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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q113-Q118):

NEW QUESTION # 113
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
B. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
C. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
D. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.
E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.

Answer: D

Explanation:
1: In Production mode, the pipeline runs continuously and updates the output tables whenever new data is available in the input sources. The compute resources are allocated on demand and released when the pipeline is stopped. This mode is suitable for production workloads that require high availability and reliability. Reference: Configure pipeline settings for Delta Live Tables, Tutorial: Run your first Delta Live Tables pipeline, Building Reliable Data Pipelines Using DataBricks' Delta Live Tables

NEW QUESTION # 114
A data engineer wants to create a new table containing the names of customers who live in France.
They have written the following command:
CREATE TABLE customersInFrance
_____ AS
SELECT id,
firstName,
lastName
FROM customerLocations
WHERE country = 'FRANCE';
A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (Pll).
Which line of code fills in the above blank to successfully complete the task?

A. TBLPROPERTIES PII
B. 0
C. "COMMENT PII"
D. COMMENT "Contains PIT

Answer: A

Explanation:
To include a property indicating that a table contains personally identifiable information (PII), the TBLPROPERTIES keyword is used in SQL to add metadata to a table. The correct syntax to define a table property for PII is as follows:
CREATE TABLE customersInFrance
USING DELTA
TBLPROPERTIES ('PII' = 'true')
AS
SELECT id,
firstName,
lastName
FROM customerLocations
WHERE country = 'FRANCE';
The TBLPROPERTIES ('PII' = 'true') line correctly sets a table property that tags the table as containing personally identifiable information. This is in accordance with organizational policies for handling sensitive information.
Reference:
Databricks documentation on Delta Lake: Delta Lake on Databricks

NEW QUESTION # 115
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 window functions for rolling and ranking calculations.
C. Using mapPartitions to manually implement rolling windows.
D. Collecting the DataFrame to the driver for faster iterative processing.
E. Using RDD aggregations for better performance.

Answer: A,B

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 # 116
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
B. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
C. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
D. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.

Answer: D

NEW QUESTION # 117
A data engineer team has decided to implement a new data platform on Databricks and is currently deciding how to store each kind of data on each data layer. What is the appropriate layer and data pairing for medallion architecture?

A. Silver Layer - Raw data from deposit account application
B. Gold Layer - Deduplicated money transfer transaction
C. Silver Layer - Cleansed master customer data
D. Bronze Layer - Summary of cash deposit amount for each country and city

Answer: C

Explanation:
In the Medallion architecture, the Silver layer holds cleansed and conformed data (such as master customer data), which has been cleaned, validated, and standardized after ingestion from the Bronze layer.

NEW QUESTION # 118
......

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