Databricks-DA-Assoc Importing Data Practice Question
Which THREE of the following are valid sources for importing data into a Databricks Delta table? (Choose three)
⚠ Common exam trap
Candidates often mistakenly include internal Databricks features like 'Delta Live Tables' or 'Unity Catalog' as data sources, failing to distinguish between data ingestion sources and governance tools.
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
✓
S3, ADLS, and GCS cloud object storage.
Databricks provides flexible connectivity to ingest data from diverse sources into the Lakehouse. Whether the data is in cloud storage, a relational database, or a streaming message broker, the platform offers optimized connectors to load this information into Delta format. Mastering these integration points allows analysts to architect robust data pipelines that can pull information from virtually any enterprise system for unified analysis and reporting within a single environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
S3, ADLS, and GCS cloud object storage.
Why this is correct
These cloud object storage services are the native foundation for data lakes in Databricks. They are the most common sources for large-scale data ingestion and are fully supported by Auto Loader, COPY INTO, and standard Spark read APIs, making them the primary choice for enterprise data warehousing strategies.
- ✗
Local files stored on the Databricks Driver node.
Why it's wrong here
Files stored on the driver node are ephemeral and not accessible across the cluster. While they can be used for small, local scripts, they are not a reliable source for enterprise-level data ingestion into Delta tables, which require persistent storage that all worker nodes can access consistently during processing.
- ✓
External relational databases via JDBC/ODBC connectors.
Why this is correct
Databricks supports JDBC/ODBC connectivity for importing data from traditional relational databases like MySQL, PostgreSQL, or SQL Server. This allows analysts to bring structured data from transactional systems into the Delta Lake, enabling cross-functional analytics that combine transactional data with broader data lake information for deeper insights.
- ✗
Direct memory buffers from the user's browser.
Why it's wrong here
Databricks does not support direct streaming of memory buffers from a user's browser into the Delta table. Data must be persisted to a supported file system or stream source before it can be ingested. The browser interface is for interaction, not for direct, low-level data ingestion via memory pointers.
- ✓
Streaming services like Apache Kafka or Amazon Kinesis.
Why this is correct
Databricks has native support for reading from streaming message brokers such as Kafka and Kinesis. This enables real-time ingestion into Delta tables, allowing businesses to analyze events as they occur. This integration is crucial for building modern, event-driven architectures that require immediate visibility into streaming telemetry or transactional logs.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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