Welcome to your Introduction to Data Engineering on Azure

What is the primary role of data engineers in Azure data engineering?

Developing machine learning models

Designing user interfaces

Building and maintaining data infrastructure

Conducting market research

Which Azure service is commonly used for orchestrating and automating data pipelines?

Azure Cosmos DB

Azure Data Lake Storage

Azure Data Factory

Azure SQL Database

What does Azure Synapse Analytics provide for seamless data warehousing?

Real-time data processing

Serverless computing

Integrated analytics service

Predictive modeling

Which trend is shaping the future of data engineering with Azure?

Social media marketing

AI and machine learning integration

Renewable energy

Virtual reality gaming

What is the primary goal of data governance in Azure data engineering?

Maximizing profit

Ensuring data security and compliance

Reducing data storage costs

Improving customer satisfaction

Which Azure service provides capabilities for processing and analyzing data at the edge?

Azure Kubernetes Service

Azure Functions

Azure IoT Edge

Azure Event Grid

What is the primary benefit of using Azure for data engineering?

Limited scalability options

High infrastructure costs

Pay-as-you-go pricing model

Complex deployment process

Which Azure service is commonly used for big data analytics and machine learning?

Azure DevOps

Azure Machine Learning

Azure Kubernetes Service

Azure Container Instances

What role do data pipelines play in Azure data engineering?

Managing social media campaigns

Orchestration and automation of data workflows

Designing user interfaces

Conducting market research

What is the primary objective of data engineering on Azure?

Maximizing data storage costs

Minimizing data processing efficiency

Ensuring data quality and reliability

Ignoring data security concerns

What is streaming data?

A) Data collected and processed in fixed intervals

B) Continuous flow of data generated, processed, and analyzed in real-time

C) Data stored in data warehouses for later analysis

D) Data transmitted in large batches periodically

What are the characteristics of streaming data?

A) High volume, low velocity, and low variety

B) Low volume, high velocity, and high variety

C) High volume, high velocity, and low variety

D) Low volume, low velocity, and high variety

Which industry commonly uses streaming data for real-time monitoring and analysis of physical assets and environments?

A) Healthcare

B) Financial Services

C) Retail

D) Internet of Things (IoT)

What technology is used to ingest, process, and analyze streaming data in real-time?

A) Message Brokers

B) Data Warehouses

C) Apache Spark

D) Stream Processing Engines

What is the primary role of data pipelines in data engineering?

A) Centralized storage of data

B) Orchestration of data transfer and transformation

C) Real-time monitoring of data

D) Data analysis and visualization

What differentiates a data lake from a data warehouse?

A) Data lakes prioritize immediate transformation and processing of data.

B) Data lakes preserve data fidelity in its original, unaltered state.

C) Data lakes store structured data only.

D) Data lakes have limited scalability compared to data warehouses.

Why is Apache Spark essential for data engineering in the era of big data?

A) It prioritizes data transformation over data storage.

B) It is renowned for its utilization of distributed file storage.

C) It enables real-time monitoring of data pipelines.

D) It facilitates efficient processing and analysis of vast datasets.

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DP-203 Azure Data Engineering Practice