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 81–90 of 138

Q81

What happens when a data scientist attempts to use an Azure ML model in a production environment without versioning?

  • A Model fails to deploy
  • B Model cannot be retrained
  • C Model may introduce inconsistencies
  • D Model performance improves automatically
Explanation Without versioning, the model might lead to inconsistencies, affecting predictability and reliability in production.
Q82

Which Azure service is best for large-scale machine learning model training?

  • A Azure Machine Learning
  • B Azure Functions
  • C Azure Blob Storage
  • D Azure SQL Database
Explanation Azure Machine Learning provides dedicated resources and frameworks for training models, unlike the other services.
Q83

A company needs real-time insights from streaming data. Which Azure service should they use?

  • A Azure SQL Data Warehouse
  • B Azure Stream Analytics
  • C Azure Data Lake
  • D Azure Blob Storage
Explanation Azure Stream Analytics specializes in real-time analytics on streaming data, while the others focus on batch processing or storage.
Q84

You are configuring a machine learning model in Azure. What happens when you choose a low-complexity algorithm?

  • A Higher accuracy with more data
  • B Faster training times
  • C Better performance on all datasets
  • D Increased risk of overfitting
Explanation Low-complexity algorithms generally train faster but may underperform on complex patterns, in contrast to the other options.
Q85

What should you choose for real-time data processing in Azure?

  • A Azure Databricks
  • B Azure Data Lake
  • C Azure Functions
  • D Azure Blob Storage
Explanation Azure Databricks provides optimal real-time data processing capabilities, while the others focus on storage or background tasks.
Q86

A company needs to analyze large datasets stored in Azure Blob Storage. Which Azure service should they use?

  • A Azure Machine Learning
  • B Azure SQL Database
  • C Azure Data Factory
  • D Azure Synapse Analytics
Explanation Azure Synapse Analytics is optimized for querying large datasets, unlike other options more suited for specific tasks.
Q87

You are configuring a machine learning pipeline in Azure ML. What could lead to data drift during model training?

  • A Change in feature set
  • B Quality of training data
  • C Feature values changing over time
  • D Model type selection
Explanation Data drift occurs due to changing feature values over time; other options might impact model performance but not data drift specifically.
Q88

Which service is best for real-time event streaming in Azure?

  • A Azure Event Hubs
  • B Azure Blob Storage
  • C Azure SQL Database
  • D Azure Functions
Explanation Azure Event Hubs excels at ingesting and processing real-time events, while the others serve different roles.
Q89

A company needs a highly scalable service for deploying machine learning models. Which Azure service should they use?

  • A Azure Containers
  • B Azure App Service
  • C Azure Machine Learning
  • D Azure Batch
Explanation Azure Machine Learning provides built-in functionalities for model deployment and scaling.
Q90

You are configuring Azure Data Factory for data movement. What happens if a pipeline fails during execution?

  • A Data is automatically retried.
  • B Execution stops without errors.
  • C Error is logged, manual intervention needed.
  • D Previous successful steps revert.
Explanation If a pipeline fails, the error is logged, and you must manage it manually unless retry mechanisms are configured.