Databricks Databricks-Certified-Professional-Data-Engineer Test Dates & Latest Test Databricks-Certified-Professional-Data-Engineer Experience

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The Databricks Databricks-Certified-Professional-Data-Engineer Exam is designed to assess the proficiency of the candidates in various areas related to data engineering on Databricks. Databricks-Certified-Professional-Data-Engineer exam focuses on topics such as data ingestion, data transformation, data modeling, data storage, and data processing. Databricks-Certified-Professional-Data-Engineer exam tests the candidates' knowledge of using Databricks to build data pipelines that can handle large volumes of data, process data in real-time, and integrate with other data sources.

Databricks Certified Professional Data Engineer certification is an excellent choice for individuals who are looking to specialize in data engineering and want to demonstrate their expertise in Databricks technologies. It is also a valuable credential for companies that use Databricks and want to ensure that their employees have the necessary skills to manage and analyze large amounts of data effectively.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q105-Q110):

NEW QUESTION # 105
Which of the following is the correct statement for a session scoped temporary view?

A. Temporary views stored in memory
B. Temporary views are lost once the notebook is detached and re-attached
C. Temporary views can be still accessed even if cluster is restarted
D. Temporary views are created in local_temp database
E. Temporary views can be still accessed even if the notebook is detached and attached

Answer: B

Explanation:
Explanation
The answer is Temporary views are lost once the notebook is detached and attached There are two types of temporary views that can be created, Session scoped and Global
*A local/session scoped temporary view is only available with a spark session, so another notebook in the same cluster can not access it. if a notebook is detached and reattached local temporary view is lost.
*A global temporary view is available to all the notebooks in the cluster, if a cluster restarts global temporary view is lost.

NEW QUESTION # 106
Which of the following Structured Streaming queries successfully performs a hop from a Silver to Gold table?

A. 1.(spark.table("sales")
2..writeStream
3..option("checkpointLocation", checkpointPath)
4..outputMode("complete")
5..table("sales") )
B. 1.(spark.table("sales")
2..groupBy("store")
3..agg(sum("sales"))
4..writeStream
5..option("checkpointLocation", checkpointPath)
6..outputMode("complete")
7..table("aggregatedSales") )
(Correct)
C. 1.(spark.read.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales") )
D. 1.(spark.readStream.load(rawSalesLocation)
2..writeStream
3..option("checkpointLocation", checkpointPath)
4..outputMode("append")
5..table("uncleanedSales") )
E. 1.(spark.table("sales")
2..withColumn("avgPrice", col("sales") / col("units"))
3..writeStream
4..option("checkpointLocation", checkpointPath)
5..outputMode("append")
6..table("cleanedSales") )

Answer: B

Explanation:
Explanation
The answer is
1.(spark.table("sales")
2..groupBy("store")
3..agg(sum("sales"))
4..writeStream
5..option("checkpointLocation", checkpointPath)
6..outputMode("complete")
7..table("aggregatedSales") )
The gold layer is normally used to store aggregated data
Review the below link for more info,
Medallion Architecture - Databricks
Gold Layer:
1. Powers Ml applications, reporting, dashboards, ad hoc analytics
2. Refined views of data, typically with aggregations
3. Reduces strain on production systems
4. Optimizes query performance for business-critical data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
A diagram of a house Description automatically generated with low confidence

NEW QUESTION # 107
Which of the following type of tasks cannot setup through a job?

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

Answer: B

NEW QUESTION # 108
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
B. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
C. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
D. Databricks has autotuned to a smaller target file size based on the overall size of data in the table

Answer: A

Explanation:
This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade-off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Optimize" section.
https://docs.databricks.com/en/delta/tune-file-size.html#autotune-table 'Autotune file size based on workload'

NEW QUESTION # 109
Below sample input data contains two columns, one cartId also known as session id, and the second column is called items, every time a customer makes a change to the cart this is stored as an array in the table, the Marketing team asked you to create a unique list of item's that were ever added to the cart by each customer, fill in blanks by choosing the appropriate array function so the query produces below expected result as shown below.
Schema: cartId INT, items Array<INT>
Sample Data

1.SELECT cartId, ___ (___(items)) as items
2.FROM carts GROUP BY cartId
Expected result:
cartId items
1 [1,100,200,300,250]

A. ARRAY_UNION, COLLECT_SET
B. ARRAY_UNION, ARRAY_DISTINT
C. FLATTEN, COLLECT_UNION
D. ARRAY_DISTINCT, ARRAY_UNION
E. ARRAY_UNION, FLATTEN

Answer: A

Explanation:
Explanation
COLLECT SET is a kind of aggregate function that combines a column value from all rows into a unique list ARRAY_UNION combines and removes any duplicates, Graphical user interface, application Description automatically generated with medium confidence

NEW QUESTION # 110
......

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