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

Section	Weight	Objectives
Development and Ingestion	30%	- Data ingestion patterns and methods 1. Delta Lake basics and usage 2. Batch and streaming ingestion 3. Connecting to external data sources - Notebook development fundamentals 1. Using PySpark and Spark SQL 2. Data exploration and validation
Data Processing & Transformations	31%	- Data transformation techniques 1. Complex data processing logic 2. DataFrame operations and transformations 3. Aggregations, joins, and window functions - Query optimization and performance 1. Understanding query plans 2. Optimization strategies
Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Data layout and optimization: partitioning, file sizing, caching 2. Workspace navigation and management 3. Compute options: clusters, SQL warehouses, serverless
Productionizing Data Pipelines	18%	- Workflow orchestration 1. Lakeflow Jobs creation and management 2. Scheduling, triggers, and dependencies - Deployment and CI/CD 1. Databricks Asset Bundles 2. Version control integration - Monitoring and troubleshooting 1. Logging and error handling 2. Pipeline reliability and recovery
Data Governance & Quality	11%	- Unity Catalog implementation 1. Data governance model 2. Permissions and access control - Data quality and reliability 1. Schema enforcement and evolution 2. Data validation and quality checks

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q29-Q34):

NEW QUESTION # 29
A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?

A. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
B. They can create a new job from scratch and add both tasks to run concurrently.
C. They can clone the existing task to a new Job and then edit it to run the new notebook.
D. They can clone the existing task in the existing Job and update it to run the new notebook.
E. They can create a new task in the existing Job and then add it as a dependency of the original task.

Answer: C

NEW QUESTION # 30
An organization has implemented a data pipeline in Databricks and needs to ensure it can scale automatically based on varying workloads without manual cluster management. The goal is to meet the company's Service Level Agreements (SLAs), which require high availability and minimal downtime, while Databricks automatically handles resource allocation and optimization.
Which approach fulfills these requirements?

A. Deploy job clusters with fixed configurations, dedicated to specific tasks, without automatic scaling
B. Use Serverless compute in Databricks to automatically scale and provision resources with minimal manual intervention
C. Use interactive clusters in Databricks, adjusting cluster sizes manually based on workload demands
D. Use spot instances to allocate resources dynamically while minimizing costs, with potential interruptions

Answer: B

Explanation:
Databricks documentation recommends serverless compute as the simplest and most reliable compute option when the workload is supported. Serverless compute is designed to automatically provision resources, scale with demand, reduce infrastructure management, and apply platform optimizations without requiring users to configure clusters manually. This directly supports the requirement for automatic scaling, reduced operational overhead, and better alignment with strict SLAs. Databricks also states that serverless compute is always available and scales according to workload, making it a strong fit for organizations seeking high availability and minimal downtime. Fixed-configuration job clusters in option B still require manual sizing decisions and do not meet the "no manual cluster management" requirement. Spot instances in option C may reduce costs but can be interrupted, which makes them a poor choice when reliability is a top requirement. Interactive clusters in option D are intended more for development and exploration and still need manual management. Based on Databricks guidance, serverless compute is the correct choice.
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NEW QUESTION # 31
A BI team runs short, highly concurrent SQL queries against gold tables and needs sub-second startup with no cluster management or idle cost.
Which compute option best fits this requirement?

A. A classic SQL warehouse with auto-stop disabled
B. An all-purpose cluster with autoscaling and a 120-minute auto-termination setting
C. A single-node job cluster started per query
D. A serverless SQL warehouse

Answer: D

Explanation:
Serverless SQL warehouses start in seconds from a Databricks-managed compute pool, scale automatically with query concurrency, and stop billing when idle. All-purpose and classic warehouses require cluster provisioning time and can incur idle cost while waiting for auto- termination.

NEW QUESTION # 32
A data engineer is 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 reliable completion of the pipeline are the highest priorities.
Which type of Databricks cluster should the data engineer configure?

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

Answer: A

Explanation:
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.

NEW QUESTION # 33
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?

A. They can turn on the Auto Stop feature for the SQL endpoint.
B. They can increase the cluster size of the SQL endpoint.
C. They can turn on the Serverless feature for the SQL endpoint.
D. They can increase the maximum bound of the SQL endpoint's scaling range
E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized."

Answer: C

Explanation:
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

NEW QUESTION # 34
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