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

Section	Weight	Objectives
Topic 1: Spark SQL and DataFrames	15-20%	- Handle null values and data quality - Join and union DataFrames - Aggregate and group data - Write and execute Spark SQL queries
Topic 2: Lakehouse Platform Concepts	10-15%	- Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits - Explain data governance and security concepts
Topic 3: Data Pipeline Architecture	15-20%	- Implement incremental data processing - Monitor and optimize pipeline performance - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns
Topic 4: Delta Lake Fundamentals	20-25%	- Understand ACID transactions and time travel - Explain Delta Lake features and benefits - Write to and read from Delta tables - Create and manage Delta tables
Topic 5: Python for Data Engineering	10-15%	- Work with Spark APIs in Python - Use PySpark for data processing - Implement user-defined functions (UDFs)
Topic 6: Apache Spark Data Processing Fundamentals	20-25%	- Apply transformations and actions on DataFrames - Work with structured data types (arrays, maps, structs) - Create and use Spark DataFrames - Use Spark SQL for data processing

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q213-Q218):

NEW QUESTION # 213
A data engineer needs to ingest streaming data from Kafka into a Delta Lake table using Databricks. The pipeline must guarantee exactly-once processing semantics and maintain checkpoint information to recover from failures. Which Spark Structured Streaming feature ensures reliable processing and fault tolerance?

A. Partition pruning
B. Caching
C. Checkpointing
D. Watermarking

Answer: C

NEW QUESTION # 214
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.

The code block used by the data engineer is below:
Which line of code should the data engineer use to fill in the blank if the data engineer only wants the query to execute a micro-batch to process data every 5 seconds?

A. trigger(processingTime="5 seconds")
B. trigger("5 seconds")
C. trigger(once="5 seconds")
D. trigger(continuous="5 seconds")

Answer: A

NEW QUESTION # 215
A data engineer is working with two tables. Each of these tables is displayed below in its entirety.

The data engineer runs the following query to join these tables together:

Which of the following will be returned by the above query?

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

Answer: A

Explanation:
Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition is ON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns:
customer_id, name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.
The use of INNER JOIN can be referenced from Databricks documentation on SQL JOIN or from other sources like W3Schools or GeeksforGeeks.

NEW QUESTION # 216
Which of the following commands will return the number of null values in the member_id column?

A. SELECT count_if(member_id IS NULL) FROM my_table;
B. SELECT count(member_id) - count_null(member_id) FROM my_table;
C. SELECT null(member_id) FROM my_table;
D. SELECT count(member_id) FROM my_table;
E. SELECT count_null(member_id) FROM my_table;

Answer: A

Explanation:
Explanation
https://docs.databricks.com/en/sql/language-manual/functions/count.html Returns A BIGINT.
If * is specified also counts row containing NULL values.
If expr are specified counts only rows for which all expr are not NULL.
If DISTINCT duplicate rows are not counted.

NEW QUESTION # 217
A data engineer is developing an ETL process based on Spark SQL. The execution fails. The data engineer checks the Spark Ul and can see the ERRORS as follows:

Which two corrective actions should the data engineer perform to resolve this issue?
Choose 2 answers - (Q) Narrow the filters in order to collect less data in the query

A. Fix the shuffle partitions to 50 to ensure the allocation
B. Cache the dataset in order to boost the query performance
C. Upsize the worker nodes and activate autoshuffle partitions
D. Upsize the driver node and deactivate autoshuffle partitions

Answer: C,D

NEW QUESTION # 218
......

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