AI-900 Practice Test 43 — 15 Questions

Question 1

A company's HR department wants to create a self-service bot that can answer employee questions about company policies. They have a collection of policy documents in PDF format. Which Azure AI Language feature should they use to ingest these documents and enable the bot to provide answers based on them?

Accepted Answer

Custom Question Answering. Custom Question Answering (CQA) is the correct choice because it is specifically designed to ingest documents (including PDFs) and build a knowledge base of question-answer pairs. The bot can then query this knowledge base to provide answers based on the policy documents, using the underlying Azure Cognitive Search and language models to match user questions to the most relevant content.

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Sentiment Analysis

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Key Phrase Extraction

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Language Detection

Question 2

A company deploys an AI system to screen job applications and recommend candidates for interviews. The system consistently rates male candidates higher than equally qualified female candidates. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Fairness. The AI system's consistent rating of male candidates higher than equally qualified female candidates demonstrates a clear bias in outcomes based on gender, which directly violates the Fairness principle. Fairness in responsible AI requires that AI systems treat all people equitably, avoiding discrimination based on sensitive attributes such as gender, race, or age. This bias likely stems from biased training data or flawed feature engineering that encodes historical hiring disparities.

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Reliability and safety

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Privacy and security

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Inclusiveness

Question 3

A bank deploys an AI system that uses a complex deep learning model to approve or reject loan applications. When a loan is rejected, customers demand to know the specific reasons. The bank wants to ensure the AI system operates in a way that allows them to explain its decisions. Which Microsoft responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Transparency. The bank's requirement to explain why a loan was rejected directly aligns with the transparency principle, which mandates that AI systems be understandable and that their decisions can be communicated to users. In this scenario, the complex deep learning model must be interpretable, often through techniques like feature importance analysis or surrogate models, to provide specific reasons for rejection. Transparency ensures that customers can receive meaningful explanations, building trust and enabling accountability.

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Reliability and safety

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Privacy and security

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Fairness

Question 4

A company wants to build a chatbot that answers customer questions using a large language model. The company has an extensive internal knowledge base with accurate, up-to-date product information. To ensure the chatbot's answers are based on this reliable source rather than the model's internal knowledge, which technique should they use?

Accepted Answer

Grounding with retrieval-augmented generation. Option C is correct because grounding with retrieval-augmented generation (RAG) retrieves relevant, up-to-date chunks from the internal knowledge base and provides them as context to the large language model (LLM) at inference time. This ensures the chatbot's answers are factually based on the company's reliable source rather than relying on the model's potentially outdated or incorrect parametric memory.

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Fine-tuning the model on the knowledge base

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Zero-shot learning

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Prompt engineering with few-shot examples

Question 5

A data scientist is training a regression model to predict energy consumption. The dataset includes features like temperature, humidity, time of day, and day of week. After training, the model performs well on the training set but poorly on new data. Which approach would most likely help reduce this problem?

Accepted Answer

Use a simpler model with fewer parameters.. The model performs well on the training set but poorly on new data, which is classic overfitting. Using a simpler model with fewer parameters reduces the model's capacity to memorize noise and irrelevant patterns, forcing it to learn the underlying generalizable relationships. This directly addresses the variance problem without requiring additional data or computational resources.

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Add more features to the model.

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Increase the number of training epochs.

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Use a more complex model to capture more patterns.

Question 6

A company is developing a chatbot that can both answer customer questions in natural language and create images on demand (e.g., 'Generate a picture of a product prototype'). Which combination of Azure generative AI models should they integrate?

Accepted Answer

A. GPT-4 for text and DALL-E for images. Option A is correct because GPT-4 is a generative AI model optimized for natural language understanding and generation, making it ideal for answering customer questions in a conversational manner. DALL-E is a generative AI model specifically designed to create images from textual descriptions, enabling the chatbot to generate product prototypes on demand. Together, they cover both text and image generation requirements.

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B. GPT-3 for text and Custom Vision for images

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C. BERT for text and OCR for images

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D. Language Understanding (LUIS) and Face API

Question 7

A data scientist is building a model to predict the exact temperature in degrees Celsius based on humidity and atmospheric pressure. The model will output a single numeric value for each input. Which type of machine learning task is this?

Accepted Answer

Regression. This is a regression task because the goal is to predict a continuous numeric value (temperature in degrees Celsius) from input features (humidity and atmospheric pressure). Regression models output a real number, unlike classification which predicts discrete categories. In Azure Machine Learning, regression algorithms like Linear Regression or Decision Forest Regression are used for such tasks.

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Classification

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Clustering

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Object detection

Question 8

What is Azure AI Search (formerly Cognitive Search) and how does it relate to generative AI?

Accepted Answer

An enterprise search service used in RAG to retrieve relevant documents for LLM context. Azure AI Search is an enterprise search service that indexes and retrieves relevant documents from your own data sources. In the context of generative AI, it is a core component of the Retrieval Augmented Generation (RAG) pattern, where it provides the LLM with up-to-date, domain-specific context to ground its responses, preventing hallucinations and ensuring factual accuracy.

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A service that generates answers using only the language model's built-in training knowledge

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A tool for searching through Azure OpenAI model configurations

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A database service for storing generated AI content

Question 9

A bank deploys an AI system to automatically approve or reject loan applications. After six months, an audit reveals that the system approves loans at a significantly lower rate for applicants from a specific ethnic group compared to other groups with similar financial profiles. Which Microsoft responsible AI principle is most directly violated by this outcome?

Accepted Answer

Fairness. The AI system's approval rate disparity for a specific ethnic group, despite similar financial profiles, directly violates the Fairness principle. Fairness requires that AI systems treat all groups equitably and avoid discrimination based on sensitive attributes like ethnicity. This outcome demonstrates a lack of fairness in the model's decision-making process.

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Transparency

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Privacy

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Reliability

Question 10

What is named entity recognition (NER) and provide an example of its output?

Accepted Answer

NER identifies and categorizes named entities like people, organizations, locations, and dates in text. Named entity recognition (NER) is a natural language processing (NLP) capability that identifies and classifies key elements in text into predefined categories such as person names, organizations, locations, dates, and quantities. Option B correctly describes this function, and its output typically includes the extracted entity along with its category label, for example, {'entity': 'Microsoft', 'category': 'Organization'}.

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NER identifies grammatical parts of speech like nouns and verbs

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NER generates new names for products based on brand guidelines

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NER converts names into anonymous placeholders for privacy

Question 11

A city government deploys an AI system that automatically detects traffic violations (e.g., running red lights) from traffic camera footage. The system triggers fines without immediate human review. According to Microsoft's responsible AI principles, which principle is most directly concerned with ensuring there is human oversight and that the organization can be held liable for the system's decisions?

Accepted Answer

Accountability. The Accountability principle in Microsoft's responsible AI framework requires that organizations take ownership of AI system outcomes and ensure human oversight. In this scenario, the system automatically issues fines without human review, which directly challenges accountability because there is no mechanism for human intervention or liability assignment. This principle mandates that the organization must be able to answer for the system's decisions, including errors or biases.

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Transparency

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Reliability and Safety

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Privacy and Security

Question 12

A company uses Azure OpenAI Service to power an AI assistant that helps customers with product troubleshooting. The assistant must maintain the conversation history to provide contextually relevant answers across multiple turns. Which API endpoint should be used for this purpose?

Accepted Answer

Chat Completions API. The Chat Completions API is designed for multi-turn conversational scenarios because it accepts a list of messages with roles (system, user, assistant) that represent the conversation history. This allows the model to maintain context across multiple interactions, making it the correct choice for an AI assistant that needs to provide contextually relevant answers over several turns.

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Completions API

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Embeddings API

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Fine-tuning

Question 13

What is the primary difference between supervised and unsupervised machine learning?

Accepted Answer

Supervised uses labeled training data; unsupervised finds patterns in unlabeled data. The primary difference between supervised and unsupervised machine learning is that supervised learning uses labeled training data to learn a mapping from inputs to outputs, while unsupervised learning finds hidden patterns or structures in unlabeled data without predefined labels. This distinction is fundamental to choosing the right approach for a given problem, such as classification (supervised) versus clustering (unsupervised).

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Supervised learning requires human oversight; unsupervised runs automatically

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Supervised is for images; unsupervised is for text

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Supervised is older and less accurate than unsupervised

Question 14

A company wants to automatically categorize support tickets into categories such as 'Billing', 'Technical Issue', and 'Account Management'. They have a set of 1,000 labeled tickets that they can use to train a model. Which Azure AI Language feature should they use?

Accepted Answer

Custom text classification. Custom text classification is the correct choice because it allows you to train a model on your own labeled dataset (1,000 tickets) to categorize text into custom-defined classes like 'Billing', 'Technical Issue', and 'Account Management'. This feature is specifically designed for scenarios where you need to classify documents into user-defined categories, unlike pre-built classification options.

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Key phrase extraction

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Sentiment analysis

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Language detection

Question 15

What is the difference between Azure AI Vision and Azure AI Custom Vision?

Accepted Answer

Azure AI Vision offers pre-built models; Custom Vision trains custom models on your labeled images. Azure AI Vision provides pre-built, ready-to-use models for common computer vision tasks like image analysis, OCR, and facial recognition, requiring no training data. Azure AI Custom Vision, on the other hand, allows you to train custom models using your own labeled images to solve specific classification or object detection problems. This distinction makes option B correct because it accurately captures the core difference between a pre-built service and a customizable training platform.

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Azure AI Vision is faster; Custom Vision is more accurate

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Azure AI Vision analyzes only photos; Custom Vision analyzes documents

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They are different names for the same service