DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER VALID EXAM TESTKING & DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER VCE TORRENT

Databricks-Certified-Professional-Data-Engineer Valid Exam Testking & Databricks-Certified-Professional-Data-Engineer Vce Torrent

Databricks-Certified-Professional-Data-Engineer Valid Exam Testking & Databricks-Certified-Professional-Data-Engineer Vce Torrent

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

NEW QUESTION # 33
Which statement describes Delta Lake Auto Compaction?

  • A. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
  • B. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
  • C. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
  • D. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
  • E. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.

Answer: D

Explanation:
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB. Auto Compaction only compacts files that have not been compacted previously. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Compaction for Delta Lake on Databricks" section.
"Auto compaction occurs after a write to a table has succeeded and runs synchronously on the cluster that has performed the write. Auto compaction only compacts files that haven't been compacted previously."
https://learn.microsoft.com/en-us/azure/databricks/delta/tune-file-size


NEW QUESTION # 34
What are the advantages of the Hashing Features?

  • A. Less pass through the training data
  • B. Easily reverse engineer vectors to determine which original feature mapped to a vector location
  • C. Requires the less memory

Answer: A,C

Explanation:
Explanation
SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and
shoehorning the training data into vectors of that size. This approach is known as feature hashing. The
shoehorning is done by picking one or more locations by using a hash of the name of the variable for
continuous variables or a hash of the variable name and the category name or word for categorical, text*like, or
word-like data.
This hashed feature approach has the distinct advantage of requiring less memory and one less pass through
the training data, but it can make it much harder to reverse engineer vectors to determine which original
feature mapped to a vector location. This is because multiple features may hash to the same location. With
large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to
understand what a classifier is doing.
An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like
variables aren't a problem.


NEW QUESTION # 35
Which of the following commands can be used to query a delta table?

  • A. 1.%python
    2.delta.sql("select * from table")
  • B. 1.%sql
    2.Select * from table_name
  • C. 1.%python
    2.spark.sql("select * from table_name")
  • D. Both A & B
    (Correct)
  • E. 1.%python
    2.execute.sql("select * from table")

Answer: D

Explanation:
Explanation
The answer is both options A and B
Options C and D are incorrect because there is no command in Spark called execute.sql or delta.sql


NEW QUESTION # 36
Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?

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

Answer: A

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 # 37
A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

  • A. Total Disk Space remains constant
  • B. The five Minute Load Average remains consistent/flat
  • C. Overall cluster CPU utilization is around 25%
  • D. Bytes Received never exceeds 80 million bytes per second
  • E. Network I/O never spikes

Answer: C

Explanation:
This is the correct answer because it indicates a bottleneck caused by code executing on the driver. A bottleneck is a situation where the performance or capacity of a system is limited by a single component or resource. A bottleneck can cause slow execution, high latency, or low throughput. A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor. When evaluating the Ganglia Metrics for this cluster, one can look for indicators that show how the cluster resources are being utilized, such as CPU, memory, disk, or network. If the overall cluster CPU utilization is around 25%, it means that only one out of the four nodes (driver + 3 executors) is using its full CPU capacity, while the other three nodes are idle or underutilized. This suggests that the code executing on the driver is taking too long or consuming too much CPU resources, preventing the executors from receiving tasks or data to process. This can happen when the code has driver-side operations that are not parallelized or distributed, such as collecting large amounts of data to the driver, performing complex calculations on the driver, or using non-Spark libraries on the driver. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "View cluster status and event logs - Ganglia metrics" section; Databricks Documentation, under "Avoid collecting large RDDs" section.
In a Spark cluster, the driver node is responsible for managing the execution of the Spark application, including scheduling tasks, managing the execution plan, and interacting withthe cluster manager. If the overall cluster CPU utilization is low (e.g., around 25%), it may indicate that the driver node is not utilizing the available resources effectively and might be a bottleneck.


NEW QUESTION # 38
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