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

A data scientist needs to deploy a model to Azure that supports real-time predictions. Which Azure service should they use?

A Azure Kubernetes Service
B Azure Logic Apps
C Azure Machine Learning
D Azure Virtual Machines

Explanation Azure Machine Learning is designed for model deployment and real-time predictions, while the other services serve different purposes.

Q122

You are configuring Azure Data Factory for data ingestion. What will happen if the dataset format in the source does not match the defined dataset format in ADF?

A Data will be automatically converted.
B Data ingestion will fail.
C Only valid data will be ingested.
D Infer schema and adjust automatically.

Explanation If the dataset formats do not match, ingestion fails rather than converting data types, unlike options A, C, and D which are incorrect.

Q123

A company needs to classify customer emails into categories using machine learning. Which approach should they take with Azure?

A Use Azure Blob Storage only.
B Implement Azure Functions for email parsing.
C Apply Azure Cognitive Services for text analytics.
D Train a model in Azure DevOps.

Explanation Azure Cognitive Services provides ready-to-use text analytics for email classification, whereas the other options are either incomplete or unsuitable.

Q124

Which service is best suited for real-time analytics on streaming data in Azure?

A Azure Stream Analytics
B Azure Data Lake Storage
C Azure Synapse Analytics
D Azure Blob Storage

Explanation Azure Stream Analytics is designed for real-time analytics, while the others focus on storage or batch processing.

Q125

A company needs to securely store and manage sensitive medical data in Azure. Which solution is most appropriate?

A Azure Blob Storage
B Azure Cosmos DB
C Azure Databricks
D Azure SQL Database with encryption

Explanation Azure SQL Database with encryption provides secure data management, whereas the others do not primarily focus on high-level security.

Q126

You are configuring a machine learning model in Azure ML. What happens if you use too little training data?

A The model will overfit.
B The model will underfit.
C The model will achieve high accuracy.
D The model will be less complex.

Explanation Using too little training data leads to underfitting, while overfitting typically occurs with too much unnecessary complexity.

Q127

Which Azure service is best for predictive analytics?

A Azure Machine Learning
B Azure Functions
C Azure Blob Storage
D Azure SQL Database

Explanation Azure Machine Learning offers tools specifically for predictive analytics; other options serve different purposes.

Q128

You are configuring a pipeline in Azure Data Factory. What happens if a pipeline activity fails?

A Pipeline automatically retries indefinitely
B Pipeline halts and reports failure
C All previous activities are rolled back
D Pipeline completes with warnings

Explanation The pipeline will halt and report the failure unless configured for retries; other options misrepresent default behavior.

Q129

A company needs to monitor machine learning model performance in real-time. Which Azure service should they use?

A Azure Monitor
B Azure Data Lake Storage
C Azure Synapse Analytics
D Azure DevOps

Explanation Azure Monitor provides the metrics and logging necessary for real-time performance monitoring; the others do not specialize in monitoring.

Q130

Which service is best for model deployment in Azure?

A Azure Machine Learning Endpoint
B Azure Batch
C Azure Data Lake
D Azure Databricks

Explanation Azure Machine Learning Endpoint is specifically designed for deploying ML models, while the other options serve different purposes.