Databricks-Certified-Data-Engineer-Associate Online Exam - Examcollection Databricks-Certified-Data-Engineer-Associate Vce

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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

SectionObjectives
Topic 1: Data Ingestion and ELT Development- ETL patterns and transformations
- Data ingestion using Spark SQL and PySpark
- Handling structured and semi-structured data
Topic 2: Databricks Lakehouse Platform Fundamentals- Workspace, architecture, and core platform concepts
- Clusters, notebooks, and basic Databricks environment usage
Topic 3: Productionizing Data Pipelines- Databricks Workflows / Jobs orchestration
- Pipeline deployment and operationalization
- Scheduling and monitoring jobs
Topic 4: Data Governance and Quality- Data quality concepts and management
- Unity Catalog basics
- Data access control and governance
Topic 5: Data Processing and Transformations- PySpark DataFrame transformations
- User-defined functions (UDFs)
- Delta Lake fundamentals (tables, transactions, optimization)
- Apache Spark SQL operations (joins, aggregations, filtering)

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q317-Q322):

NEW QUESTION # 317
A production ETL notebook runs once nightly and currently executes on a shared all-purpose cluster that stays running 24 hours a day.
Which change reduces cost while preserving the schedule?

Answer: D

Explanation:
Job clusters are provisioned when the job run starts and terminated when it completes, so compute is billed only for the run duration and at the lower Jobs Compute rate. All-purpose clusters left running continuously are billed at a higher rate even when idle.


NEW QUESTION # 318
A data engineer needs to ingest from both streaming and batch sources for a firm that relies on highly accurate data. Occasionally, some of the data picked up by the sensors that provide a streaming input are outside the expected parameters. If this occurs, the data must be dropped, but the stream should not fail. Which feature of Delta Live Tables meets this requirement?

Answer: C

Explanation:
Delta Live Tables Expectations allow defining data quality rules. Records that fail the expectations (such as sensor readings outside valid ranges) can be dropped without stopping the streaming pipeline, ensuring continuous and accurate processing.


NEW QUESTION # 319
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

Answer: A

Explanation:
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/


NEW QUESTION # 320
A data engineer has realized that the data files associated with a Delta table are incredibly small. They want to compact the small files to form larger files to improve performance.
Which of the following keywords can be used to compact the small files?

Answer: A

Explanation:
The keyword that can be used to compact the small files associated with a Delta table is OPTIMIZE. The OPTIMIZE command performs file compaction on a Delta table by rewriting a set of small files into a set of larger files1. This can improve the performance of queries that scan the table by reducing the number of files that need to be read and the amount of metadata that needs to be processed1. The OPTIMIZE command can also optionally sort the data within each file by a given set of columns, which can further improve the query performance by enabling data skipping and predicate pushdown1. The OPTIMIZE command can be applied to the whole table or to a specific partition of the table1.
The other keywords are not suitable for compacting the small files associated with a Delta table. REDUCE is a keyword used in the SQL syntax for aggregating data using a user-defined function2. COMPACTION is not a valid keyword in SQL or Python. REPARTITION is a keyword used in the Python syntax for changing the number of partitions of a DataFrame or an RDD3. VACUUM is a keyword used to remove files that are no longer referenced by a Delta table and are older than a retention threshold4.
1: OPTIMIZE | Databricks on AWS
2: REDUCE | Databricks on AWS
3: repartition | Databricks on AWS
4: VACUUM | Databricks on AWS


NEW QUESTION # 321
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?

Answer: A

Explanation:
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. References: Git integration with Databricks Repos - Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available - New 'Files' Feature in ..., Databricks Repos - What it is and how we can use it | Adatis.


NEW QUESTION # 322
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