'product' field from a previous skill.. Option B is correct because the custom skill must receive the email body text for sentiment analysis and the product field from a prior skill (e.g., a key phrase extraction or entity recognition skill) to identify the primary product. This ensures the Azure Function has all required inputs to produce the desired outputs (sentiment score and product name).

Answer

Set continueOnError to false to ensure that if the skill fails for one document, the entire indexer run stops.

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Set the context property to '/document/pages/*' to process each page of the email individually.

Question 18

Which THREE actions should an engineer take when deploying a custom question answering project in Azure Cognitive Service for Language?

Accepted Answer

Set up a multi-turn extraction policy for follow-up questions.. Option B is correct because multi-turn extraction is a core feature of custom question answering that allows the system to handle follow-up questions by maintaining context across turns. This is essential for conversational flows where a user's subsequent query depends on the previous answer, and it is configured via the project settings in Language Studio.

Answer

Integrate LUIS for intent detection.

Answer

Configure a single-turn extraction policy.

Question 19

A company is building a chatbot using Azure Cognitive Service for Language. They need to ensure that user utterances are correctly mapped to the appropriate intent in a custom question answering project. What should they configure?

Accepted Answer

Add synonyms and phrase list to the custom question answering project.. Option D is correct because adding synonyms and phrase lists to a custom question answering project directly improves the mapping of user utterances to intents by normalizing variations in phrasing. This configuration allows the project to recognize equivalent terms (e.g., 'cost' and 'price') as the same intent, ensuring accurate intent mapping without requiring exact matches.

Answer

Add synonyms and phrase list to the LUIS application.

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Add alternative question phrases to the QnA pairs in the custom question answering project.

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Define entities in the custom question answering project to capture key information.

Question 20

Which TWO actions should you take to optimize a custom text classification model in Azure Cognitive Service for Language?

Accepted Answer

Ensure that training examples for different labels do not have overlapping content.. Option A is correct because overlapping content between labels (e.g., the same text appearing in both 'positive' and 'negative' training examples) confuses the custom text classification model, leading to poor decision boundaries. Azure Cognitive Service for Language uses a multi-class or multi-label classifier that learns distinct patterns for each label; overlapping content introduces ambiguity, reducing precision and recall. Ensuring distinct, non-overlapping training examples per label helps the model learn clear, separable features.

Answer

Oversample the minority classes to balance the dataset.

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Remove all stop words from the training data.

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Remove examples with neutral sentiment to focus on positive and negative classes.