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

Section	Weight	Objectives
Spark SQL and DataFrames	15-20%	- Handle null values and data quality - Write and execute Spark SQL queries - Join and union DataFrames - Aggregate and group data
Apache Spark Data Processing Fundamentals	20-25%	- Work with structured data types (arrays, maps, structs) - Use Spark SQL for data processing - Create and use Spark DataFrames - Apply transformations and actions on DataFrames
Data Pipeline Architecture	15-20%	- Understand ELT vs ETL patterns - Monitor and optimize pipeline performance - Design data pipelines for batch and streaming - Implement incremental data processing
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 - Implement user-defined functions (UDFs) - Use PySpark for data processing
Delta Lake Fundamentals	20-25%	- Write to and read from Delta tables - Understand ACID transactions and time travel - Explain Delta Lake features and benefits - Create and manage Delta tables

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

Databricks-Certified-Data-Engineer-Associate Testfagen - Databricks-Certified-Data-Engineer-Associate Simulationsfragen

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Databricks Certified Data Engineer Associate Exam Databricks-Certified-Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q84-Q89):

84. Frage
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. FROM CSV
B. None of these lines of code are needed to successfully complete the task
C. FROM "path/to/csv"
D. USING DELTA
E. USING CSV

Antwort: C

Begründung:
A data lakehouse is a new paradigm that can be used to simplify and unify siloed data architectures that are specialized for specific use cases. A data lakehouse combines the best of both data lakes and data warehouses, providing a single platform that supports diverse data types, open standards, low-cost storage, high-performance queries, ACID transactions, schema enforcement, and governance. A data lakehouse enables data engineers to build reliable and scalable data pipelines that can serve various downstream applications and users, such as data science, machine learning, analytics, and reporting. A data lakehouse leverages the power of Delta Lake, a storage layer that brings reliability and performance to data lakes. Reference: What is a data lakehouse?, Delta Lake, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics

85. Frage
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?

A. "COMMENT PII"
B. PII
C. There is no way to indicate whether a table contains PII.
D. COMMENT "Contains PII"
E. TBLPROPERTIES PII

Antwort: D

Begründung:
Explanation
Ref:https://www.databricks.com/discover/pages/data-quality-management
CREATE TABLE my_table (id INT COMMENT 'Unique Identification Number', name STRING COMMENT 'PII', age INT COMMENT 'PII') TBLPROPERTIES ('contains_pii'=True) COMMENT 'Contains PII';

86. Frage
A data engineer is decommissioning a sandbox schema in Unity Catalog. Some tables are ephemeral staging outputs that can be safely removed entirely, but a few tables point at shared cloud storage used by downstream jobs outside Databricks. The engineer must avoid deleting any shared files when cleaning up catalog objects.
How does Unity Catalog behave when dropping Managed vs External tables?

A. Drop managed tables that are ephemeral and drop external tables; files for both remain for 7 days
B. Drop external tables first to delete their files, then drop managed tables to keep their files for recovery
C. Drop managed staging tables to remove data and metadata, and drop external tables to remove only metadata
D. Drop all tables; Databricks will only remove metadata for both managed and external tables

Antwort: C

Begründung:
In Unity Catalog, managed tables and external tables have different lifecycle behavior. When you drop a managed table , Databricks removes the table metadata and also schedules deletion of the underlying managed data files according to the managed-table retention behavior. By contrast, when you drop an external table , Unity Catalog removes only the table metadata and does not delete the underlying files in cloud storage. This distinction exists specifically so that external data can remain available to systems outside Databricks. In the scenario described, ephemeral staging outputs that should disappear completely are best represented by managed tables, while tables backed by shared cloud storage should be external tables so that dropping them does not remove files used by downstream jobs. Therefore, option C is the correct answer.
Options A, B, and D contradict documented Unity Catalog behavior because external table files are not automatically deleted, while managed table data is lifecycle-managed by Databricks.
=========

87. Frage
A data engineer runs a statement every day to copy the previous day's sales into the table transactions. Each day's sales are in their own file in the location "/transactions/raw".
Today, the data engineer runs the following command to complete this task:

After running the command today, the data engineer notices that the number of records in table transactions has not changed.
Which of the following describes why the statement might not have copied any new records into the table?

A. The names of the files to be copied were not included with the FILES keyword.
B. The PARQUET file format does not support COPY INTO.
C. The previous day's file has already been copied into the table.
D. The COPY INTO statement requires the table to be refreshed to view the copied rows.
E. The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.

Antwort: C

Begründung:
Explanation
https://docs.databricks.com/en/ingestion/copy-into/index.html The COPY INTO SQL command lets you load data from a file location into a Delta table. This is a re-triable and idempotent operation; files in the source location that have already been loaded are skipped. if there are no new records, the only consistent choice is C no new files were loaded because already loaded files were skipped.

88. Frage
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 at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.
B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
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 once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
E. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

Antwort: B

Begründung:
Explanation
You can optimize pipeline execution by switching between development and production modes. Use the Delta Live Tables Environment Toggle Icon buttons in the Pipelines UI to switch between these two modes. By default, pipelines run in development mode.
When you run your pipeline in development mode, the Delta Live Tables system does the following:
Reuses a cluster to avoid the overhead of restarts. By default, clusters run for two hours when development mode is enabled. You can change this with the pipelines.clusterShutdown.delay setting in the Configure your compute settings.
Disables pipeline retries so you can immediately detect and fix errors.
In production mode, the Delta Live Tables system does the following:
Restarts the cluster for specific recoverable errors, including memory leaks and stale credentials.
Retries execution in the event of specific errors, for example, a failure to start a cluster.
https://docs.databricks.com/en/delta-live-tables/updates.html#optimize-execution

89. Frage
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