Describe an analytics workload on Azure →hardMultiple Select

DP-900 Describe an analytics workload on Azure Practice Question

Which TWO options are valid ways to ingest data into Azure Data Lake Storage Gen2?

⚠ Common exam trap

It's easy for candidates to confuse tools that can read from or query Azure Data Lake Storage Gen2 (like Power BI or Azure SQL Database) with tools that can directly write or ingest data into it, overlooking the specific ingestion mechanisms required.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

AzCopy

AzCopy (C) is a valid ingestion method because it is a command-line utility designed to copy data to and from Azure Storage, including Data Lake Storage Gen2, using commands like `azcopy copy` with blob or ADLS Gen2 endpoints. Azure Data Factory (E) is also correct because it provides connectors and copy activities that can load data from numerous sources into ADLS Gen2 as a sink. Power BI (A) is primarily a visualization and reporting service, not a data ingestion tool for ADLS Gen2. Azure SQL Database (B) is a relational database service and can be a source or target, but it is not itself a valid ingestion mechanism into ADLS Gen2. Azure DevOps (D) is a development lifecycle platform and does not provide native data ingestion into ADLS Gen2.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Power BI
Why it's wrong here
Power BI is a business analytics service for visualizing and reporting on data, not an ingestion service. It can connect to data sources and import data into its own in-memory model, but that is for analysis, not for moving data into a data lake or warehouse. It lacks the orchestration, scheduling, and large-scale data movement capabilities of dedicated ingestion tools.
✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a managed relational database service that can act as a source or destination for data, but it is not an ingestion tool. Ingesting data into Azure Data Lake Storage from Azure SQL Database would require a service like Azure Data Factory, which can connect to the database and copy data. Merely having a database does not perform ingestion.
✓
AzCopy
Why this is correct
AzCopy is a command-line utility designed for high-performance copying of data to and from Azure Blob Storage and Azure Data Lake Storage. It supports synchronization, recursive directory copies, and resumable transfers, making it a valid lightweight option for ad-hoc or scripted ingestion. It can be integrated into pipelines via scripts, though it lacks native orchestration and scheduling.
✗
Azure DevOps
Why it's wrong here
Azure DevOps is a suite of tools for software development lifecycle management, including CI/CD pipelines, repos, and boards. It is not designed for data ingestion into Azure storage; its pipelines might execute scripts that use tools like AzCopy, but DevOps itself does not move data. Using it for ingestion would be an indirect and unconventional approach, not a native ingestion method.
✓
Azure Data Factory
Why this is correct
Azure Data Factory is a cloud-based data integration service that orchestrates and automates data movement and transformation. It provides over 90 connectors, a visual pipeline designer, and scheduling capabilities, making it the primary ETL/ELT service for ingesting data into Azure Data Lake Storage. It supports activities like Copy Data, allowing complex data flows from diverse sources.

Go deeper

DP-900 Describe an analytics workload on Azure Practice Question

Why each option matters

Option-by-option breakdown

Related to this question