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

TopicDetails
Topic 1
  • Incremental Data Processing: In this topic questions about identifying Delta Lake, benefits of ACID transactions, a scenario to use an external table, location of a table, the benefits of Zordering, the kind of files, CTAS as a solution, the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE, and the necessary component to create a new DLT pipeline. Moreover, the topic also discusses directory structure of Delta Lake files, generated column, adding a table comment, and the benefits of the MERGE command.
Topic 2
  • Databricks Lakehouse Platform: This topic covers the relationship between the data lakehouse and the data warehouse, the improvement in data quality, comparing and contrasting silver and gold tables, elements of the Databricks Platform Architecture, and differentiating between all-purpose clusters and jobs clusters. Moreover, it identifies how cluster software is versioned, how clusters can be filtered, how to use multiple languages, how to run one notebook, how notebooks can be shared, Git operations, and limitations in Databricks Notebooks. Lastly, the topic describes how clusters are terminated, how to use multiple languages, and how Databricks Repos enables CI
  • CD workflows.
Topic 3
  • Data Governance: It identifies one of the four areas of data governance, Unity Catalog securables, and the cluster security modes. It also discusses how to create a UC-enabled all-purpose cluster and a DBSQL warehouse. The topic explains how to implement data object access control, create a DBSQL warehouse, and e a UC-enabled all-purpose cluster.
Topic 4
  • ELT with Apache Spark: It focuses on extracting data, identifying the prefix, creating a view, duplicating rows, creating a new table, utilizing the dot, parsing JSON, and defining a SQL UDF. Moreover, the topic delves into describing the security model, identifying the location of a function, and identifying the PIVOT.
Topic 5
  • Production Pipelines: It focuses on identifying the advantages of using multiple tasks in Jobs, a suitable scenario where predecessor task should be set up, CRON as an opportunity for scheduling opportunity, and how an alert can be sent via email. The topic also discusses setting up a predecessor task in Jobs, reviewing a task's execution history, and debugging a failed task. Lastly, it delves into setting up a retry policy in case of failure and creating an alert in the case of a failed task.

Reference: https://www.databricks.com/learn/certification/data-engineer-associate

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Databricks Certified Data Engineer Associate Sample Questions:

Identify how the count_if function and the count where x is null can be used.
Consider a table random_values with below data.
What would be the output of below query?
select count_if(col1 > 1) as count_a, count(*) as count_b,count(col1)
as count_c from random_values
col1
0
1
2
NULL
2
3

  • A. 4 6 6
  • B. 3 6 5
  • C. 3 6 6
  • D. 4 6 5
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?

  • A. Checkpointing and Write-ahead Logs
  • B. Write-ahead Logs and Idempotent Sinks
  • C. Structured Streaming cannot record the offset range of the data being processed in each trigger.
  • D. Replayable Sources and Idempotent Sinks
  • E. Checkpointing and Idempotent Sinks
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?

  • A. SELECT * FROM sales
  • B. spark.sql("sales")
  • C. There is no way to share data between PySpark and SQL.
  • D. spark.delta.table("sales")
  • E. spark.table("sales")
Reveal Solution  Discussion  0

Correct Answer: E  🗳️

A data engineer needs to append new records to an existing Delta table while preserving existing data. The ingestion pipeline runs every hour and adds incremental data without replacing previous records. Which write mode should be used?

  • A. overwrite
  • B. append
  • C. errorIfExists
  • D. ignore
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

A data engineer is decommissioning a sandbox schema in Unity Catalog. Some tables are ephemeral staging outputs that can be safety 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 all tables; Databricks will only remove metadata for both managed and external tables.
  • 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 managed tables that are ephemeral and DROP external tables; files for both remain for 7 days.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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