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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

Section	Objectives
Data Processing and Transformations	- Delta Lake fundamentals (tables, transactions, optimization) - Apache Spark SQL operations (joins, aggregations, filtering) - User-defined functions (UDFs) - PySpark DataFrame transformations
Data Ingestion and ELT Development	- ETL patterns and transformations - Data ingestion using Spark SQL and PySpark - Handling structured and semi-structured data
Databricks Lakehouse Platform Fundamentals	- Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage
Productionizing Data Pipelines	- Pipeline deployment and operationalization - Databricks Workflows / Jobs orchestration - Scheduling and monitoring jobs
Data Governance and Quality	- Data access control and governance - Unity Catalog basics - Data quality concepts and management

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q118-Q123):

NEW QUESTION # 118
A data engineer is joining a very large events_df (billions of rows) with a small country_lookup_df (fewer than 10,000 rows) on the country_code column. The join is slow due to data shuffling. How should the data engineer improve performance?

A. Cache both DataFrames, then run the same join
B. broadcast country_lookup_df when performing the join
C. Persist both DataFrames, then run the same join
D. broadcast events_df when performing the join

Answer: B

Explanation:
Broadcasting country_lookup_df sends the small lookup table to each executor, avoiding a large shuffle of the billions-row events_df and improving join performance.

NEW QUESTION # 119
A data engineering team wants to validate a new ingestion pipeline locally while ensuring large aggregations run in serverless compute in their Databricks workspace. They plan to use Databricks Connect and have the option to attach to either a shared cluster or serverless.
Which workspace requirement should be confirmed first to avoid connection failures?

A. Verify that the local Spark version equals the serverless Spark version to satisfy Spark Connect parity.
B. Verify that only assigned access mode clusters are used because serverless is not supported by Databricks Connect.
C. Verify that the workspace has Unity Catalog disabled and the Databricks Connect version is less than the serverless Runtime version.
D. Verify that the workspace has Unity Catalog enabled and that the Databricks Connect version supports serverless for the target Runtime release.

Answer: D

Explanation:
Databricks Connect requires Unity Catalog to be enabled when connecting to serverless compute, and the Databricks Connect version must explicitly support serverless for the target Databricks Runtime to ensure compatibility and prevent connection failures.

NEW QUESTION # 120
Which of the following approaches should be used to send the Databricks Job owner an email in the case that the Job fails?

A. Setting up an Alert in the Notebook
B. Manually programming in an alert system in each cell of the Notebook
C. Setting up an Alert in the Job page
D. There is no way to notify the Job owner in the case of Job failure
E. MLflow Model Registry Webhooks

Answer: C

Explanation:
Explanation
https://docs.databricks.com/en/workflows/jobs/job-notifications.html

NEW QUESTION # 121
A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities.
Which type of Databricks cluster should the data engineer configure?

A. A high-concurrency cluster designed for interactive SQL workloads.
B. A job cluster configured to autoscale across multiple workers during the pipeline run.
C. A lightweight single-node cluster with low worker node count to reduce costs.
D. An all-purpose cluster always kept running to ensure low-latency job startup times.

Answer: B

Explanation:
Job clusters are optimized for automated production workloads. They start when a job is triggered and terminate automatically once the task completes. This ensures cost control while maintaining performance and reliability for batch ETL. Autoscaling allows Databricks to add or remove workers dynamically based on workload size, ensuring large data volumes are processed efficiently.
All-purpose clusters are intended for development or ad-hoc workloads, not scheduled ETL.
Reference Source: Databricks Compute and Job Cluster Configuration Documentation - "Autoscaling and Job Clusters."

NEW QUESTION # 122
A data engineer has finished developing a new Declarative Automation Bundle (formerly Databricks Asset Bundle) for a streaming pipeline. They need to ensure the configuration is syntactically correct, push the code to the workspace, and finally trigger the pipeline to verify it runs as expected. Which sequence of Databricks CLI commands should the engineer use to perform these steps in the correct order?

A. databricks bundle check -> databricks bundle upload -> databricks bundle start
B. databricks bundle validate -> databricks bundle sync -> databricks bundle execute
C. databricks bundle validate -> databricks bundle deploy -> databricks bundle run
D. databricks bundle init -> databricks bundle deploy -> databricks bundle run

Answer: C

Explanation:
databricks bundle validate checks the bundle configuration, databricks bundle deploy uploads and deploys its resources to the workspace, and databricks bundle run triggers the deployed pipeline.

NEW QUESTION # 123
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