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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q287-Q292):

NEW QUESTION # 287
Which compute option should be chosen in a scenario where small-scale ad-hoc Python scripts need to be run at high frequency and should wind down quickly after these queries have finished running?

A. SQL Warehouse
B. Serverless Compute
C. Job Cluster
D. All-purpose Cluster

Answer: B

Explanation:
Serverless Compute is ideal for small-scale, frequent, ad-hoc Python scripts because it provisions automatically, scales quickly, and terminates when queries finish, minimizing operational overhead and costs.

NEW QUESTION # 288
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which change will need to be made to the pipeline when migrating to Delta Live Tables?

A. The pipeline will need to be written entirely in Python.
B. The pipeline can have different notebook sources in SQL & Python.
C. The pipeline will need to be written entirely in SQL.
D. The pipeline will need to use a batch source in place of a streaming source.

Answer: B

Explanation:
When migrating to Delta Live Tables (DLT) with a data pipeline that involves different programming languages across various data layers, the migration does not require unifying the pipeline into a single language. Delta Live Tables support multi-language pipelines, allowing data engineers and data analysts to work in their preferred languages, such as Python for data engineering tasks (raw, bronze, and silver layers) and SQL for data analytics tasks (gold layer). This capability is particularly beneficial in collaborative settings and leverages the strengths of each language for different stages of data processing.
References:Databricks documentation on Delta Live Tables: Delta Live Tables Guide

NEW QUESTION # 289
A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

A. Option A
B. Option B
C. Option E
D. Option D
E. Option C

Answer: C

Explanation:
Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks

NEW QUESTION # 290
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. FROM CSV
B. FROM "path/to/csv"
C. None of these lines of code are needed to successfully complete the task
D. USING DELTA
E. USING CSV

Answer: B

Explanation:
A data lakehouse is a new paradigm that can be used to simplify and unify siloed data architectures that are specialized for specific use cases. A data lakehouse combines the best of both data lakes and data warehouses, providing a single platform that supports diverse data types, open standards, low-cost storage, high-performance queries, ACID transactions, schema enforcement, and governance. A data lakehouse enables data engineers to build reliable and scalable data pipelines that can serve various downstream applications and users, such as data science, machine learning, analytics, and reporting. A data lakehouse leverages the power of Delta Lake, a storage layer that brings reliability and performance to data lakes. References: What is a data lakehouse?, Delta Lake, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics

NEW QUESTION # 291
A data engineer investigates a slow stage in the Databricks Spark UI. The engineer scrolls down to the Summary Metrics section for task durations, and they observe for that stage:
25th percentile duration: 40 seconds
Median duration: 45 seconds
75th percentile duration: 50 seconds
Max duration: 80 seconds
What is the interpretation of these metrics?

A. There is a GC issue.
B. The stage is healthy.
C. The stage likely suffers from data skew.
D. The cluster is under-provisioned.

Answer: C

Explanation:
The maximum task duration is 60% higher than the 75th percentile (80s versus 50s). Databricks identifies this pattern as a likely indication that a small number of tasks process disproportionately large partitions-data skew.

NEW QUESTION # 292
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