AI-900 Practice Test 6 — 10 Questions

Question 1

A customer support team wants to automatically analyze thousands of product reviews. Their goal is to extract the most frequently mentioned topics (e.g., 'battery life', 'customer service', 'screen quality') without manually reading each review. 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 automatically identifies the main points or topics (e.g., 'battery life', 'customer service', 'screen quality') from unstructured text. This directly meets the requirement to extract frequently mentioned topics from thousands of product reviews without manual reading.

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Sentiment analysis

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Language detection

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Entity recognition

Question 2

A customer support team receives thousands of unstructured chat transcripts every day. They want to automatically identify the most common recurring issues (e.g., 'long wait time', 'payment error', 'login problem') without training a custom model. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it automatically identifies the most salient topics or 'key phrases' from unstructured text without requiring any custom training. In this scenario, the recurring issues like 'long wait time' or 'payment error' are exactly the type of multi-word, topic-level phrases that key phrase extraction surfaces, making it the ideal prebuilt feature for summarizing common support topics.

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Named entity recognition (NER)

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Sentiment analysis

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Language detection

Question 3

A data scientist has a dataset containing images of handwritten digits (0-9) where each image is labeled with the correct digit. The goal is to train a model that can predict the digit from a new image. Which type of machine learning approach should be used?

Accepted Answer

Classification. This is a supervised learning problem where the model must predict a discrete class label (digit 0-9) from input images. Classification algorithms, such as logistic regression or neural networks, are designed to map inputs to categorical outputs, making B the correct choice.

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Regression

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Clustering

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Reinforcement learning

Question 4

A company uses a large language model to generate answers to employee questions about internal HR policies. However, the model sometimes produces answers that are factually incorrect or not based on the official policies. To reduce these inaccuracies, the company wants to provide the model with relevant, up-to-date policy documents as extra context before generating a response. Which technique is being applied?

Accepted Answer

Grounding with relevant data (RAG). The technique described is Retrieval-Augmented Generation (RAG), which retrieves relevant, up-to-date policy documents from an external knowledge base and provides them as context to the large language model before generating a response. This grounds the model's output in verified data, reducing factual inaccuracies without modifying the model itself. Option C is correct because RAG directly addresses the need to supply extra context from authoritative sources.

Answer

Prompt engineering only

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Fine-tuning the model on policy documents

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Using a content filter

Question 5

A customer service team wants to build an Azure AI-powered bot that can understand the intent behind customer messages. For example, the bot should recognize that 'I want to return my shoes' maps to a 'ReturnItem' intent, and 'Where is my order?' maps to 'TrackOrder'. Which Azure service provides pre-built models specifically for intent recognition?

Accepted Answer

Language Understanding (LUIS). Language Understanding (LUIS) is the correct Azure service because it provides pre-built models and custom capabilities specifically designed for intent recognition and entity extraction from natural language utterances. The scenario requires mapping customer messages like 'I want to return my shoes' to a 'ReturnItem' intent, which is exactly the core function of LUIS—it analyzes user input to identify the user's goal (intent) and any relevant details (entities).

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Text Analytics

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Translator Text

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Speech-to-text

Question 6

What is the 'bias-variance tradeoff' in machine learning?

Accepted Answer

The tradeoff between underfitting (high bias) and overfitting (high variance) when choosing model complexity. Option B is correct because the bias-variance tradeoff describes the inverse relationship between underfitting (high bias, where the model is too simple to capture patterns) and overfitting (high variance, where the model is too complex and captures noise). In Azure Machine Learning, this tradeoff is managed by tuning hyperparameters like regularization strength or tree depth to balance model complexity and generalization.

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The tradeoff between model accuracy and inference speed

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The tradeoff between training data quantity and model quality

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The difference in fairness metrics between biased and unbiased model versions

Question 7

A customer service team wants to analyze thousands of support tickets to automatically categorize them into predefined topics like 'billing', 'technical issue', and 'account management'. They have a small set of labeled tickets for each category. Which Azure AI Language feature should they use?

Accepted Answer

C. Option C is correct because the Custom Text Classification feature of Azure AI Language allows you to train a model using a small set of labeled tickets to automatically categorize text into predefined topics like 'billing', 'technical issue', and 'account management'. This feature is specifically designed for scenarios where you have labeled data and need to classify documents into custom categories, making it ideal for analyzing support tickets.

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A

Answer

B

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D

Question 8

A customer support team uses an AI chatbot to analyze incoming messages. They want to automatically identify the most frequently mentioned topics, such as 'shipping delay', 'refund policy', and 'product quality', without manually reading each message. Which Azure AI Language feature should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct choice because it automatically identifies the main topics and concepts in text, such as 'shipping delay', 'refund policy', and 'product quality', without requiring manual reading. This feature returns a list of key phrases that represent the most salient points in the input, making it ideal for topic frequency analysis in customer support messages.

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Language Detection

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Sentiment Analysis

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Entity Recognition

Question 9

A customer support team wants to create a chatbot that can answer common questions about employee benefits. They have a PDF document containing a list of frequently asked questions with their answers. Which Azure AI Language feature should they use to build a solution that extracts answers directly from this content?

Accepted Answer

Custom Question Answering. Custom Question Answering (C) is the correct choice because it is specifically designed to ingest documents like PDFs and extract question-answer pairs from them, enabling a chatbot to respond directly with answers from the content. This feature uses a pre-built or custom knowledge base to match user queries to the most relevant answer, making it ideal for the described scenario.

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Sentiment Analysis

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Key Phrase Extraction

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Text Analytics for Health

Question 10

A bank deploys an AI system that uses a deep neural network to approve personal loan applications. A customer whose loan was rejected requests a detailed explanation of why the decision was made. The bank's AI team realizes that the model's internal workings are too complex to provide a simple, understandable reason. According to Microsoft's responsible AI principles, which principle is most directly violated by this situation?

Accepted Answer

Transparency. The bank's inability to provide a clear, understandable explanation for the AI's loan decision directly violates the transparency principle. Microsoft's responsible AI principles require that AI systems be understandable and that their decisions can be explained to users, especially when those decisions have significant impact. A deep neural network's complex, non-linear decision boundaries and lack of inherent interpretability make it a 'black box,' which undermines the required transparency.

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Fairness

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Reliability & Safety

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Privacy & Security