Practice Exam Questions

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Question 1

Which Azure service is most commonly used to store large, unstructured datasets for machine learning training?

A. Azure SQL Database
B. Azure Blob Storage
C. Azure Cosmos DB
D. Azure Virtual Machines

✅ Correct Answer: B. Azure Blob Storage

Explanation:
Azure Blob Storage is designed to store large amounts of unstructured data such as files, images, and CSVs. It is the most common data storage service used in machine learning workflows.

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Question 2

Which Azure service is specifically designed to train, manage, and deploy machine learning models?

A. Azure Kubernetes Service (AKS)
B. Azure Machine Learning
C. Azure Data Factory
D. Azure App Service

✅ Correct Answer: B. Azure Machine Learning

Explanation:
Azure Machine Learning provides managed tools and compute for training, evaluating, and deploying machine learning models. It is the core ML service in Azure.

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Question 3

You need to store structured, relational data that will be used to train a machine learning model. Which Azure service is most appropriate?

A. Azure Blob Storage
B. Azure Data Lake Storage
C. Azure SQL Database
D. Azure File Storage

✅ Correct Answer: C. Azure SQL Database

Explanation:
Azure SQL Database is used for structured data stored in tables with defined schemas, making it suitable for relational datasets used in machine learning.

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Question 4

Which Azure service is primarily used to deploy machine learning models for scalable, real-time predictions?

A. Azure Virtual Machines
B. Azure Machine Learning compute
C. Azure Kubernetes Service (AKS)
D. Azure Blob Storage

✅ Correct Answer: C. Azure Kubernetes Service (AKS)

Explanation:
AKS is commonly used to deploy machine learning models in production environments where scalability and high availability are required.

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Question 5

What is the primary purpose of compute resources in machine learning?

A. To store training data
B. To visualize data
C. To train and run machine learning models
D. To manage user access

✅ Correct Answer: C. To train and run machine learning models

Explanation:
Compute resources provide the processing power required to train models and perform inference.

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Question 6

Which Azure service provides customizable compute environments, including GPU-based machines, for machine learning workloads?

A. Azure Functions
B. Azure Virtual Machines
C. Azure Logic Apps
D. Azure SQL Database

✅ Correct Answer: B. Azure Virtual Machines

Explanation:
Azure Virtual Machines allow users to fully control the operating system, software, and hardware configuration, making them ideal for specialized ML workloads.

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Question 7

Which data service is best suited for big data analytics and large-scale machine learning workloads?

A. Azure Blob Storage
B. Azure SQL Database
C. Azure Data Lake Storage Gen2
D. Azure Table Storage

✅ Correct Answer: C. Azure Data Lake Storage Gen2

Explanation:
Azure Data Lake Storage Gen2 is optimized for analytics and big data workloads, making it ideal for large-scale machine learning scenarios.

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Question 8

In a typical Azure machine learning workflow, where are trained models and output artifacts often stored?

A. Azure Virtual Machines
B. Azure Blob Storage
C. Azure SQL Database
D. Azure Active Directory

✅ Correct Answer: B. Azure Blob Storage

Explanation:
Blob Storage is commonly used to store trained models, logs, and experiment outputs due to its scalability and cost efficiency.

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Question 9

Which Azure service combines data storage and analytics capabilities for machine learning and data science?

A. Azure Data Lake Storage
B. Azure File Storage
C. Azure App Service
D. Azure Functions

✅ Correct Answer: A. Azure Data Lake Storage

Explanation:
Azure Data Lake Storage is built for analytics and integrates well with data science and machine learning workloads.

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Question 10

Which statement best describes Azure Machine Learning compute?

A. It is used only for storing machine learning data
B. It provides managed compute resources for training and inference
C. It replaces Azure Virtual Machines
D. It is used only for model deployment

✅ Correct Answer: B. It provides managed compute resources for training and inference

Explanation:
Azure Machine Learning compute offers scalable, managed CPU and GPU resources specifically designed for training and running machine learning models.

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Final Exam Tips 🔑

For AI-900, remember these high-yield associations:

• Blob Storage → unstructured ML data
• Data Lake Storage → big data & analytics
• Azure SQL Database → structured data
• Azure Machine Learning → training & managing models
• Virtual Machines → custom ML environments
• AKS → scalable deployment

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