AI-102 Practice Test 34 — 15 Questions

Question 1

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 is used to set the behavior and context of the AI model, including specifying the desired output format or language. By setting the system message to 'You are a helpful assistant that always writes code in Python', the developer can instruct the model to generate Python code consistently. This parameter is part of the chat completions API and directly influences the model's persona and constraints.

Answer

temperature

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top_p

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max_tokens

Question 2

A company wants to generate personalized product descriptions for its e-commerce site using Azure OpenAI. They need to ensure the model's output adheres to brand guidelines and does not generate prohibited content. Which approach should they use?

Accepted Answer

Use a system message with brand guidelines and apply content filtering.. Using a system message allows you to embed brand guidelines directly into the conversation context, instructing the model on tone, style, and prohibited content. Azure OpenAI's content filtering provides an additional safety layer by automatically detecting and blocking harmful or policy-violating outputs, ensuring compliance with both brand and regulatory requirements.

Answer

Use prompt engineering with negative prompts and ignore content filtering.

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Provide few-shot examples in the user message and rely on the model's training.

Answer

Fine-tune the model with brand guidelines and disable content filtering for performance.

Question 3

You are implementing a knowledge mining solution using Azure AI Search with a custom skillset. The custom skill is an Azure Function that enriches documents with additional metadata. You need to ensure that the custom skill receives the entire document content as input. How should you configure the skill's context and inputs?

Accepted Answer

Set context to '/document' and input source to '/document/content'.. To pass the entire document content as input to a custom skill, the skill's context should be set to '/document', which allows the skill to run once per document. The input source should be '/document/content' to reference the entire content field of the document. Options A and B set context to '/document/content', which runs the skill on each content node individually, not the whole document. Option C uses '/document/normalized_images/*' as input, which is for images, not the content. Therefore, option D is correct.

Answer

Set context to '/document/content' and input source to '/document/metadata'.

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Set context to '/document/content' and input source to '/document/content'.

Answer

Set context to '/document' and input source to '/document/normalized_images/*'.

Question 4

You have the above Azure AI Search indexer definition. The indexer runs successfully but the 'content' field in the index is empty for all documents. What is the likely cause?

Accepted Answer

The outputFieldMapping references '/document/content' which is not produced by the skillset.. The indexer runs successfully but the 'content' field is empty because the outputFieldMapping maps a path '/document/content' that does not exist in the enrichment tree produced by the skillset. The skillset must output a node at that path; if it doesn't, the mapping cannot populate the field. Option A correctly identifies this as the likely cause.

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The indexer schedule is too frequent.

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The fieldMapping for 'metadata_storage_path' is incorrect.

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The batchSize is too large, causing some items to fail silently.

Question 5

You are building a solution to extract key information from scanned invoices. The invoices are in PDF format and contain both printed and handwritten fields. Which Azure AI service should you use?

Accepted Answer

Azure AI Document Intelligence (formerly Form Recognizer). Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract text, key-value pairs, and tables from documents like scanned invoices, and it supports both printed and handwritten text. Option A (Language Service) is used for text analytics and NLP, not structured document extraction. Option B (Speech Service) handles audio transcription and speech, not documents. Option C (Computer Vision) can extract text via OCR but is less specialized than Document Intelligence for invoice-specific extraction.

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Language Service

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Speech Service

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Computer Vision

Question 6

Refer to the exhibit. You execute a search query on an Azure AI Search index and get these results. The query was 'brown fox'. Why is the first result scored higher than the second?

Accepted Answer

The first document has a higher term frequency and better term proximity for the query terms. The default scoring algorithm in Azure AI Search uses term frequency (TF) and term proximity. The first document likely has a higher term frequency for 'brown' and 'fox', and the terms are closer together (e.g., appearing as a phrase), resulting in a higher score. Option D correctly identifies this. Option A is incorrect because no scoring profile is mentioned. Option B is incorrect because vector search is not used. Option C is incorrect because no semantic ranking is configured.

Answer

The first document has a higher value in a scoring profile field

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The first document is more similar to the query in vector space

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The first document was boosted by a semantic ranking function

Question 7

You are deploying an Azure AI Search solution that indexes medical research papers. The papers contain sensitive patient data that must be de-identified before indexing. You need to use Azure AI Services to detect and redact personal information. Which combination of skills should you include in a skillset?

Accepted Answer

PII detection skill. The PII detection skill in Azure AI Search uses Azure AI Services to identify and redact personally identifiable information from text, which is exactly what is needed to de-identify sensitive patient data before indexing. The other options are incorrect: A) Custom Entity Lookup skill requires a predefined list and does not perform redaction, and Sentiment skill is irrelevant; C) Text Translation skill translates text, and Entity Recognition skill identifies entities but does not redact them; D) Entity Recognition skill identifies entities but does not redact, and Key Phrase Extraction skill extracts key phrases, neither of which de-identify data.

Answer

Custom Entity Lookup skill and Sentiment skill

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Text Translation skill and Entity Recognition skill

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Entity Recognition skill and Key Phrase Extraction skill

Question 8

Which TWO Azure AI services are most appropriate for extracting text from images and recognizing handwritten text?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (Option A) provides document analysis and extraction capabilities, including OCR and handwriting recognition. Azure AI Vision (Option C) offers the Read API for extracting printed and handwritten text from images. Azure AI Speech (Option B) is for speech-to-text, not image OCR. Azure AI Search (Option D) is for indexing and searching data, not text extraction. Azure AI Language (Option E) provides NLP capabilities, not OCR.

Answer

Azure AI Speech

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Azure AI Search

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Azure AI Language

Question 9

You want to use the Azure AI Language service to summarize long customer support conversations into a short summary. Which feature should you use?

Accepted Answer

Conversational Summarization. Conversational Summarization is the correct feature because it is specifically designed to condense multi-turn dialogues, such as customer support conversations, into concise summaries. Unlike generic text summarization, it understands the conversational flow, speaker turns, and context to produce a coherent summary of the interaction.

Answer

Sentiment Analysis

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Entity Extraction

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Key Phrase Extraction

Question 10

You are a solution architect at a news agency. The agency publishes thousands of articles daily. You need to build a knowledge mining solution that enables journalists to search for articles by topic, sentiment, key people, and locations mentioned. The articles are stored as HTML files in Azure Blob Storage. The solution must also provide a summary for each article. You plan to use Azure AI Search with cognitive skills and Azure OpenAI. Which combination of skills and features should you include to meet all requirements with the best performance and accuracy?

Accepted Answer

Skillset with Entity Recognition skill, Sentiment skill, and Key Phrase Extraction skill. Use Azure OpenAI service to generate summaries via a custom skill that calls the GPT model. Enable semantic search.. It combines Entity Recognition (for people/locations), Sentiment (for sentiment), Key Phrase Extraction (for topics), and a custom skill using Azure OpenAI for summarization, with semantic search for optimal performance. Option A is incorrect: Azure AI Document Intelligence is designed for documents like PDFs and images, not HTML; using Azure AI Language for entities and sentiment is okay but alone lacks key phrase extraction for topics and summarization. Option B is incorrect: it includes Text Translation skill, which is not needed, and lacks Key Phrase Extraction for topics and summarization. Option D is incorrect: Text Analytics for Health is specialized for medical terms, which is irrelevant, and the skill set lacks Key Phrase Extraction for topics.

Answer

Use Azure AI Document Intelligence to extract content from HTML, then use Azure AI Language to extract entities and sentiment. Index in Azure AI Search with semantic search.

Answer

Skillset with Entity Recognition skill, Sentiment skill, Key Phrase Extraction skill, and Text Translation skill. Enable semantic search.

Answer

Skillset with Entity Recognition skill, Sentiment skill, and Text Analytics for Health skill to extract medical terms. Use Azure OpenAI for summarization as a custom skill.

Question 11

You are building a knowledge mining solution for a financial services company that needs to extract key financial terms (e.g., revenue, EBITDA, net income) from annual reports in PDF format. The solution must use a custom skill that runs a Python script to perform the extraction. The Python script is deployed as an Azure Function. You have added the custom skill to the skillset and tested it with a small set of documents. However, when processing the full dataset, the custom skill fails with time-out errors. The Azure Function has a default timeout of 230 seconds. What should you do to resolve the issue without changing the extraction logic?

Accepted Answer

Change the Azure Function to a Premium plan and increase the function timeout.. The custom skill fails with time-out errors because the Azure Function's default timeout of 230 seconds is too short for processing large documents. Option D is correct because upgrading to a Premium plan allows you to increase the function timeout (up to 30 minutes with Premium plan, or unlimited with Dedicated plan). Option A is wrong because the indexer's batch size controls how many documents are processed simultaneously, but the timeout is per document (or per skill execution), so smaller batches won't solve the per-document timeout issue. Option B is wrong because Document Intelligence custom extraction models are pre-built for common forms and are not suitable for custom Python extraction logic. Option C is wrong because splitting the skillset does not change the execution time of the individual skill; the same timeout per invocation would still apply.

Answer

Configure the indexer to process documents in smaller batches.

Answer

Replace the custom skill with a Document Intelligence custom extraction model.

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Split the skillset into multiple skillsets and run them sequentially.

Question 12

You are designing a knowledge mining solution that must extract entities from scanned handwritten forms. The forms contain signatures and checkboxes. Which combination of Azure AI services should you recommend?

Accepted Answer

Azure AI Document Intelligence with a custom neural model and Azure AI Language for entity linking. The scenario requires extracting entities from scanned handwritten forms, which demands a custom neural model in Azure AI Document Intelligence to handle the variability of handwriting, signatures, and checkboxes. Azure AI Language's entity linking then enriches the extracted entities by disambiguating and linking them to a knowledge base, providing structured, meaningful output. The premade models in Document Intelligence are designed for printed text and common layouts, not handwritten content, making a custom neural model essential.

Answer

Azure AI Document Intelligence with a premade model and Azure AI Computer Vision

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Azure AI Computer Vision (OCR) and Azure AI Search with integrated vectorization

Answer

Azure Cognitive Search and Azure AI Document Intelligence with a premade model

Question 13

You need to translate a document from English to Spanish using Azure OpenAI. Which parameter should you include in the prompt to specify the target language?

Accepted Answer

user message. In Azure OpenAI, the user message is where you provide the instruction or context for the model, including specifying the target language for translation. By including 'Translate the following English text to Spanish' in the user message, you direct the model to perform the language translation task. The other parameters control generation behavior (temperature, max_tokens) or set the model's persona (system message), but do not specify the target language.

Answer

temperature

Answer

system message

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max_tokens

Question 14

You are deploying a conversational AI chatbot using Azure AI Language service. The chatbot must be able to switch between multiple intents in a single conversation without restarting the session. Which feature should you enable?

Accepted Answer

Orchestration workflow. Orchestration workflow in Azure AI Language service allows a chatbot to switch between multiple intents within a single conversation by routing requests to different language models or custom question answering knowledge bases. This enables the chatbot to handle diverse intents seamlessly without restarting the session, as each intent can be processed by the most appropriate component.

Answer

Active learning

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Dynamic entity extraction

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Prebuilt domain components

Question 15

An application uses Azure AI Vision to analyze images and extract text. The application crashes when processing images with embedded barcodes. You suspect the issue is related to the image pre-processing. Which step should you add to the pipeline to resolve the issue?

Accepted Answer

Detect and remove barcodes from the image before OCR. Azure AI Vision's OCR engine is designed to extract text from natural images and documents, but embedded barcodes can introduce noise or unexpected patterns that interfere with the text detection algorithm. By detecting and removing barcodes from the image before OCR, you eliminate this interference, allowing the OCR engine to focus on textual content without crashing or producing erroneous results.

Answer

Increase the image contrast before sending to the OCR engine

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Add more training data with barcodes to the OCR model

Answer

Resize the image to a smaller resolution to reduce barcode impact