Databricks Updated Databricks-Certified-Data-Engineer-Associate Exam Questions and Answers by marco

Option D is the correct answer because it enables the Serverless feature for the SQL endpoint, which allows the endpoint to automatically scale up and down based on the query load. This way, the endpoint can handle more concurrent queries and reduce the time it takes to return results. The Serverless feature also reduces the cold start time of the endpoint, which is the time it takes to start the cluster when a query is submitted to a non-running endpoint. The Serverless feature is available for both AWS and Azure Databricks platforms.

 Databricks SQL Serverless, Serverless SQL endpoints, New Performance Improvements in Databricks SQL

For large-scale batch ETL workloads , Databricks documentation recommends using job clusters that are created specifically for a job run and terminated automatically when the job completes. A job cluster with autoscaling enabled provides the best balance of performance, reliability, and cost efficiency for long-running nightly pipelines. Autoscaling allows the cluster to dynamically add or remove worker nodes based on the workload demands, ensuring sufficient parallelism to process very large JSON datasets efficiently while avoiding overprovisioning when demand decreases. This is especially important when processing raw data into Delta tables, where shuffle-heavy transformations and writes benefit from multiple workers. Single-node clusters (option B) are not suitable for very large volumes of data and risk excessive runtimes or job failures. High-concurrency clusters (option C) are optimized for interactive and concurrent SQL queries, not long-running batch ETL jobs. Always-on all-purpose clusters (option D) increase costs unnecessarily and are intended for interactive development, not scheduled production pipelines. Databricks best practices clearly position autoscaling job clusters as the preferred solution for reliable, cost-effective batch ETL in production on Databricks.

Auto Loader in Databricks utilizes Spark Structured Streaming for processing data incrementally. This allows Auto Loader to efficiently ingest streaming or batch data at scale and to recognize new data as it arrives in cloud storage. Spark Structured Streaming provides the underlying engine that supports various incremental data loading capabilities like schema inference and file notification mode, which are crucial for the dynamic nature of data lakes.

 To return the number of null values in the member_id column, the best option is to use the count_if function, which counts the number of rows that satisfy a given condition. In this case, the condition is that the member_id column is null. The other options are either incorrect or not supported by Spark SQL. Option A will return the number of non-null values in the member_id column. Option B will not work because there is no count_null function in Spark SQL. Option D will not work because there is no null function in Spark SQL. Option E will not work because there is no count_null function in Spark SQL. References:

Built-in Functions - Spark SQL, Built-in Functions

count_if - Spark SQL, Built-in Functions