AI-900 Practice Test 34 — 15 Questions

Question 1

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.

Answer

Setting the temperature parameter to 0.0

Answer

Using the frequency_penalty parameter

Answer

Configuring the max_tokens parameter

Question 2

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.

Answer

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 3

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.

Answer

temperature

Answer

top_p

Answer

frequency_penalty

Question 4

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.

Answer

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

Answer

GPS coordinates of where the photo was taken

Answer

The camera settings used to capture the image

Question 5

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.

Answer

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

Answer

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

Answer

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 6

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.

Answer

Computer Vision: Text documents

Answer

Speech: Image (file or URL)

Question 7

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.

Answer

Computer Vision: Translate text between languages

Answer

Speech: Extract key phrases from text

Question 8

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.

Answer

Object detection

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Image classification

Answer

Optical Character Recognition (OCR)

Question 9

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.

Answer

Image classification

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Object detection

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Optical character recognition (OCR)

Question 10

What is 'natural language processing' (NLP) as a category of AI workload?

Accepted Answer

Using AI to process and understand text and speech in human languages. Natural language processing (NLP) is an AI workload that focuses on enabling computers to interpret, understand, and generate human language in both text and speech forms. It combines computational linguistics with statistical machine learning models to perform tasks like sentiment analysis, language translation, and speech recognition. This makes option A the correct definition.

Answer

Programming computers using natural spoken language instead of code

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A network protocol for low-latency language model inference

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Automatically converting speech to a natural-sounding language

Question 11

A retail company has a dataset of customer transaction records with no predefined categories. They want to identify natural groupings of customers based on their purchasing behavior to create targeted marketing campaigns. Which type of machine learning should they use in Azure Machine Learning?

Accepted Answer

Clustering. Clustering is the correct choice because the goal is to discover natural groupings in unlabeled data based on purchasing behavior. Azure Machine Learning provides clustering algorithms like K-Means that automatically partition customers into segments without predefined labels, enabling targeted marketing campaigns.

Answer

Classification

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Regression

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Reinforcement learning

Question 12

What is an ML pipeline in Azure Machine Learning?

Accepted Answer

A workflow of connected steps for automating the end-to-end ML process. An ML pipeline in Azure Machine Learning is a workflow of connected steps that automates the end-to-end machine learning process, including data preparation, training, evaluation, and deployment. This enables reproducibility, reusability, and orchestration of complex ML tasks without manual intervention.

Answer

The networking infrastructure connecting Azure ML compute nodes

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A data streaming service for real-time model predictions

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A GitHub repository for storing ML model code

Question 13

A hospital wants to use AI to predict which patients are at high risk of readmission within 30 days of discharge. What type of AI task is this?

Accepted Answer

Classification or regression to predict readmission risk. Predicting readmission risk is a supervised learning task where the model learns from historical patient data (features like age, diagnosis, lab results) to output a risk score. If the output is a continuous probability (e.g., 0.75 risk), it is regression; if it is a discrete category (e.g., high/low risk), it is classification. Both are valid approaches for this predictive scenario.

Answer

Clustering to group similar patients together

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Generative AI to create patient health summaries

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Anomaly detection to find unusual test results

Question 14

An e-commerce website wants to automatically remove the background from product photos uploaded by sellers so that items appear on a consistent plain background. Which Azure Computer Vision capability should they use?

Accepted Answer

Background Removal. Background Removal is the correct capability because it is specifically designed to isolate the foreground subject from the background in an image, producing a transparent or solid-color background. This directly meets the requirement of automatically removing backgrounds from product photos to create a consistent plain background. Azure's Background Removal API uses deep learning models trained on millions of images to segment the primary object from its surroundings.

Answer

Optical Character Recognition (OCR)

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Image Captioning

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Object Detection

Question 15

A company wants to automate the processing of expense reports by extracting printed text from images of receipts. Which Azure Computer Vision capability should they use?

Accepted Answer

OCR (Read API). The OCR (Read API) is the correct Azure Computer Vision capability for extracting printed text from images of receipts. It is specifically designed to detect and extract text from images and documents, supporting both printed and handwritten text, making it ideal for automating expense report processing.

Answer

Object detection

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Semantic segmentation

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Image Analysis (description generation)