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100% Pass 2025 Databricks Databricks-Certified-Data-Engineer-Associate Authoritative Exam Question

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Databricks Certified Data Engineer Associate certification is a vendor-neutral certification, which means that it is not tied to any specific technology or vendor. This makes it an excellent certification for professionals who want to work with a variety of data engineering tools and technologies.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?

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

Answer: E

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 # 20
A data engineer needs access to a table new_uable, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.
Which approach can be used to identify the owner of new_table?

A. Review the Owner field in the table's page in Data Explorer
B. There is no way to identify the owner of the table
C. Review the Permissions tab in the table's page in Data Explorer
D. Review the Owner field in the table's page in the cloud storage solution

Answer: A

Explanation:
To find the owner of a table in Databricks, one can utilize the Data Explorer feature. The Data Explorer provides detailed information about various data objects, including tables. By navigating to the specific table's page in Data Explorer, a data engineer can review the Owner field, which identifies the individual or role that owns the table. This information is crucial for obtaining the necessary permissions or for any administrative actions related to the table.
Reference:
Databricks documentation on Data Explorer: Using Data Explorer in Databricks

NEW QUESTION # 21
A data engineer needs to apply custom logic to identify employees with more than 5 years of experience in array column employees in table stores. The custom logic should create a new column exp_employees that is an array of all of the employees with more than 5 years of experience for each row. In order to apply this custom logic at scale, the data engineer wants to use the FILTER higher-order function.
Which of the following code blocks successfully completes this task?

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

Answer: E

Explanation:
Option A is the correct answer because it uses the FILTER higher-order function correctly to filter out employees with more than 5 years of experience from the array column "employees". It applies a lambda function i -> i.years_exp > 5 that checks if the years of experience of each employee in the array is greater than 5. If this condition is met, the employee is included in the new array column "exp_employees".

NEW QUESTION # 22
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. Data Explorer
C. Delta Lake
D. Auto Loader
E. Unity Catalog

Answer: D

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 # 23
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("5 seconds")
B. trigger(once="5 seconds")
C. trigger(processingTime="5 seconds")
D. trigger(continuous="5 seconds")
E. trigger()

Answer: C

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). References: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

NEW QUESTION # 24
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