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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q102-Q107):

NEW QUESTION # 102
A data engineering team runs nightly batch ETL pipelines that transform large volumes of data stored in Delta Lake. The workloads are predictable and run on a fixed schedule. The team wants automatic scaling and cost efficiency, paying only for compute used during job execution. Which Databricks compute option is appropriate for this workload?

A. An all-purpose compute cluster configured with autoscaling.
B. A job compute cluster triggered by a scheduled workflow.
C. A single-node compute cluster configured without autoscaling.
D. A Databricks SQL warehouse configured for serverless execution.

Answer: B

Explanation:
Job compute is designed for scheduled, automated workloads. It can autoscale for the ETL workload and terminates after the job finishes, avoiding the idle cost of persistent all-purpose compute.

NEW QUESTION # 103
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. Use interactive clusters in Databricks, adjusting cluster sizes manually based on workload demands
B. Deploy job clusters with fixed configurations, dedicated to specific tasks, without automatic scaling
C. Use spot instances to allocate resources dynamically while minimizing costs, with potential interruptions
D. Use Serverless compute in Databricks to automatically scale and provision resources with minimal manual intervention

Answer: D

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 # 104
Which of the following data lakehouse features results in improved data quality over a traditional data lake?

A. A data lakehouse supports ACID-compliant transactions.
B. A data lakehouse stores data in open formats.
C. A data lakehouse allows the use of SQL queries to examine data.
D. A data lakehouse enables machine learning and artificial Intelligence workloads.
E. A data lakehouse provides storage solutions for structured and unstructured data.

Answer: A

NEW QUESTION # 105
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when it is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
B. They can set up the dashboard's SQL endpoint to be serverless.
C. They can turn on the Auto Stop feature for the SQL endpoint.
D. They can reduce the cluster size of the SQL endpoint.
E. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.

Answer: C

Explanation:
The Auto Stop feature allows the SQL endpoint to automatically stop after a specified period of inactivity. This can help reduce the cost and resource consumption of the SQL endpoint, as it will only run when it is needed to refresh the dashboard or execute queries. The data engineer can configure the Auto Stop setting for the SQL endpoint from the SQL Endpoints UI, by selecting the desired idle time from the Auto Stop dropdown menu. The default idle time is 120 minutes, but it can be set to as low as 15 minutes or as high as 240 minutes. Alternatively, the data engineer can also use the SQL Endpoints REST API to set the Auto Stop setting programmatically. Reference: SQL Endpoints UI, SQL Endpoints REST API, Refreshing SQL Dashboard

NEW QUESTION # 106
Which of the following is hosted completely in the control plane of the classic Databricks architecture?

A. Databricks web application
B. Worker node
C. Driver node
D. JDBC data source
E. Databricks Filesystem

Answer: A

Explanation:
The control plane in the classic Databricks architecture is responsible for managing the Databricks workspace, user and group management, and cluster management, among other things. The Databricks web application is a part of the control plane that enables users to interact with the workspace, create and manage clusters, and work with notebooks, jobs, and data.
Worker nodes and driver nodes are part of the data plane, which is responsible for executing data processing tasks. JDBC data sources and the Databricks Filesystem are services that are used by both the control plane and the data plane.

NEW QUESTION # 107
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