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

Section	Weight	Objectives
Topic 1: Data Processing & Transformations	31%	- Query optimization and performance 1. Optimization strategies 2. Understanding query plans - Data transformation techniques 1. Complex data processing logic 2. Aggregations, joins, and window functions 3. DataFrame operations and transformations
Topic 2: 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
Topic 3: Development and Ingestion	30%	- Notebook development fundamentals 1. Using PySpark and Spark SQL 2. Data exploration and validation - Data ingestion patterns and methods 1. Delta Lake basics and usage 2. Connecting to external data sources 3. Batch and streaming ingestion
Topic 4: Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Workspace navigation and management 2. Data layout and optimization: partitioning, file sizing, caching 3. Compute options: clusters, SQL warehouses, serverless
Topic 5: Productionizing Data Pipelines	18%	- Monitoring and troubleshooting 1. Logging and error handling 2. Pipeline reliability and recovery - Deployment and CI/CD 1. Databricks Asset Bundles 2. Version control integration - 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 (Q239-Q244):

NEW QUESTION # 239
A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail transactions in the month of April. There are no duplicate records between the tables.
Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?

A. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INTERSECT SELECT * from april_transactions;
B. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INNER JOIN SELECT * FROM april_transactions;
C. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
OUTER JOIN SELECT * FROM april_transactions;
D. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION SELECT * FROM april_transactions;
E. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
MERGE SELECT * FROM april_transactions;

Answer: D

Explanation:
The correct command to create a new table that contains all records from two tables without duplicate records is to use the UNION operator. The UNION operator combines the results of two queries and removes any duplicate rows. The INNER JOIN, OUTER JOIN, and MERGE operators do not remove duplicate rows, and the INTERSECT operator only returns the rows that are common to both tables. Therefore, option B is the only correct answer. References: Databricks SQL Reference - UNION, Databricks SQL Reference - JOIN, Databricks SQL Reference - MERGE, [Databricks SQL Reference - INTERSECT]

NEW QUESTION # 240
What is the primary function of the Silver layer in the Databricks medallion architecture?

A. Validate, clean, and deduplicate data for further processing
B. Store historical data solely for auditing purposes
C. Aggregate and enrich data for business analytics
D. lngest raw data in its original state

Answer: A

NEW QUESTION # 241
A data engineer has a Python notebook in Databricks, but they need to use SQL to accomplish a specific task within a cell. They still want all of the other cells to use Python without making any changes to those cells.
Which of the following describes how the data engineer can use SQL within a cell of their Python notebook?

A. They can attach the cell to a SQL endpoint rather than a Databricks cluster
B. It is not possible to use SQL in a Python notebook
C. They can add %sql to the first line of the cell
D. They can simply write SQL syntax in the cell
E. They can change the default language of the notebook to SQL

Answer: C

NEW QUESTION # 242
A data engineer needs to compute metrics such as:
- rolling 7-day sales
- percent of total revenue
- top-N most profitable categories
Which two approaches are appropriate for implementing these metrics with PySpark DataFrames? (Choose two.)

A. Using joins and groupBy to compute percent-of-total metrics.
B. Using mapPartitions to manually implement rolling windows.
C. Using window functions for rolling and ranking calculations.
D. Using RDD aggregations for better performance.
E. Collecting the DataFrame to the driver for faster iterative processing.

Answer: A,C

Explanation:
Window functions are the correct approach for rolling calculations and ranking tasks such as rolling 7-day sales and top-N categories. Joins and groupBy are appropriate for percent-of-total calculations by first computing total revenue at the needed level and then comparing each subgroup against that total.

NEW QUESTION # 243
A data engineer needs access to a table new_table, but they do not have the correct permissions.
They can ask the table owner for permission, but they do not know who the table owner is.
Which of the following approaches can be used to identify the owner of new_table?

A. Review the Owner field in the table's page in the cloud storage solution
B. All of these options can be used to identify the owner of the table
C. Review the Owner field in the table's page in Data Explorer
D. There is no way to identify the owner of the table
E. Review the Permissions tab in the table's page in Data Explorer

Answer: C

NEW QUESTION # 244
......

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