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Databricks Certified Data Engineer Professional Exam Sample Questions (Q104-Q109):

NEW QUESTION # 104
Which statement describes the correct use of pyspark.sql.functions.broadcast?

A. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
B. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
C. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
D. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
E. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Answer: A

Explanation:
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html The broadcast function in PySpark is used in the context of joins. When you mark a DataFrame with broadcast, Spark tries to send this DataFrame to all worker nodes so that it can be joined with another DataFrame without shuffling the larger DataFrame across the nodes. This is particularly beneficial when the DataFrame is small enough to fit into the memory of each node. It helps to optimize the join process by reducing the amount of data that needs to be shuffled across the cluster, which can be a very expensive operation in terms of computation and time.
The pyspark.sql.functions.broadcast function in PySpark is used to hint to Spark that a DataFrame is small enough to be broadcast to all worker nodes in the cluster. When this hint is applied, Spark can perform a broadcast join, where the smaller DataFrame is sent to each executor only once and joined with the larger DataFrame on each executor. This can significantly reduce the amount of data shuffled across the network and can improve the performance of the join operation. In a broadcast join, the entire smaller DataFrame is sent to each executor, not just a specific column or a cached version on attached storage. This function is particularly useful when one of the DataFrames in a join operation is much smaller than the other, and can fit comfortably in the memory of each executor node.

NEW QUESTION # 105
A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.
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Which command should be removed from the notebook before scheduling it as a job?

A. Cmd 5
B. Cmd 6
C. Cmd 3
D. Cmd 2
E. Cmd 4

Answer: B

Explanation:
When scheduling a Databricks notebook as a job, it's generally recommended to remove or modify commands that involve displaying output, such as using the display() function. Displaying data using display() is an interactive feature designed for exploration and visualization within the notebook interface and may not work well in a production job context.
The finalDF.explain() command, which provides the execution plan of the DataFrame transformations and actions, is often useful for debugging and optimizing queries. While it doesn't Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from display interactive visualizations like display(), it can still be informative for understanding how Spark is executing the operations on your DataFrame.

NEW QUESTION # 106
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A. Instruct the marketing team to download results as a CSV and email them to the sales organization.
B. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
C. Add a parallel table write to the current production pipeline, updating a new sales table that varies Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from as required from marketing table.
D. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
E. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.

Answer: B

Explanation:
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.

NEW QUESTION # 107
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A. Total VMs: 1
400 GB per Executor
160 Cores / Executor
B. Total VMs: 4
100 GB per Executor
40 Cores/Executor
C. Total VMs: 2
200 GB per Executor
80 Cores / Executor
D. Total VMs: 8
50 GB per Executor
20 Cores / Executor

Answer: A

Explanation:
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https://docs.databricks.com/en/clusters/cluster-config-best-practices.html

NEW QUESTION # 108
A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each microbatch of data is processed in less than 3s; at least 12 times per minute, a microbatch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution.
Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

A. Increase the number of shuffle partitions to maximize parallelism, since the trigger interval cannot be modified without modifying the checkpoint directory.
B. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
C. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.
D. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to maximum allowable threshold should minimize this cost.
E. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.

Answer: D

NEW QUESTION # 109
......

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