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Databricks Databricks-Certified-Data-Engineer-Associate Exam Overview:

Certification Vendor:Databricks
Exam Name:Databricks Certified Data Engineer Associate Exam
Exam Number:Databricks-Certified-Data-Engineer-Associate
Related Certifications:Databricks Certified Data Engineer Professional
Databricks Certified Data Analyst Associate
Available Languages:Portuguese (Brazil), English, Japanese, Korean
Exam Price:200 USD
Certificate Validity Period:2 years
Real Exam Qty:45
Passing Score:Not publicly disclosed, approximately 70%
Exam Duration:90 minutes
Exam Format:Multiple choice, Proctored
Recommended Training:Databricks Academy - Data Engineer Associate Learning Path
Databricks Documentation
Exam Registration:Official Registration
Sample Questions:Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions
Exam Way:Online proctored or in-person at authorized test centers
Pre Condition:No mandatory prerequisites; 6+ months hands-on experience with Databricks and data engineering tasks recommended
Official Syllabus URL:https://www.databricks.com/learn/certification/data-engineer-associate

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

TopicDetails
Topic 1
  • 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 2
  • 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 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
  • 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 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

Databricks Certified Data Engineer Associate Sample Questions:

Question #1

A data engineer ingests semi-structured JSON logs into a Delta table using Auto Loader with schema evolution enabled. A new string field userAgent appears in the JSON. What happens to the new userAgent field?

  • A. The new userAgent field is silently ignored, and its data is dropped for all new files.
  • B. All existing data is backfilled with a default value for userAgent and made non-nullable.
  • C. The new userAgent field is added as a new nullable column in the Delta table, populated for new data.
  • D. Ingestion fails with a schema-mismatch error until the table is altered.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #2

A data engineer needs to optimize the data layout and query performance for an e-commerce transactions Delta table. The table is partitioned by "purchase_date" a date column which helps with time-based queries but does not optimize searches on user statistics "customer_id", a high- cardinality column.
The table is usually queried with filters on "customer_id" within specific date ranges, but since this data is spread across multiple files in each partition, it results in full partition scans and increased runtime and costs.
How should the data engineer optimize the Data Layout for efficient reads?

  • A. Alter the table implementing liquid clustering by "customer_id" and "purchase_date".
  • B. Alter table implementing liquid clustering on "customer_id" while keeping the existing partitioning.
  • C. Alter the table to partition by "customer_id".
  • D. Enable delta caching on the cluster so that frequent reads are cached for performance.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #3

A data engineer must prevent rows with a null order_id from ever being written to a Delta table, causing the write to fail if violated.
Which command enforces this?

  • A. ALTER TABLE orders ALTER COLUMN order_id DROP NOT NULL
  • B. ALTER TABLE orders SET TBLPROPERTIES ('delta.enforceNotNull' = 'true')
  • C. CREATE TABLE orders (order_id INT COMMENT 'required')
  • D. ALTER TABLE orders ADD CONSTRAINT id_nn CHECK (order_id IS NOT NULL)
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #4

A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema. Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

  • A. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;
  • B. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
  • C. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
  • D. GRANT USE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #5

A data engineer is designing a streaming pipeline and wants to limit how long Spark maintains state information for aggregation queries. Which Structured Streaming feature defines how long late data can be processed?

  • A. Cache
  • B. Partition
  • C. Checkpoint
  • D. Watermark
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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