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

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

>> Databricks-Certified-Data-Engineer-Associate Zertifizierungsantworten <<

Databricks-Certified-Data-Engineer-Associate Pass Dumps & PassGuide Databricks-Certified-Data-Engineer-Associate Prüfung & Databricks-Certified-Data-Engineer-Associate Guide

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Databricks Certified Data Engineer Associate Exam Databricks-Certified-Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q280-Q285):

280. Frage
A data engineer is joining a very large events_df (billions of rows) with a small country_lookup_df (fewer than 10,000 rows) on the country_code column. The join is slow due to data shuffling. How should the data engineer improve performance?

A. broadcast events_df when performing the join
B. broadcast country_lookup_df when performing the join
C. Cache both DataFrames, then run the same join
D. Persist both DataFrames, then run the same join

Antwort: B

Begründung:
Broadcasting country_lookup_df sends the small lookup table to each executor, avoiding a large shuffle of the billions-row events_df and improving join performance.

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

Antwort: E

Begründung:
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]

282. Frage
Which of the following benefits is provided by the array functions from Spark SQL?

A. An ability to work with data within certain partitions and windows
B. An ability to work with data in a variety of types at once
C. An ability to work with time-related data in specified intervals
D. An ability to work with an array of tables for procedural automation
E. An ability to work with complex, nested data ingested from JSON files

Antwort: E

Begründung:
The array functions from Spark SQL are a subset of the collection functions that operate on array columns1. They provide an ability to work with complex, nested data ingested from JSON files or other sources2. For example, the explode function can be used to transform an array column into multiple rows, one for each element in the array3. The array_contains function can be used to check if a value is present in an array column4. The array_join function can be used to concatenate all elements of an array column with a delimiter. These functions can be useful for processing JSON data that may have nested arrays or objects. References: 1: Spark SQL, Built-in Functions - Apache Spark 2: Spark SQL Array Functions Complete List - Spark By Examples 3: Spark SQL Array Functions - Syntax and Examples - DWgeek.com 4: Spark SQL, Built-in Functions - Apache Spark : Spark SQL, Built-in Functions - Apache Spark : [Working with Nested Data Using Higher Order Functions in SQL on Databricks - The Databricks Blog]

283. Frage
A data engineer has developed a Python notebook in a Databricks workspace and has configured it to run as a scheduled Job to process daily sales data. How are the storage and execution of this notebook managed within the Databricks architecture?

A. The notebook is stored unencrypted in the workspace storage bucket, and the code executes on the cluster driver node in the compute plane when the job runs.
B. The notebook is stored securely and encrypted in Unity Catalog, and the code executes in Delta Lake when the job runs.
C. The notebook is stored securely and encrypted in the control plane, and the code executes in the compute plane when the job runs.
D. The notebook is stored securely and encrypted in the compute plane, and the code executes in the control plane when the job runs.

Antwort: C

Begründung:
Databricks workspace notebooks and their metadata are securely stored and encrypted in the control plane. When the scheduled job runs, the notebook code is executed on Databricks compute in the compute plane, where data processing occurs.

284. Frage
A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema.
Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

Antwort: C

285. Frage
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