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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:	DE-A
Certificate Validity Period:	2 years
Exam Format:	Scenario-based questions, Proctored online or test center, Multiple-choice
Exam Duration:	90 minutes
Passing Score:	Approximately ~70% (not officially fixed/publicly guaranteed)
Related Certifications:	Databricks Certified Data Engineer Professional
Exam Price:	USD 200
Available Languages:	English
Real Exam Qty:	45 scored multiple-choice questions
Recommended Training:	Official Data Engineering with Databricks Course Databricks Data Engineering Learning Path (Databricks Academy)
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions
Exam Way:	Online proctored exam or test center
Pre Condition:	No formal prerequisites required; recommended ~6 months hands-on experience with Databricks, Spark SQL, and PySpark.
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-associate

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a certification program designed to assess an individual's expertise in data engineering using Databricks. Databricks-Certified-Data-Engineer-Associate Exam is designed to test the practical knowledge and skills required to work with big data using Databricks, a cloud-based data analytics platform.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q109-Q114):

NEW QUESTION # 109
A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level.
Which of the following tools can the data engineer use to solve this problem?

A. Unity Catalog
B. Data Explorer
C. Auto Loader
D. Delta Lake
E. Delta Live Tables

Answer: E

Explanation:
Delta Live Tables is a tool that enables data engineers to build and manage reliable data pipelines with minimal code. One of the features of Delta Live Tables is data quality monitoring, which allows data engineers to define quality expectations for their data and automatically check them at every step of the pipeline. Data quality monitoring can help detect and resolve data quality issues, such as missing values, duplicates, outliers, or schema changes. Data quality monitoring can also generate alerts and reports on the quality level of the data, and enable data engineers to troubleshoot and fix problems quickly. References: Delta Live Tables Overview, Data Quality Monitoring

NEW QUESTION # 110
Identify a scenario to use an external table.
A Data Engineer needs to create a parquet bronze table and wants to ensure that it gets stored in a specific path in an external location.
Which table can be created in this scenario?

A. A managed table where the location is pointing to specific path in external location.
B. An external table where the schema has managed location pointing to specific path in external location.
C. An external table where the location is pointing to specific path in external location.
D. A managed table where the catalog has managed location pointing to specific path in external location.

Answer: C

NEW QUESTION # 111
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
B. Replace spark.read with spark.readStream
C. Replace format("delta") with format("stream")
D. Replace predict with a stream-friendly prediction function
E. Replace "transactions" with the path to the location of the Delta table

Answer: B

Explanation:
To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. References: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources

NEW QUESTION # 112
A data engineer is working with two tables. Each of these tables is displayed below in its entirety.

The data engineer runs the following query to join these tables together:

Which of the following will be returned by the above query?

A. Option C
B. Option A
C. Option D
D. Option E
E. Option B

Answer: A

NEW QUESTION # 113
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.

The code block used by the data engineer is below:
Which line of code should the data engineer use to fill in the blank if the data engineer only wants the query to execute a micro-batch to process data every 5 seconds?

A. trigger(continuous="5 seconds")
B. trigger(once="5 seconds")
C. trigger("5 seconds")
D. trigger(processingTime="5 seconds")

Answer: D

NEW QUESTION # 114
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