ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 TEST TORRENT IS VERY HELPFUL FOR YOU TO LEARN ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 EXAM - ACTUALCOLLECTION

Associate-Developer-Apache-Spark-3.5 Test Torrent is Very Helpful for You to Learn Associate-Developer-Apache-Spark-3.5 Exam - ActualCollection

Associate-Developer-Apache-Spark-3.5 Test Torrent is Very Helpful for You to Learn Associate-Developer-Apache-Spark-3.5 Exam - ActualCollection

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q29-Q34):

NEW QUESTION # 29
A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

  • A. save with mode ErrorIfExists
  • B. save with mode Ignore
  • C. saveAsTable with mode ErrorIfExists
  • D. saveAsTable with mode Overwrite

Answer: C

Explanation:
Comprehensive and Detailed Explanation:
The methodsaveAsTable()creates a new table and optionally fails if the table exists.
From Spark documentation:
"The mode 'ErrorIfExists' (default) will throw an error if the table already exists." Thus:
Option A is correct.
Option B (Overwrite) would overwrite existing data - not acceptable here.
Option C and D usesave(), which doesn't create a managed table with metadata in the metastore.
Final Answer: A


NEW QUESTION # 30
A developer is trying to join two tables,sales.purchases_fctandsales.customer_dim, using the following code:

fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in thepurchases_fcttable that do not exist in thecustomer_dimtable are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?

  • A. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
  • B. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
  • C. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
  • D. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Spark, the default join type is an inner join, which returns only the rows with matching keys in both DataFrames. To retain all records from the left DataFrame (purch_df) and include matching records from the right DataFrame (cust_df), a left outer join should be used.
By specifying the join type as'left', the modified code ensures that all records frompurch_dfare preserved, and matching records fromcust_dfare included. Records inpurch_dfwithout a corresponding match incust_dfwill havenullvalues for the columns fromcust_df.
This approach is consistent with standard SQL join operations and is supported in PySpark's DataFrame API.


NEW QUESTION # 31
A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

  • A. users.write.saveAsTable("default_table").option("path", "/some/path")
  • B. users.write.saveAsTable("default_table", path="/some/path")
  • C. users.write(path="/some/path").saveAsTable("default_table")
  • D. users.write.option("path", "/some/path").saveAsTable("default_table")

Answer: D

Explanation:
To persist a table and specify the save path, use:
users.write.option("path","/some/path").saveAsTable("default_table")
The .option("path", ...) must be applied before calling saveAsTable.
Option A uses invalid syntax (write(path=...)).
Option B applies.option()after.saveAsTable()-which is too late.
Option D uses incorrect syntax (no path parameter in saveAsTable).
Reference:Spark SQL - Save as Table


NEW QUESTION # 32
A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?

  • A. Reduce the value of spark.sql.shuffle.partitions
  • B. Increase the value of spark.sql.shuffle.partitions
  • C. Increase the size of the dataset to create more partitions
  • D. Enable dynamic resource allocation to scale resources as needed

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The number of tasks is controlled by the number of partitions. By default,spark.sql.shuffle.partitionsis 200. If stages are showing very few tasks (less than total cores), you may not be leveraging full parallelism.
From the Spark tuning guide:
"To improve performance, especially for large clusters, increasespark.sql.shuffle.partitionsto create more tasks and parallelism." Thus:
A is correct: increasing shuffle partitions increases parallelism
B is wrong: it further reduces parallelism
C is invalid: increasing dataset size doesn't guarantee more partitions D is irrelevant to task count per stage Final Answer: A


NEW QUESTION # 33
What is the behavior for functiondate_sub(start, days)if a negative value is passed into thedaysparameter?

  • A. An error message of an invalid parameter will be returned
  • B. The number of days specified will be removed from the start date
  • C. The same start date will be returned
  • D. The number of days specified will be added to the start date

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The functiondate_sub(start, days)subtracts the number of days from the start date. If a negative number is passed, the behavior becomes a date addition.
Example:
SELECT date_sub('2024-05-01', -5)
-- Returns: 2024-05-06
So, a negative value effectively adds the absolute number of days to the date.
Reference: Spark SQL Functions # date_sub()


NEW QUESTION # 34
......

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