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

Section	Objectives
Data Ingestion and ELT Development	- Data ingestion using Spark SQL and PySpark - ETL patterns and transformations - Handling structured and semi-structured data
Data Processing and Transformations	- Delta Lake fundamentals (tables, transactions, optimization) - PySpark DataFrame transformations - User-defined functions (UDFs) - Apache Spark SQL operations (joins, aggregations, filtering)
Databricks Lakehouse Platform Fundamentals	- Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts
Productionizing Data Pipelines	- Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization - Scheduling and monitoring jobs
Data Governance and Quality	- Data access control and governance - Data quality concepts and management - Unity Catalog basics

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

Answer: C

Explanation:
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/

NEW QUESTION # 13
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
MERGE SELECT * FROM april_transactions;
C. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION 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
OUTER JOIN SELECT * FROM april_transactions;

Answer: C

NEW QUESTION # 14
A data engineer at a company that uses Databricks with Unity Catalog needs to share a collection of tables with an external partner who also uses a Databricks workspace enabled for Unity Catalog. The data engineer decides to use Delta Sharing to accomplish this. What is the first piece of information the data engineer should request from the external partner to set up Delta Sharing?

A. The name of their Databricks cluster
B. The sharing identifier of their Unity Catalog metastore
C. Their Databricks account password
D. The IP address of their Databricks workspace

Answer: B

Explanation:
To configure Delta Sharing between two Databricks workspaces with Unity Catalog, the first requirement is the sharing identifier of the partner's Unity Catalog metastore, which enables secure setup of the share and recipient relationship.

NEW QUESTION # 15
A data engineer needs to apply custom logic to identify employees with more than 5 years of experience in array column employees in table stores. The custom logic should create a new column exp_employees that is an array of all of the employees with more than 5 years of experience for each row. In order to apply this custom logic at scale, the data engineer wants to use the FILTER higher-order function.
Which of the following code blocks successfully completes this task?

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

Answer: B

Explanation:
Option A is the correct answer because it uses the FILTER higher-order function correctly to filter out employees with more than 5 years of experience from the array column "employees". It applies a lambda function i -> i.years_exp > 5 that checks if the years of experience of each employee in the array is greater than 5. If this condition is met, the employee is included in the new array column "exp_employees".
References: The use of higher-order functions like FILTER can be referenced from Databricks documentation on Higher-Order Functions.

NEW QUESTION # 16
A company uses Delta Sharing to collaborate with partners across different cloud providers and geographic regions. What will result in additional costs due to cross-region or egress fees?

A. Utilizing Delta Sharing for internal data analytics within a single cloud environment
B. Sharing data within the same cloud provider and region
C. Accessing Delta Sharing data using a VPN within the same data center
D. Transferring data via Delta Sharing across clouds and across different geographic regions

Answer: D

Explanation:
Databricks documents that Delta Sharing does not require data replication, but cloud providers can still charge data egress fees when data is shared across clouds or across geographic regions. Databricks specifically states that sharing within the same region incurs no egress cost, while cross-cloud or cross-region transfers can create additional charges from the underlying cloud provider. That means option A is correct. Options B and C describe same-cloud or same-region use cases that do not trigger the cross-region/cross-cloud egress pattern Databricks calls out. Option D is unrelated to the main documented billing driver for Delta Sharing costs. The important principle is that Delta Sharing itself is designed to avoid replication overhead, but the physical movement of data between cloud boundaries or regions can still result in vendor networking charges. Therefore, when sharing data with external partners in other regions or clouds, engineers should plan for possible egress costs and monitor them accordingly.
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NEW QUESTION # 17
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