Databricks-Spark-Assoc Using Spark SQL Practice Question
Which of the following is the most efficient way to convert a Spark DataFrame into a format suitable for low-latency SQL queries in Databricks?
⚠ Common exam trap
Candidates often suggest converting to Parquet or JSON, believing these are the standard for SQL. They overlook that Delta Lake is the native, optimized format for the Databricks Lakehouse architecture.
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
✓
Write the data into a Delta table.
Delta Lake is the gold standard for Spark workloads because it brings ACID transactions, schema enforcement, and high performance to data lakes. Converting DataFrames to Delta tables enables indexing, data skipping, and file-level statistics, which are essential for low-latency SQL access. This approach replaces older formats like Parquet, offering superior capabilities and seamless integration with the Databricks engine, making it the standard practice for modern data lake architecture.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write the data as a collection of CSV files in the cloud storage.
Why it's wrong here
CSV is a text-based, row-oriented format that is extremely slow for SQL queries. It does not support compression, metadata, or predicate pushdown, all of which are required for low-latency access. Reading CSV files requires the engine to parse the entire dataset, which is inefficient for large-scale analytical tasks.
- ✓
Write the data into a Delta table.
Why this is correct
Delta tables are optimized for performance with features like Z-Ordering, data skipping, and statistics. By leveraging these features, Delta tables provide the best balance of write performance and low-latency read performance for SQL queries in Databricks, making them the preferred choice for analytical data storage in the lakehouse.
- ✗
Keep the data as a temporary view in the driver's memory.
Why it's wrong here
Temporary views reside in memory, which is limited and volatile. They do not persist data to disk, meaning the data is lost when the cluster terminates. Furthermore, storing large datasets in the driver's memory will quickly lead to OOM errors, making this approach unsuitable for any production-scale SQL workload.
- ✗
Write the data as JSON files to exploit document-based query engines.
Why it's wrong here
JSON is not an efficient format for relational analytical queries. It is verbose, lacks a schema, and does not support the columnar optimizations that make SQL queries fast in Spark. Using JSON for storage would result in significantly slower read times compared to columnar formats like Delta or Parquet.
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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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