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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.
Option A is correct because Delta Lake stores a transaction log (_delta_log) that records every commit, enabling time travel queries such as SELECT * FROM table VERSION AS OF n or TIMESTAMP AS OF, which is valuable for auditing, reproducing past results, and recovering from bad writes in a daily batch pipeline. Option D is correct because Delta Lake provides ACID transactions on top of ADLS Gen2, so concurrent writers and readers see consistent snapshots and partial or failed writes are not exposed, which matters when multiple jobs or retries touch the same output. Option B is wrong because schema evolution is actually a Delta Lake strength (mergeSchema and automatic schema evolution in MERGE/append), while Parquet requires manual rewrites or workarounds. Option C is wrong because Delta Lake does not automatically compress data to reduce storage cost; it relies on Parquet compression plus OPTIMIZE/Z-ORDER and VACUUM for layout and cleanup, and it adds transaction-log overhead. Option E is wrong because Azure Databricks natively reads and writes Parquet files, including via Spark and external tables.
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 transaction log stores metadata for every write, enabling time travel to query or restore earlier table versions without duplicating the underlying Parquet files. This satisfies the daily batch scenario's need for historical data access and rollback, while retaining Parquet's columnar storage benefits for cost-effective processing.
- ✗
Parquet is easier to implement for schema evolution than Delta Lake.
Why it's wrong here
Parquet has no schema evolution mechanism; changing columns requires rewriting files, whereas Delta Lake supports schema evolution and enforcement through its transaction log. It is tempting because Parquet is a simple, widely supported columnar format, but it suits static-schema outputs rather than evolving ones.
- ✗
Delta Lake reduces storage costs by automatically compressing data.
Why it's wrong here
Delta Lake does not automatically compress data to reduce storage costs; it stores Parquet files plus a transaction log, which adds storage overhead. It is tempting because Delta Lake does provide automatic file compaction and optimisation, but those target small-file and query performance problems, not storage cost reduction.
- ✓
Delta Lake supports ACID transactions, ensuring data consistency during concurrent writes.
Why this is correct
Delta Lake's transaction log delivers ACID guarantees, so concurrent writes during the daily batch cannot leave partially committed or inconsistent output in ADLS Gen2. Parquet files lack this coordination, risking corrupt reads if jobs overlap. This satisfies the stem's data consistency requirement while remaining cost-effective for batch processing.
- ✗
Parquet files are not natively supported by Azure Databricks.
Why it's wrong here
Azure Databricks reads and writes Parquet natively as its default file format, so this claim is false. It is tempting because Delta Lake is the recommended table format on Databricks, but Parquet remains fully supported for both batch and streaming reads and writes.
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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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