AI-900 Practice Test 30 — 15 Questions

Question 1

Match each Azure AI service to its regional availability constraint.

Question 2

Match each Azure AI service to its associated API or SDK.

Question 3

Match each Azure AI service to its data input type.

Question 4

Match each Azure AI tool to its purpose in the AI lifecycle.

Question 5

Match each Azure AI concept to its definition.

Question 6

Match each Azure AI service to its use case.

Question 7

What is 'AI transparency' and why is it challenging for deep learning models?

Accepted Answer

Deep learning models are 'black boxes' — high performance but difficult to explain because of millions of interacting parameters. Option B is correct because deep learning models, particularly those with many layers and millions of parameters, operate as 'black boxes.' Their internal decision-making processes are highly complex and non-linear, making it extremely difficult to trace how specific inputs lead to particular outputs. This lack of interpretability is the core challenge of AI transparency in deep learning.

Answer

Transparency is easy for all AI models because they use simple mathematical formulas

Answer

Transparency only matters for AI systems used in consumer products

Answer

Transparency is fully solved by showing the training data to stakeholders

Question 8

What is 'pronunciation assessment' in Azure AI Speech?

Accepted Answer

Scoring a speaker's accuracy, fluency, and completeness against native pronunciation norms. Pronunciation assessment in Azure AI Speech evaluates a speaker's spoken audio against native pronunciation norms, providing scores for accuracy, fluency, and completeness. This is a feature of the Speech-to-Text API that uses a reference script and phoneme-level comparison to generate detailed feedback, making it ideal for language learning and accent reduction applications.

Answer

Checking whether a text-to-speech voice pronounces technical terms correctly

Answer

Detecting regional accents to route calls to the appropriate customer service team

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Generating a list of commonly mispronounced words in a specific language

Question 9

Which Azure AI service detects and identifies human faces in images, including attributes like age estimate and emotion?

Accepted Answer

Azure AI Face. Azure AI Face is the correct service because it is specifically designed to detect and identify human faces in images, and it can extract attributes such as age estimates, emotions (e.g., happiness, sadness), and facial landmarks. Unlike general-purpose image analysis, Azure AI Face uses specialized face detection models and returns face rectangles along with optional attribute data.

Answer

Azure AI Vision

Answer

Azure AI Custom Vision

Answer

Azure AI Video Indexer

Question 10

Which of the following is an example of an AI workload that uses prediction?

Accepted Answer

Predicting equipment failure based on sensor data. Option B is correct because predicting equipment failure based on sensor data is a classic example of a predictive AI workload. It uses historical sensor data (e.g., temperature, vibration, pressure) to train a machine learning model that forecasts when equipment is likely to fail, enabling proactive maintenance. This falls under the AI workload category of prediction, where the model outputs a future outcome or probability.

Answer

Displaying a list of items in alphabetical order

Answer

Storing customer records in a database

Answer

Formatting text documents

Question 11

What is a feature in the context of machine learning?

Accepted Answer

An individual measurable property used as input to a machine learning model. In machine learning, a feature is an individual measurable property or characteristic of the data that is used as input to a model. Features are the variables that the model learns from to make predictions or classifications. This is a fundamental concept in ML, as the quality and relevance of features directly impact model performance.

Answer

The output or prediction made by a machine learning model

Answer

A type of neural network layer

Answer

A software capability in Azure Machine Learning

Question 12

What is Azure OpenAI Service?

Accepted Answer

Azure's deployment of OpenAI models with enterprise security and compliance. Azure OpenAI Service is correct because it provides access to OpenAI's powerful generative AI models (like GPT-4, GPT-3.5, and DALL-E) through Azure's cloud platform, with built-in enterprise-grade security, compliance, and responsible AI guardrails. Unlike a generic API, it integrates with Azure Active Directory, virtual networks, and private endpoints, ensuring data residency and privacy for enterprise workloads.

Answer

A service for building traditional rule-based chatbots

Answer

A machine learning training platform for custom models

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A database service for storing AI training data

Question 13

Which Azure AI capability can analyze video to identify and track specific people or objects across frames?

Accepted Answer

Azure AI Video Indexer. Azure AI Video Indexer is the correct choice because it is specifically designed to analyze video content, including the ability to detect, track, and identify people or objects across frames using AI-powered computer vision and audio analysis. It provides features like face detection, object tracking, and motion detection over time, making it suitable for this scenario.

Answer

Azure AI Custom Vision

Answer

Azure AI Face

Answer

Azure AI Vision OCR

Question 14

Which type of machine learning uses labeled training data where the correct output is provided for each input?

Accepted Answer

Supervised learning. Supervised learning is the correct answer because it explicitly uses labeled training data where each input example is paired with the correct output label. The algorithm learns to map inputs to outputs by minimizing the error between its predictions and the provided labels, enabling tasks like classification and regression.

Answer

Unsupervised learning

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Reinforcement learning

Answer

Transfer learning

Question 15

Which AI principle ensures that AI systems are developed and used in ways that are transparent and understandable to affected stakeholders?

Accepted Answer

Transparency. Transparency is the correct answer because it directly addresses the requirement that AI systems be open, explainable, and understandable to stakeholders. This principle ensures that decisions made by AI models can be audited, interpreted, and communicated clearly, which is essential for building trust and enabling informed consent.

Answer

Reliability

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Fairness

Answer

Privacy