Sample Questions from Databricks Data Engineer – Associate (DB-DEA)

Preview a few questions below — answers are revealed when you take the exam.

1. A team needs to optimize a Databricks pipeline for processing large-scale streaming data. The pipeline currently experiences latency issues during peak usage times. What approach should they take to address these performance bottlenecks?

   • Implement a caching layer using Databricks Delta to store intermediate results and reduce the load on the streaming data source.
   • Migrate the entire pipeline to a different cloud provider to leverage their more robust streaming data services.
   • Increase the number of Databricks clusters and use dynamic allocation to handle peak loads more effectively.
   • Utilize Apache Spark's built-in machine learning libraries to predict and preemptively scale resources based on usage patterns.

2. Consider the situation where a Databricks job fails intermittently due to resource constraints. Which method solves it best?

   • Reconfigure the job to use a smaller cluster size to reduce resource consumption.
   • Implement a retry mechanism within the job to handle transient failures more gracefully.
   • Migrate the job to a different region with more available resources.
   • Use Databricks Autoscaling to automatically adjust the cluster size based on the job's resource requirements.

3. Identify the command used in Databricks to create a new Delta table.

   • CREATE DELTA TABLE
   • CREATE TABLE USING DELTA
   • CREATE DELTA DATABASE
   • CREATE TABLE WITH DELTA

4. Which option best evaluates the performance of a Databricks cluster configured for batch processing?

   • Analyze the cluster's CPU and memory utilization over time using Databricks' built-in monitoring tools.
   • Compare the cluster's performance metrics with industry benchmarks for similar workloads.
   • Conduct a series of load tests with varying data volumes to identify performance bottlenecks.
   • Review the cluster's configuration settings and adjust them based on best practices for batch processing.

5. How should a professional assess the effectiveness of a Databricks pipeline designed for real-time data ingestion?

   • Monitor the pipeline's latency and throughput metrics to ensure they meet the required service level agreements.
   • Conduct regular code reviews to ensure the pipeline's code adheres to best practices and standards.
   • Perform periodic data quality checks to verify the accuracy and completeness of the ingested data.
   • Evaluate the pipeline's scalability by gradually increasing the data volume and observing its performance.