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

Section	Weight	Objectives
Data Processing & Transformations	31%	- Data transformation techniques 1. DataFrame operations and transformations 2. Complex data processing logic 3. Aggregations, joins, and window functions - Query optimization and performance 1. Optimization strategies 2. Understanding query plans
Productionizing Data Pipelines	18%	- Monitoring and troubleshooting 1. Pipeline reliability and recovery 2. Logging and error handling - Workflow orchestration 1. Lakeflow Jobs creation and management 2. Scheduling, triggers, and dependencies - Deployment and CI/CD 1. Version control integration 2. Databricks Asset Bundles
Development and Ingestion	30%	- Notebook development fundamentals 1. Using PySpark and Spark SQL 2. Data exploration and validation - Data ingestion patterns and methods 1. Batch and streaming ingestion 2. Delta Lake basics and usage 3. Connecting to external data sources
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
Data Governance & Quality	11%	- Data quality and reliability 1. Schema enforcement and evolution 2. Data validation and quality checks - Unity Catalog implementation 1. Permissions and access control 2. Data governance model

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q51-Q56):

NEW QUESTION # 51
A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?

A. The table was external
B. The table did not have a location
C. The table was managed
D. The table's data was larger than 10 GB
E. The table's data was smaller than 10 GB

Answer: A

Explanation:
Explanation
managed tables files and metadata are managed by metastore and will be deleted when the table is dropped .
while external tables the metadata is stored in a external location.hence when a external table is dropped you clear off only the metadata and the files (data) remain.

NEW QUESTION # 52
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation cause the job to fail.
B. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
C. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
D. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
E. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.

Answer: A

Explanation:
Explanation
https://docs.databricks.com/en/delta-live-tables/expectations.html
Action
Result
warn (default)
Invalid records are written to the target; failure is reported as a metric for the dataset.
drop
Invalid records are dropped before data is written to the target; failure is reported as a metrics for the dataset.
fail
Invalid records prevent the update from succeeding. Manual intervention is required before re-processing.

NEW QUESTION # 53
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. COMMENT "Contains PIT
B. 0
C. TBLPROPERTIES PII
D. "COMMENT PII"

Answer: C

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 # 54
A data engineer is troubleshooting two different pipeline failures:
- Pipeline A fails with a java.lang.OutOfMemoryError is immediately
thrown after the command display(df.collect()) is called on a 100GB
dataset.
- Pipeline B fails during a wide transformation (a join of two large
tables) with an ExecutorLostFailure error message, indicating executor
memory exhaustion during shuffle.
Which action should the data engineer take to fix these two issues?

A. For Pipeline A, increase the spark.driver.memory; for Pipeline B, increase the spark.sql.shuffle.partitions or executor memory.
B. For both pipelines, enable Adaptive Query Execution (AQE) as it automatically manages memory allocation for both the driver and executors.
C. For Pipeline A, increase the number of executors; for Pipeline B, increase the driver memory.
D. For Pipeline A, switch to a Storage Optimized cluster; for Pipeline B, use a Compute Optimized cluster.

Answer: A

Explanation:
collect() moves the entire 100GB dataset to the driver, causing driver memory exhaustion, so Pipeline A requires avoiding collect() or increasing driver memory. Pipeline B fails on executors during a shuffle, so increasing shuffle partitions to reduce partition size or adding executor memory addresses the issue.

NEW QUESTION # 55
In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?

A. When the target table is an external table
B. When the source table can be deleted
C. When the target table cannot contain duplicate records
D. When the source is not a Delta table
E. When the location of the data needs to be changed

Answer: C

NEW QUESTION # 56
......

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