Microsoft Azure

Designing and Implementing a Data Science Solution on Azure

DP-100

Master data science on Azure with the DP-100 exam focusing on designing and implementing solutions.

138 questions 0 views Free
Start Mock Test Timed · Full-length · Scored

Questions 31–40 of 138

Q31

Which Azure service is mainly used for building conversational AI applications?

  • A Azure Bot Service
  • B Azure Functions
  • C Azure Storage
  • D Azure CDN
Explanation Azure Bot Service specifically provides a framework for creating conversational interfaces, while the other options serve different purposes.
Q32

A company needs to deploy a data science model rapidly to a web application. Which Azure feature should they use?

  • A Azure Kubernetes Service
  • B Azure Machine Learning Endpoint
  • C Azure VM Scale Sets
  • D Azure Data Factory
Explanation Azure Machine Learning Endpoint allows rapid deployment of models, whereas others serve deployment or orchestration roles.
Q33

You are configuring an Azure Machine Learning workspace. What happens if you do not choose a storage account during the setup?

  • A Setup will fail with an error
  • B Default storage will be assigned
  • C Setup continues without storage
  • D You can add it later only
Explanation If no storage is specified, Azure assigns a default storage account, while the others misrepresent possible outcomes during setup.
Q34

Which service is best for real-time analytics in Azure?

  • A Azure Stream Analytics
  • B Azure DevOps
  • C Azure Data Lake
  • D Azure Blob Storage
Explanation Azure Stream Analytics processes data in real time; the others don't focus on real-time analytics.
Q35

A company needs to deploy a machine learning model at scale. Which Azure service should they use?

  • A Azure Functions
  • B Azure Machine Learning
  • C Azure App Service
  • D Azure Storage Account
Explanation Azure Machine Learning is designed specifically for scalable model deployment.
Q36

What happens when you enable auto-scaling in Azure Machine Learning?

  • A Model training halts until resources are free
  • B Resources automatically adjust based on load
  • C Data processing stops if resources overload
  • D User must manually adjust parameters each time
Explanation Auto-scaling adjusts resources based on demand, enabling efficient usage.
Q37

Which Azure service is best for creating ML models?

  • A Azure Machine Learning
  • B Azure Blob Storage
  • C Azure Data Lake
  • D Azure SQL Database
Explanation Azure Machine Learning is specifically designed for ML model development, while others serve different purposes.
Q38

A retail company needs to process large volumes of streaming data in real-time. What Azure service should they use?

  • A Azure Functions
  • B Azure Stream Analytics
  • C Azure Batch
  • D Azure Cosmos DB
Explanation Azure Stream Analytics is optimized for real-time analytics on streaming data, unlike the other options.
Q39

You are configuring a data science project in Azure. What happens if you select 'Experiment' instead of 'Pipeline'?

  • A It becomes a long-running job
  • B No execution/UI will be available
  • C You can track versions of models
  • D It's optimized for production deployment
Explanation Selecting 'Experiment' allows version tracking, while 'Pipeline' focuses on production workflows or orchestration.
Q40

Which Azure service is best for deploying ML models with minimal management?

  • A Azure Machine Learning
  • B Azure Databricks
  • C Azure Function Apps
  • D Azure Batch
Explanation Azure Machine Learning offers streamlined model deployment, while the others require more management or are for different tasks.