AI-900 Practice Test 33 — 15 Questions

Question 1

An e-commerce company has a dataset of customer purchase histories with no predefined categories. The data analyst wants to identify natural groupings of customers based on their purchasing behavior to target marketing campaigns. Which type of machine learning should the analyst use?

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

Accepted Answer

Clustering. Clustering is the correct choice because it is an unsupervised learning technique used to discover inherent groupings in data without predefined labels. In this scenario, the analyst wants to identify natural customer segments based on purchase behavior, which aligns perfectly with clustering algorithms like K-Means or DBSCAN that partition data into clusters of similar patterns.

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Regression

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Classification

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Reinforcement learning

Question 2

What is 'retail intelligence' using computer vision and what business value does it provide?

Accepted Answer

Using store video to analyse traffic flow, dwell time, queue length, and planogram compliance. Retail intelligence using computer vision involves analyzing video feeds from in-store cameras to extract actionable insights such as customer traffic flow, dwell time at shelves, queue lengths, and planogram compliance. This is a classic computer vision workload on Azure, often implemented using Azure Video Indexer or Custom Vision, which processes visual data rather than transactional or textual data.

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AI that recommends products to online shoppers based on browsing history

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An AI system that processes retail POS transaction data to forecast sales

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Sentiment analysis of customer reviews from retail websites to improve products

Question 3

What is artificial intelligence (AI) in the context of computer science?

Accepted Answer

Software that enables machines to simulate human intelligence and learn from data. Artificial intelligence (AI) in computer science refers to software systems that can perform tasks typically requiring human intelligence, such as learning from data, reasoning, and decision-making. This definition encompasses machine learning, deep learning, and other subfields where models are trained on data to improve performance over time, rather than following explicitly programmed rules.

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A type of computer hardware that processes data faster than traditional CPUs

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A programming language used to write algorithms

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A type of database that stores structured information

Question 4

A data scientist trains a regression model to predict house prices. The model has a mean absolute error (MAE) of $5,000 on the test set. Which statement best interprets this metric?

Accepted Answer

On average, the model's predictions are $5,000 away from the actual prices.. Mean Absolute Error (MAE) measures the average absolute difference between predicted and actual values. An MAE of $5,000 means that, on average, each prediction deviates from the true house price by $5,000. This is a standard interpretation of MAE in regression metrics.

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The model is accurate 95% of the time.

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The model's predictions are within $5,000 of the actual prices for 50% of the houses.

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The square root of the average squared error is $5,000.

Question 5

Which AI workload type is used when a system needs to automatically organize unstructured data into meaningful groups without predefined categories?

Accepted Answer

Clustering. Clustering is the correct AI workload type because it is an unsupervised learning technique that automatically groups unstructured data into meaningful clusters based on inherent similarities, without requiring predefined categories or labeled training data. This makes it ideal for tasks like customer segmentation, document organization, or anomaly detection where the natural structure of the data is unknown.

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Classification

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Regression

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Object detection

Question 6

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 7

A marketing team uses Azure OpenAI Service to generate ad copy. They notice the model sometimes uses offensive language. Which Azure OpenAI feature should they use to automatically block such content?

Accepted Answer

Enabling content filtering. Azure OpenAI Service includes built-in content filtering that automatically detects and blocks offensive or harmful language in both prompts and completions. This feature uses AI-based classifiers to enforce responsible AI policies without requiring manual configuration of model parameters.

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Setting the temperature parameter to 0.0

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Using the frequency_penalty parameter

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Configuring the max_tokens parameter

Question 8

What is 'time series forecasting' and what Azure ML tools support it?

Accepted Answer

Predicting future values in time-ordered data (sales, demand, energy) using Azure ML AutoML. Time series forecasting is a machine learning technique that predicts future values based on historical, time-ordered data, such as sales, demand, or energy consumption. Azure ML AutoML supports this by automatically selecting the best model (e.g., ARIMA, Prophet, or gradient boosting) and tuning hyperparameters for time-dependent features like seasonality and trends.

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Forecasting how long model training will take based on dataset size

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Scheduling ML jobs to run at specific times using Azure ML pipelines

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Analysing historical model performance over time to detect degradation

Question 9

A developer uses Azure OpenAI to generate Python code. They want the model to limit the length of the generated code to avoid overly long and complex functions. Which parameter should the developer set in the API call?

Accepted Answer

max_tokens. The `max_tokens` parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. By setting a lower `max_tokens` value, the developer can cap the length of the generated Python code, preventing overly long and complex functions. This is the correct parameter for limiting output length.

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temperature

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top_p

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frequency_penalty

Question 10

What does Azure AI Vision's image tagging feature return?

Accepted Answer

A list of descriptive keywords about the image content with confidence scores. Azure AI Vision's image tagging feature analyzes the content of an image and returns a list of descriptive keywords (tags) along with a confidence score for each tag. This allows applications to automatically identify objects, people, scenes, and actions within the image without requiring manual labeling.

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A JSON file with the image's color palette in hex codes

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GPS coordinates of where the photo was taken

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The camera settings used to capture the image

Question 11

Drag and drop the steps to create a cognitive service resource in Azure into the correct order.

Accepted Answer

Step 1: Sign in to Azure portal, Step 2: Click 'Create a resource' and search for 'Cognitive Services', Step 3: Select the desired service and configure settings, Step 4: Review and create. Creating a cognitive service involves navigating the portal, selecting the service type, configuring settings, and deploying.

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Step 1: Sign in to Azure portal, Step 2: Select the desired service and configure settings, Step 3: Click 'Create a resource' and search for 'Cognitive Services', Step 4: Review and create

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Step 1: Click 'Create a resource' and search for 'Cognitive Services', Step 2: Sign in to Azure portal, Step 3: Select the desired service and configure settings, Step 4: Review and create

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Step 1: Sign in to Azure portal, Step 2: Click 'Create a resource' and search for 'Cognitive Services', Step 3: Review and create, Step 4: Select the desired service and configure settings

Question 12

Match each Azure AI service to its data input type.

Accepted Answer

Computer Vision: Image (file or URL). Each Azure AI service expects specific data formats: Computer Vision uses images, Text Analytics uses text, and Speech uses audio. Common confusions involve swapping these input types.

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Computer Vision: Text documents

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Speech: Image (file or URL)

Question 13

Match each Azure AI service to its use case.

Accepted Answer

Computer Vision: Extract text from images (OCR). Each Azure AI service is designed for specific tasks: Computer Vision for image analysis (e.g., OCR), Translator for text translation, Speech for speech-to-text, and Anomaly Detector for time series pattern detection. Common confusions arise from overlapping capabilities like text extraction vs translation.

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Computer Vision: Translate text between languages

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Speech: Extract key phrases from text

Question 14

A construction company uses drone images to survey construction sites. They need an automated system that can identify specific types of heavy equipment (e.g., bulldozers, cranes, excavators) in an image and also draw precise pixel-level outlines around each equipment type. Which Azure Computer Vision capability should they use?

Accepted Answer

Semantic segmentation. Semantic segmentation is the correct capability because it assigns a class label (e.g., bulldozer, crane, excavator) to every pixel in the image, producing precise pixel-level outlines around each equipment type. Object detection only provides bounding boxes, not pixel-level masks, while image classification labels the entire image without localization. OCR is irrelevant as it extracts text, not equipment shapes.

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Object detection

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Image classification

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Optical Character Recognition (OCR)

Question 15

An autonomous driving company is developing a system that needs to understand the road scene at a granular level. For each pixel in a camera image, the system must classify whether it belongs to the road, a pedestrian, a vehicle, a traffic sign, or the sky. Which Azure Computer Vision capability should they use?

Accepted Answer

Semantic segmentation. Semantic segmentation is the correct choice because it classifies every pixel in an image into a predefined category, such as road, pedestrian, vehicle, traffic sign, or sky. This pixel-level classification is essential for autonomous driving to understand the road scene at a granular level, enabling precise boundary detection and scene understanding.

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Image classification

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Object detection

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Optical character recognition (OCR)