Databricks-Certified-Professional-Data-Engineer Exam Quiz - Databricks-Certified-Professional-Data-Engineer Real Exams by nenih14658 nenih14658

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To become a Databricks Certified Professional Data Engineer, candidates must pass a rigorous exam that covers a wide range of topics related to data engineering. Databricks-Certified-Professional-Data-Engineer exam is designed to test a candidate's ability to design, build, and maintain data pipelines using Databricks tools and technologies. It also tests their understanding of data modeling, data warehousing, and data integration.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q34-Q39):

NEW QUESTION # 34
When using the complete mode to write stream data, how does it impact the target table?

A. Stream must complete to write the dataB. Delta commits transaction once the stream is stoppedC. Target table cannot be updated while stream is pendingD. Target table is overwritten for each batchE. Entire stream waits for complete data to write

Answer: D

Explanation:
Explanation
The answer is Target table is overwritten for each batch
Complete mode - The whole Result Table will be outputted to the sink after every trigger. This is supported for aggregation queries

NEW QUESTION # 35
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE . The table is configured to
run in Development mode using the Triggered Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after
clicking Start to update the pipeline?

A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will
be deployed for the update and terminated when the pipeline is stoppedB. All datasets will be updated once and the pipeline will shut down. The compute resources will be
terminatedC. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will
persist after the pipeline is stopped to allow for additional testingD. All datasets will be updated continuously and the pipeline will not shut down. The compute resources
will persist with the pipelineE. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to
allow for additional testing

Answer: E

NEW QUESTION # 36
You are currently asked to work on building a data pipeline, you have noticed that you are currently working on a very large scale ETL many data dependencies, which of the following tools can be used to address this problem?

A. DELTA LIVE TABLESB. SQL EndpointsC. STRUCTURED STREAMING with MULTI HOPD. JOBS and TASKSE. AUTO LOADER

Answer: A

Explanation:
Explanation
The answer is, DELTA LIVE TABLES
DLT simplifies data dependencies by building DAG-based joins between live tables. Here is a view of how the dag looks with data dependencies without additional meta data,
1.create or replace live view customers
2.select * from customers;
3.
4.create or replace live view sales_orders_raw
5.select * from sales_orders;
6.
7.create or replace live view sales_orders_cleaned
8.as
9.select sales.* from
10.live.sales_orders_raw s
11. join live.customers c
12.on c.customer_id = s.customer_id
13.where c.city = 'LA';
14.
15.create or replace live table sales_orders_in_la
16.selects from sales_orders_cleaned;
Above code creates below dag

Documentation on DELTA LIVE TABLES,
https://databricks.com/product/delta-live-tables
https://databricks.com/blog/2022/04/05/announcing-generally-availability-of-databricks-delta-live-tables-dlt.htm DELTA LIVE TABLES, addresses below challenges when building ETL processes
1.Complexities of large scale ETL
a.Hard to build and maintain dependencies
b.Difficult to switch between batch and stream
2.Data quality and governance
a.Difficult to monitor and enforce data quality
b.Impossible to trace data lineage
3.Difficult pipeline operations
a.Poor observability at granular data level
b.Error handling and recovery is laborious

NEW QUESTION # 37
Which of the statements is correct when choosing between lakehouse and Datawarehouse?

A. Traditional Data warehouses have special indexes which are optimized for Machine learningB. Lakehouse replaces the current dependency on data lakes and data warehouses uses an open standard storage format and supports low latency BI workloads.C. Traditional Data warehouses can serve low query latency with high reliability for BI workloadsD. SQL support is only available for Traditional Datawarehouse's, Lakehouses support Python and ScalaE. Traditional Data warehouses are the preferred choice if we need to support ACID, Lakehouse does not support ACID.

Answer: B

Explanation:
Explanation
The lakehouse replaces the current dependency on data lakes and data warehouses for modern data companies that desire:
* Open, direct access to data stored in standard data formats.
* Indexing protocols optimized for machine learning and data science.
* Low query latency and high reliability for BI and advanced analytics.

NEW QUESTION # 38
Your colleague was walking you through how a job was setup, but you noticed a warning message that said,
"Jobs running on all-purpose cluster are considered all purpose compute", the colleague was not sure why he was getting the warning message, how do you best explain this warning mes-sage?

A. All-purpose cluster provide interactive messages that can not be viewed in a jobB. All-purpose clusters are less expensive than the job clustersC. All-purpose clusters are more expensive than the job clustersD. All-purpose clusters cannot be used for Job clusters, due to performance issues.E. All-purpose clusters take longer to start the cluster vs a job cluster

Answer: C

Explanation:
Explanation
Warning message:
Graphical user interface, text, application, email Description automatically generated

Pricing for All-purpose clusters are more expensive than the job clusters AWS pricing(Aug 15th 2022)Graphical user interface Description automatically generated

Bottom of Form
Top of Form

NEW QUESTION # 39
......

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