AI-900 Practice Test 39 — 15 Questions

Question 1

A company wants to build an FAQ bot that can answer questions based on its internal knowledge base. The questions from users are often phrased in different ways. They want to match the user's intent to pre-defined answers without training a custom model. Which Azure AI Language feature should they use?

Accepted Answer

Custom Question Answering. Custom Question Answering (formerly QnA Maker) is the correct choice because it allows you to ingest a knowledge base (e.g., FAQs, manuals) and match user questions to pre-defined answers using a built-in ranking model, without training a custom ML model. It handles varied phrasing through semantic understanding and returns the best answer from the curated content, directly addressing the requirement to match intent to pre-defined answers without custom training.

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Language Understanding (LUIS)

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Translator

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Sentiment Analysis

Question 2

A company wants to use Azure OpenAI Service to generate product descriptions. They need to ensure the model's output is based on their specific product catalog and pricing, not on generic information. Which approach should they use?

Accepted Answer

Implementing Retrieval Augmented Generation (RAG) with their catalog.. Option C is correct because Retrieval Augmented Generation (RAG) allows the model to dynamically retrieve relevant product catalog and pricing information from an external knowledge base at inference time, ensuring the generated descriptions are grounded in the company's specific data rather than relying on the model's generic training data. This approach avoids the need for costly fine-tuning and keeps the output up-to-date without retraining.

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Fine-tuning the model on their product catalog.

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Using few-shot learning with examples.

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Increasing the temperature parameter.

Question 3

A city deploys an AI system that automatically issues parking fines based on camera images. A citizen disputes a fine, claiming the system misidentified their car. The city cannot provide an explanation of how the system reached its decision because the model is too complex to interpret. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Transparency. The city cannot explain how the AI system reached its decision, which directly violates the transparency principle. Transparency requires that AI systems be understandable and that organizations provide meaningful explanations of their behavior, especially when decisions have legal or financial consequences. The inability to interpret the model's reasoning prevents the citizen from understanding or challenging the fine, undermining trust and accountability.

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Privacy and security

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Inclusiveness

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Reliability and safety

Question 4

A company uses Azure OpenAI Service to generate executive summaries of lengthy reports. The generated summaries sometimes include information that was not present in the original report, making them unreliable. Which Azure OpenAI Service feature should the company use to anchor the model to the provided report content?

Accepted Answer

Use the 'Add your data' feature (also known as 'Azure OpenAI on your data'). The 'Add your data' feature (Azure OpenAI on your data) allows the model to ground its responses in the specific content you provide, such as the original report. This prevents the model from generating information not present in the source, addressing the hallucination issue directly by restricting the model's knowledge base to the uploaded documents.

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Increase the temperature parameter

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Increase the frequency_penalty parameter

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Use the system message to instruct the model to only use provided content

Question 5

A customer support team wants to analyze chat logs to automatically identify the most common reasons for customer complaints and track how customer sentiment changes throughout a conversation. They plan to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?

Accepted Answer

Key phrase extraction and sentiment analysis. Key phrase extraction identifies the most common reasons for complaints by pulling out important terms from the chat logs, while sentiment analysis tracks how customer sentiment changes throughout a conversation by assigning positive, negative, or neutral scores per utterance. Both are prebuilt Azure AI Language features that require no custom training, making them the correct combination for this scenario.

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Entity recognition and language detection

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Text summarization and question answering

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Conversational language understanding and personal identification

Question 6

A customer support team wants to automatically extract the most important words or short phrases from each customer service ticket to understand common issues. Which Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct Azure AI Language feature because it is specifically designed to identify and return the most important words and short phrases from a document, such as a customer service ticket. This allows the support team to automatically surface common issues by analyzing the extracted key phrases across many tickets. The other options serve different purposes: named entity recognition identifies specific entities like people or organizations, sentiment analysis detects emotional tone, and language detection identifies the language of the text.

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Named entity recognition

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Sentiment analysis

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Language detection

Question 7

A company uses Azure OpenAI Service to generate marketing copy for a new product. They have a strict brand voice that requires formal, technical language and explicitly prohibits any humorous or informal phrases. They want to enforce these constraints without retraining the model. Which technique should they use?

Accepted Answer

B) Prompt engineering. Prompt engineering is correct because it allows the user to craft system messages or user prompts that explicitly instruct the model to use formal, technical language and avoid humor, all without modifying the underlying model weights. This technique leverages the model's instruction-following capability to enforce constraints at inference time, making it ideal for brand voice enforcement without retraining.

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A) Fine-tuning

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C) Reinforcement learning

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D) Transfer learning

Question 8

Which of the following scenarios is an example of a recommendation system AI workload?

Accepted Answer

A music streaming service suggesting new songs based on listening history. Option B is correct because a recommendation system AI workload analyzes user behavior (e.g., listening history) to predict and suggest new items (songs) that the user is likely to enjoy. This is a classic example of a collaborative filtering or content-based filtering model, which is a core AI workload under the 'Predictive' or 'Personalization' category.

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A spelling checker that identifies misspelled words in a document

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A database storing customer purchase history

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A barcode scanner at a checkout counter

Question 9

What is a common use case for AI-powered virtual assistants or chatbots in enterprise settings?

Accepted Answer

Automating first-line support by answering common questions 24/7. Option B is correct because AI-powered virtual assistants and chatbots are commonly deployed in enterprise settings to handle first-line support inquiries, such as FAQs, password resets, or order status checks, operating 24/7 without human intervention. This reduces the workload on human agents by automating routine, high-volume interactions, allowing them to focus on complex issues. The technology relies on natural language processing (NLP) and intent recognition to understand user queries and provide predefined or dynamically generated responses.

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Replacing all human customer service employees permanently

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Making autonomous business decisions without human oversight

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Monitoring employee productivity in real time

Question 10

What is image classification and how is it different from object detection?

Accepted Answer

Image classification labels the whole image; object detection finds and locates multiple objects within it. Image classification assigns a single label to an entire image based on its dominant content, such as 'cat' or 'dog'. Object detection goes further by not only identifying multiple objects within an image but also drawing bounding boxes around each one, providing both class labels and spatial locations. This distinction is fundamental in computer vision workloads on Azure, where Custom Vision and Computer Vision API offer separate capabilities for classification and detection tasks.

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Image classification is faster; object detection is slower but more accurate

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Image classification works on videos; object detection works on static images only

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They are the same task with different names

Question 11

A customer support team wants to analyze chat transcripts to automatically extract the most frequently mentioned product issues and also determine whether each chat represents a positive, neutral, or negative customer experience. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

A. Text Analytics (prebuilt). The Text Analytics (prebuilt) feature in Azure AI Language provides pre-built capabilities for key phrase extraction (to identify frequently mentioned product issues) and sentiment analysis (to classify chats as positive, neutral, or negative). This matches the customer support team's requirements exactly without needing custom training or complex configuration.

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B. Custom Text Classification

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C. Conversational Language Understanding

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D. Question Answering

Question 12

A beverage company uses a camera system to inspect bottles on a conveyor belt. The system must automatically identify which bottles are defective (e.g., cracked or chipped) and which are acceptable, based on the overall appearance of each bottle. The company has thousands of labeled images of bottles (defective and non-defective). Which Azure Computer Vision service should they use to train a custom model?

Accepted Answer

Custom Vision – Image classification. Option B is correct because the scenario requires classifying each bottle image into one of two categories (defective or acceptable) based on overall appearance. Custom Vision – Image classification is designed exactly for this: it trains a model on labeled images to predict a single label per image, making it ideal for binary or multi-class classification tasks like defect detection.

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Custom Vision – Object detection

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Optical Character Recognition (OCR)

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Face API

Question 13

What is Azure AI Language's text summarization capability used for?

Accepted Answer

Condensing long text into shorter summaries capturing the key information. Azure AI Language's text summarization capability is designed to condense long documents into shorter summaries that capture the key information. It uses extractive or abstractive summarization techniques to identify and present the most important sentences or generate new concise text, making it ideal for quickly digesting large volumes of content.

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Translating long documents into multiple languages

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Generating new creative text based on document themes

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Classifying documents into predefined business categories

Question 14

What is tokenization in the context of natural language processing?

Accepted Answer

Breaking text into smaller units (tokens) for processing by language models. Tokenization is the process of breaking text into smaller units called tokens, which can be words, subwords, or characters. This is a fundamental preprocessing step in natural language processing because language models like GPT or BERT operate on discrete tokens rather than raw text. Each token is mapped to an integer ID from a vocabulary, enabling the model to process and generate language mathematically.

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Encrypting text for secure transmission to AI services

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Assigning security access tokens to API calls

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Converting text into a numerical score for sentiment analysis

Question 15

What is the Azure AI Custom Vision portal used for?

Accepted Answer

Training and evaluating custom image classification and object detection models without code. The Azure AI Custom Vision portal is a no-code web interface that allows users to upload images, label them, and train custom image classification or object detection models. It abstracts away the underlying machine learning code, making it accessible for non-developers to build and evaluate computer vision models tailored to their specific use cases.

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Managing Azure subscription billing for AI services

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Building chatbots using natural language understanding

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Monitoring the health of deployed AI services