AI-900 Practice Test 36 — 15 Questions

Question 1

A company wants to use Azure OpenAI to generate realistic customer conversations for training a chatbot. They have a set of example conversation snippets and want the model to mimic the style and structure of those examples. The company does not want to retrain the model. Which approach should they use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use prompt engineering with few-shot examples in the prompt. Prompt engineering with few-shot examples allows the model to mimic the style and structure of provided conversation snippets without retraining. By including a few example conversations in the prompt, the model learns the desired pattern through in-context learning, leveraging its pre-trained capabilities to generate realistic customer conversations.

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Fine-tune the model on the conversation dataset

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Use DALL-E to generate the conversations

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Apply a content filter to restrict the output style

Question 2

What is the purpose of Azure AI Language Studio?

Accepted Answer

A web-based UI for exploring and building NLP solutions without code. Azure AI Language Studio is a web-based user interface that allows users to explore, build, and manage natural language processing (NLP) solutions without writing code. It provides pre-built and customizable features such as sentiment analysis, key phrase extraction, language detection, and conversational language understanding, enabling rapid prototyping and integration of NLP capabilities into applications.

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A tool for training custom computer vision models

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A platform for managing Azure AI service billing

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A code editor for writing Python ML scripts

Question 3

A data scientist is training a model to predict whether a customer will purchase a product (Yes/No). The dataset contains 90% 'No' and 10% 'Yes'. After training, the model achieves 90% accuracy. Which evaluation metric would be more informative to assess the model's performance on the minority class?

Accepted Answer

F1 score. In this imbalanced dataset (90% 'No', 10% 'Yes'), a model that always predicts 'No' would achieve 90% accuracy, making accuracy a misleading metric. The F1 score is the harmonic mean of precision and recall, specifically designed to evaluate a model's performance on the minority class by balancing false positives and false negatives. It is the most informative metric here because it directly measures how well the model identifies the rare 'Yes' purchases without being inflated by the majority class.

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Mean Absolute Error (MAE)

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Area Under the Curve (AUC)

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R-squared

Question 4

What is 'Azure Machine Learning's job submission' and what types of training jobs are supported?

Accepted Answer

Submitting training scripts to managed compute — command, sweep, pipeline, and AutoML job types. Azure Machine Learning's job submission is the process of sending a training script to a managed compute target for execution. The supported job types are command (running a script), sweep (hyperparameter tuning), pipeline (multi-step workflows), and AutoML (automated model selection and training). This makes option B correct because it accurately lists these four job types.

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Submitting applications to join the Azure AI Engineering team at Microsoft

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Submitting model predictions as batch jobs to process large datasets overnight

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Scheduling when model monitoring jobs run to check for data drift

Question 5

What is Azure AI Studio?

Accepted Answer

A unified platform for building, evaluating, and deploying generative AI applications. Azure AI Studio is a unified platform designed specifically for building, evaluating, and deploying generative AI applications. It integrates tools for prompt engineering, model fine-tuning, and safety evaluation, enabling developers to create custom AI solutions using large language models (LLMs) from Azure OpenAI Service and other sources. This makes option B correct as it directly describes the platform's core purpose.

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A video editing tool powered by AI for content creators

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A specialized IDE for writing Python machine learning code only

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A database service for storing conversation history from AI applications

Question 6

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.

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Prompt engineering only

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Fine-tuning the model on policy documents

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Using a content filter

Question 7

What is 'pose estimation' in computer vision and what is it used for?

Accepted Answer

Detecting body keypoint positions (joints) in images to infer posture and movement. Pose estimation is a computer vision technique that detects and localizes keypoints (joints) on a human body in an image or video. These keypoints, such as shoulders, elbows, wrists, hips, and knees, are used to infer the body's posture, orientation, and movement. Option B correctly describes this process of detecting body keypoint positions to infer posture and movement.

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Estimating the correct posture for employees based on ergonomics guidelines

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Determining the camera angle and position used to capture a photograph

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Classifying whether a person is sitting or standing in an image

Question 8

A financial services company uses an AI system to detect fraudulent credit card transactions. After deployment, the system incorrectly flags a significant number of legitimate transactions as fraudulent, causing customer dissatisfaction. The company wants to reduce these false positives while still catching most fraudulent transactions. Which Microsoft responsible AI principle should guide their redesign of the system?

Accepted Answer

Reliability and safety. The Reliability and safety principle emphasizes that AI systems should perform reliably, safely, and consistently under normal conditions. In this scenario, the high rate of false positives indicates the system is not operating reliably for legitimate transactions, causing customer harm. Redesigning to reduce false positives while maintaining fraud detection aligns directly with improving the system's reliability and safety for end users.

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Fairness

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Transparency

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Privacy and security

Question 9

Drag and drop the steps to implement content moderation using Azure Content Moderator into the correct order.

Accepted Answer

Create an Azure Content Moderator resource, submit content for moderation, retrieve moderation results, then take action based on the results.. Content moderation involves setting up the resource, submitting content, reviewing results, and acting.

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Submit content for moderation, create an Azure Content Moderator resource, retrieve moderation results, then take action based on the results.

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Create an Azure Content Moderator resource, retrieve moderation results, submit content for moderation, then take action based on the results.

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Take action based on the results, create an Azure Content Moderator resource, submit content for moderation, retrieve moderation results.

Question 10

What is 'model versioning' and why is it essential in MLOps?

Accepted Answer

Tracking each iteration of a trained model for rollback, A/B testing, auditing, and reproducibility. Model versioning is the practice of tracking each iteration of a trained model, including its hyperparameters, training data snapshot, and evaluation metrics. In MLOps, it is essential because it enables rollback to a previous model if a new version performs poorly, supports A/B testing by comparing multiple model versions in production, provides an audit trail for compliance, and ensures reproducibility by capturing the exact code, data, and environment used to train each version.

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Updating the Python version used to run ML training scripts

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Releasing new features of the Azure ML service as versioned API updates

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Managing multiple versions of training data used by different model experiments

Question 11

A retail company wants to use security cameras to automatically detect when products are removed from shelves. They need to identify the specific product type (e.g., a cereal box, a soda can) and count how many units are taken. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it can both locate objects within an image (via bounding boxes) and classify them into specific categories (e.g., cereal box, soda can). This allows the system to identify the product type and count the number of units removed from shelves, which aligns directly with the requirement.

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Optical Character Recognition (OCR)

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Image tagging

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Face detection

Question 12

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 13

Match each Azure AI workload to its responsible AI principle.

Accepted Answer

Computer Vision: Ensures AI systems treat all people fairly, avoiding bias in image analysis.. Responsible AI principles guide ethical development. Computer Vision relates to fairness, NLP to inclusiveness, Speech to privacy and security, and Decision to transparency. Common confusions arise from swapping workload-specific principles.

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Computer Vision: Protects user privacy by safeguarding voice data.

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Speech: Ensures AI systems treat all people fairly, avoiding bias in image analysis.

Question 14

A data scientist trains a classification model to predict whether an email is spam or not. The model achieves 98% accuracy on the test set, but upon inspection, it classifies all emails as 'not spam' because the dataset has 95% non-spam emails. What is the most likely issue?

Accepted Answer

Data imbalance. The model achieves 98% accuracy by simply predicting all emails as 'not spam', which reflects the 95% majority class in the dataset. This is a classic symptom of class imbalance, where the model learns to exploit the skewed distribution rather than learning meaningful patterns to distinguish spam from non-spam. In Azure Machine Learning, techniques like SMOTE or stratified sampling are used to mitigate this issue.

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Overfitting

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Underfitting

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Feature scaling error

Question 15

A data scientist wants to train a machine learning model to predict the exact market price of a house based on features such as square footage, number of bedrooms, and location. Which type of machine learning task should be used?

Accepted Answer

Regression. Predicting the exact market price of a house is a regression task because the target variable (price) is a continuous numeric value. Regression algorithms, such as linear regression or decision tree regression, learn the relationship between input features (e.g., square footage, bedrooms, location) and a continuous output. In Azure Machine Learning, you would select a regression model from the designer or AutoML to solve this problem.

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Classification

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Clustering

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Anomaly Detection