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DP-203 Practice Question: A data engineering team is designing a batch…
A data engineering team is designing a batch processing solution using Azure Databricks. The data is stored in Azure Data Lake Storage Gen2 (ADLS Gen2) and must be processed daily with minimal cost. The team needs to choose between using a Delta Lake table or a Parquet file format for the processed output. Which TWO factors should the team consider when making this decision?
⚠ Common exam trap
Many candidates assume Parquet is not natively supported in Databricks, but in reality, Parquet is the default storage format for Delta Lake and is fully supported; the key differentiators are ACID transactions and time travel, not format compatibility.
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
✓
Delta Lake provides time travel capabilities for accessing historical data versions.
Delta Lake's time travel feature allows querying previous versions of data using a timestamp or version number, which is essential for auditing, rollback, and reproducing historical reports. This capability is built on the transaction log that tracks every change, making it a key differentiator from plain Parquet files.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Delta Lake provides time travel capabilities for accessing historical data versions.
Why this is correct
Delta Lake's time travel feature allows querying previous versions of data.
- ✗
Parquet is easier to implement for schema evolution than Delta Lake.
Why it's wrong here
Delta Lake supports schema evolution more easily than Parquet.
- ✗
Delta Lake reduces storage costs by automatically compressing data.
Why it's wrong here
Delta Lake does not automatically compress data; storage costs depend on the underlying file format.
- ✓
Delta Lake supports ACID transactions, ensuring data consistency during concurrent writes.
Why this is correct
Delta Lake provides ACID transactions for reliable concurrent operations.
- ✗
Parquet files are not natively supported by Azure Databricks.
Why it's wrong here
Azure Databricks natively supports reading and writing Parquet files.
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Same concept, more angles
1 more way this is tested on DP-203
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You are designing a batch processing pipeline that reads CSV files from Azure Blob Storage, performs aggregations using Azure Databricks, and writes results to Azure Synapse Analytics. The pipeline must handle schema drift (new columns appearing in source files). Which approach should you recommend?
easy- A.Use Azure Data Factory mapping data flows with schema drift enabled, mapping to a fixed sink schema.
- B.Define a fixed schema in the source and ignore any new columns.
- ✓ C.Use Spark with mergeSchema option when reading, and write using a Delta table to evolve schema automatically.
- D.Use Azure Stream Analytics to pre-process and enforce schema.
Why C: Spark's `mergeSchema` option, when used with Delta Lake, automatically evolves the schema to accommodate new columns in CSV files. This allows the batch pipeline to handle schema drift without manual intervention, and writing to a Delta table ensures the schema evolution is persisted and compatible with downstream writes to Azure Synapse Analytics.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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