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Databricks Certified Data Engineer Associate Exam Sample Questions (Q33-Q38):

NEW QUESTION # 33
A data engineer manages multiple external tables linked to various data sources. The data engineer wants to manage these external tables efficiently and ensure that only the necessary permissions are granted to users for accessing specific external tables.
How should the data engineer manage access to these external tables?

A. Set up Azure Blob Storage permissions at the container level, allowing access to all external tables.
B. Grant permissions on the Databricks workspace level, which will automatically apply to all external tables.
C. Create a single user role with full access to all external tables and assign it to all users.
D. Use Unity Catalog to manage access controls and permissions for each external table individually.

Answer: D

NEW QUESTION # 34
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 use the same source of truth for their work
B. Both teams would respond more quickly to ad-hoc requests
C. Both teams would be able to collaborate on projects in real-time
D. Both teams would reorganize to report to the same department
E. Both teams would autoscale their work as data size evolves

Answer: A

NEW QUESTION # 35
Which of the following can be used to simplify and unify siloed data architectures that are specialized for specific use cases?

A. Data warehouse
B. All of these
C. Data lakehouse
D. Data lake
E. None of these

Answer: C

NEW QUESTION # 36
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. Auto Loader
B. Delta Lake
C. Unity Catalog
D. Data Explorer
E. Databricks SQL

Answer: A

Explanation:
Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage. Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks. Reference: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live Tables

NEW QUESTION # 37
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:

If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?

A. processingTime(1)
B. trigger(availableNow=True)
C. trigger(continuous="once")
D. trigger(parallelBatch=True)
E. trigger(processingTime="once")

Answer: B

Explanation:
Explanation
https://stackoverflow.com/questions/71061809/trigger-availablenow-for-delta-source-streaming-queries-in-pyspa

NEW QUESTION # 38
......

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