Databricks - Databricks-Certified-Data-Engineer-Associate - Databricks Certified Data Engineer Associate Exam Accurate Minimum Pass Score

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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
Exam Duration:	90 minutes
Exam Price:	$200 USD
Passing Score:	70%
Real Exam Qty:	60
Available Languages:	English
Exam Format:	Multiple Select, Multiple Choice
Related Certifications:	Databricks Certified Data Analyst Associate
Certificate Validity Period:	2 years
Sample Questions:	Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions
Exam Way:	Online proctored or in-person testing center
Pre Condition:	Recommended: 6+ months of experience with Databricks and Apache Spark
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-associate

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Databricks Certified Data Engineer Associate certification is an essential certification for individuals who want to pursue a career in data engineering. Databricks Certified Data Engineer Associate Exam certification exam covers a range of topics and is designed to ensure that candidates have the necessary skills and knowledge to build and maintain large-scale data pipelines. By earning this certification, candidates can demonstrate their expertise in data engineering and increase their chances of getting hired by top companies in the data and AI industry.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q138-Q143):

NEW QUESTION # 138
Which of the following describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?

A. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.
B. CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static.
C. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally.
D. CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.
E. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.

Answer: C

Explanation:
A streaming live table or view processes data that has been added only since the last pipeline update. Streaming tables and views are stateful; if the defining query changes, new data will be processed based on the new query and existing data is not recomputed. This is useful when data needs to be processed incrementally, such as when ingesting streaming data sources or performing incremental loads from batch data sources. A live table or view, on the other hand, may be entirely computed when possible to optimize computation resources and time. This is suitable when data needs to be processed in full, such as when performing complex transformations or aggregations that require scanning all the data. Reference: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.

NEW QUESTION # 139
A data engineer is attempting to grant a user access to a view. They successfully run GRANT SELECT ON VIEW sales_catalog.market_data.summary TO analyst_group.
However, the analyst still receives an "Insufficient Permissions" error when trying to query the view.
In which order should the engineer verify the Hierarchy of Securable Objects to ensure the analyst has the necessary usage permissions?

A. WORKSPACE -> CLUSTER -> VIEW
B. STORAGE CREDENTIAL -> EXTERNAL LOCATION -> VIEW
C. CATALOG -> SCHEMA -> VIEW
D. METASTORE -> TABLE -> VIEW

Answer: C

Explanation:
To query the view, the analyst needs USE CATALOG on sales_catalog, then USE SCHEMA on market_data, and SELECT on summary. Privileges on the underlying tables are not required for querying a Unity Catalog view.

NEW QUESTION # 140
Which of the following benefits is provided by the array functions from Spark SQL?

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

Answer: B

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 # 141
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 cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

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

Answer: A

Explanation:
The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). Reference: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

NEW QUESTION # 142
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?

A. The ability to distribute complex data operations
B. The ability to manipulate the same data using a variety of languages
C. The ability to set up alerts for query failures
D. The ability to support batch and streaming workloads
E. The ability to collaborate in real time on a single notebook

Answer: D

Explanation:
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale1. Delta Lake supports upserts using the merge operation, which enables you to efficiently update existing data or insert new data into your Delta tables2. Delta Lake also provides time travel capabilities, which allow you to query previous versions of your data or roll back to a specific point in time3. Reference: 1: What is Delta Lake? | Databricks on AWS 2: Upsert into a table using merge | Databricks on AWS 3: [Query an older snapshot of a table (time travel) | Databricks on AWS] Learn more

NEW QUESTION # 143
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