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Databricks Certified Professional Data Engineer certification exam is designed for data engineers who work with Databricks. Databricks-Certified-Professional-Data-Engineer exam tests the candidate's ability to design, build, and maintain data pipelines, as well as their knowledge of various data engineering tools and techniques. Databricks-Certified-Professional-Data-Engineer Exam is intended to validate the candidate's proficiency in using Databricks for data engineering tasks.

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Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam is a highly sought-after certification for individuals who want to demonstrate their expertise in building reliable, scalable, and performant data pipelines using Databricks. Databricks Certified Professional Data Engineer Exam certification is designed to validate the skills and knowledge required to design, implement, and maintain data pipelines for big data processing using Databricks.

Databricks is an American-based technology company that develops software that manages big data via Apache Spark. Their software helps different organizations to process and analyze large data sets efficiently. Therefore, there is a need to certify professionals to handle these technologies. The Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) Certification Exam is designed to validate the knowledge and proficiency of data engineers in designing and building effective and scalable data engineering solutions on the Databricks platform.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q58-Q63):

NEW QUESTION # 58
Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?

A. Control plane
B. Data plane
C. JDBC data source
D. Databricks Filesystem
E. Databricks web application

Answer: B

Explanation:
Explanation
The answer is Data Plane, which is where compute(all-purpose, Job Cluster, DLT) are stored this is generally a customer cloud account, there is one exception SQL Warehouses, currently there are 3 types of SQL Warehouse compute available(classic, pro, serverless), in classic and pro compute is located in customer cloud account but serverless computed is located in Databricks cloud account.
Diagram, timeline Description automatically generated

NEW QUESTION # 59
Which of the following is a Continuous Probability Distributions?

A. Binomial probability distribution
B. Negative binomial distribution
C. Normal probability distribution
D. Poisson probability distribution

Answer: C

NEW QUESTION # 60
Which of the following type of tasks cannot setup through a job?

A. Databricks SQL Dashboard refresh
B. DELTA LIVE PIPELINE
C. Notebook
D. Spark Submit
E. Python

Answer: A

NEW QUESTION # 61
A transactions table has been liquid clustered on the columns product_id, user_id, and event_date.
Which operation lacks support for cluster on write?

A. INSERT INTO operations
B. spark.write.format('delta').mode('append')
C. spark.writestream.format('delta').mode('append')
D. CTAS and RTAS statements

Answer: C

Explanation:
Delta Lake'sLiquid Clusteringis an advanced feature that improves query performance by dynamically clustering data without requiring costly compaction steps like traditional Z-ordering.
When performing writes to aLiquid Clusteredtable, some write operations automatically maintain clustering, while othersdo not.
Explanation of Each Option:
* (A) spark.writestream.format('delta').mode('append') (Correct Answer)
* Reason:Streaming writes (writestream) donotsupportLiquid Clusteringbecause streaming data arrives in micro-batches.
* Since Liquid Clustering needs efficient global reorganization of files, streaming append operations don't provide sufficient data volume at a time to be effectively clustered.
* Delta Lake documentation states that Liquid Clustering is only supported for batch writes.
* (B) CTAS and RTAS statements
* Reason:CREATE TABLE AS SELECT (CTAS) and REPLACE TABLE AS SELECT (RTAS) are batch operationsand can enforce Liquid Clustering.
* These operations create or replace a table based on a query result, and since they are batch-based, Liquid Clustering applies.
* (C) INSERT INTO operations
* Reason:INSERT INTOis supportedfor Liquid Clustering because it is a batch operation.
* While it may not be as efficient as MERGE or COPY INTO, clustering is applied upon execution.
* (D) spark.write.format('delta').mode('append')
* Reason:Batch append operationsare supportedfor Liquid Clustering.
* Unlike streaming append, batch writes allow the optimizer to re-cluster data efficiently.
Conclusion:
Sincestreaming append operations do not support Liquid Clustering, option(A)is the correct answer.
References:
* Liquid Clustering in Delta Lake - Databricks Documentation

NEW QUESTION # 62
Which of the following scenarios is the best fit for the AUTO LOADER solution?

A. Efficiently process new data incrementally from cloud object storage
B. Efficiently copy data from data lake location to another data lake location
C. Incrementally process new streaming data from Apache Kafa into delta lake
D. Incrementally process new data from relational databases like MySQL
E. Efficiently move data incrementally from one delta table to another delta table

Answer: A

Explanation:
Explanation
The answer is, Efficiently process new data incrementally from cloud object storage.
Please note: AUTO LOADER only works on data/files located in cloud object storage like S3 or Azure Blob Storage it does not have the ability to read other data sources, although AU-TO LOADER is built on top of structured streaming it only supports files in the cloud object stor-age. If you want to use Apache Kafka then you can just use structured streaming.
Diagram Description automatically generated

Auto Loader and Cloud Storage Integration
Auto Loader supports a couple of ways to ingest data incrementally
1.Directory listing - List Directory and maintain the state in RocksDB, supports incremental file listing
2.File notification - Uses a trigger+queue to store the file notification which can be later used to retrieve the file, unlike Directory listing File notification can scale up to millions of files per day.
[OPTIONAL]
Auto Loader vs COPY INTO?
Auto Loader
Auto Loader incrementally and efficiently processes new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a new Structured Streaming source called cloudFiles. Given an input directory path on the cloud file storage, the cloudFiles source automatically processes new files as they arrive, with the option of also processing existing files in that directory.
When to use Auto Loader instead of the COPY INTO?
*You want to load data from a file location that contains files in the order of millions or higher. Auto Loader can discover files more efficiently than the COPY INTO SQL command and can split file processing into multiple batches.
*You do not plan to load subsets of previously uploaded files. With Auto Loader, it can be more difficult to reprocess subsets of files. However, you can use the COPY INTO SQL command to reload subsets of files while an Auto Loader stream is simultaneously running.
Refer to more documentation here,
https://docs.microsoft.com/en-us/azure/databricks/ingestion/auto-loader

NEW QUESTION # 63
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Databricks Certified Professional Data Engineer Exam Sample Questions (Q58-Q63):

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