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Databricks Certified Data Engineer Associate certification exam is a computer-based exam that consists of 60 multiple-choice questions. Candidates are given two hours to complete the exam, and they must score at least 70% to pass. Databricks-Certified-Data-Engineer-Associate exam is available in multiple languages, including English, Spanish, French, German, and Japanese.

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Databricks-Certified-Data-Engineer-Associate Test Questions Answers - Original Databricks-Certified-Data-Engineer-Associate Questions

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a challenging and rewarding certification exam that can help data engineers to validate their skills and knowledge in working with Databricks. Databricks Certified Data Engineer Associate Exam certification is recognized by leading organizations and can help data engineers to advance their careers. If you are a data engineer who is interested in working with Databricks, then this certification exam is definitely worth considering.

Databricks Certified Data Engineer Associate Exam covers a range of topics related to data engineering, including data ingestion, ETL (extract, transform, load) processes, data modeling, and data warehousing. Databricks-Certified-Data-Engineer-Associate exam also covers Databricks-specific tools and technologies, such as Databricks Delta Lake and Databricks Runtime. Passing the exam requires a deep understanding of these topics and the ability to apply that knowledge in real-world scenarios. Successful candidates will have demonstrated their ability to design and implement reliable, scalable, and efficient data pipelines using Databricks.

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

NEW QUESTION # 51
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?

A. Both teams would be able to collaborate on projects in real-time
B. Both teams would respond more quickly to ad-hoc requests
C. Both teams would use the same source of truth for their work
D. Both teams would reorganize to report to the same department
E. Both teams would autoscale their work as data size evolves

Answer: C

Explanation:
A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. By using a data lakehouse, both the data analysis and data engineering teams can access the same data sources and formats, ensuring data consistency and quality across their reports. A data lakehouse also supports schema enforcement and evolution, data validation, and time travel to old table versions, which can help resolve data conflicts and errors1. References: 1: What is a Data Lakehouse? - Databricks 2: What is a data lakehouse? | IBM

NEW QUESTION # 52
Which of the following benefits is provided by the array functions from Spark SQL?

A. An ability to work with data in a variety of types at once
B. An ability to work with time-related data in specified intervals
C. An ability to work with an array of tables for procedural automation
D. An ability to work with data within certain partitions and windows
E. An ability to work with complex, nested data ingested from JSON files

Answer: E

Explanation:
Explanation
Array functions in Spark SQL are primarily used for working with arrays and complex, nested data structures, such as those often encountered when ingesting JSON files. These functions allow you to manipulate and query nested arrays and structures within your data, making it easier to extract and work with specific elements or values within complex data formats. While some of the other options (such as option A for working with different data types) are features of Spark SQL or SQL in general, array functions specifically excel at handling complex, nested data structures like those found in JSON files.

NEW QUESTION # 53
A data engineer is attempting to drop a Spark SQL table my_table. The data engineer wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?

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

Answer: A

NEW QUESTION # 54
A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?

A. Databricks SQL
B. Delta Lake
C. Data Explorer
D. Unity Catalog
E. Auto Loader

Answer: E

Explanation:
Explanation
Auto Loader incrementally and efficiently processes new data files as they arrive in cloud storage without any additional setup.https://docs.databricks.com/en/ingestion/auto-loader/index.html

NEW QUESTION # 55
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?

A. spark.sql
B. SELECT * FROM sales
C. spark.table
D. There is no way to share data between PySpark and SQL.
E. spark.delta.table

Answer: A

Explanation:
The spark.sql operation allows the data engineering team to run a SQL query and return the result as a PySpark DataFrame. This way, the data engineering team can use the same query that the data analyst has developed and operate with the results in PySpark. For example, the data engineering team can use spark.sql("SELECT * FROM sales") to get a DataFrame of all the records from the sales Delta table, and then apply various tests or transformations using PySpark APIs. The other options are either not valid operations (A, D), not suitable for running a SQL query (B, E), or not returning a DataFrame (A). References: Databricks Documentation - Run SQL queries, Databricks Documentation - Spark SQL and DataFrames.

NEW QUESTION # 56
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