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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q62-Q67):

NEW QUESTION # 62
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(path="/some/path").saveAsTable("default_table")
  • C. users.write.option("path", "/some/path").saveAsTable("default_table")
  • D. users.write.saveAsTable("default_table", path="/some/path")

Answer: C

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 # 63
A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?

  • A. DataFrame.select()
  • B. DataFrame.groupBy().agg()
  • C. DataFrame.withColumn()
  • D. DataFrame.filter()

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Operations that trigger data movement across partitions (like groupBy, join, repartition) result in a shuffle and a new stage.
From Spark documentation:
"groupBy and aggregation cause data to be shuffled across partitions to combine rows with the same key." Option A (groupBy + agg) # causes shuffle.
Options B, C, and D (filter, withColumn, select) # transformations that do not require shuffling; they are narrow dependencies.
Final Answer: A


NEW QUESTION # 64
A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

  • A. Optimize the data processing logic by repartitioning the DataFrame.
  • B. Cache large DataFrames to persist them in memory.
  • C. Increase the memory allocated to the Spark Driver.
  • D. Modify the Spark configuration to disable garbage collection

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The message"GC overhead limit exceeded"typically indicates that the JVM is spending too much time in garbage collection with little memory recovery. This suggests that the driver or executor is under-provisioned in memory.
The most effective remedy is to increase the driver memory using:
--driver-memory 4g
This is confirmed in Spark's official troubleshooting documentation:
"If you see a lot ofGC overhead limit exceedederrors in the driver logs, it's a sign that the driver is running out of memory."
-Spark Tuning Guide
Why others are incorrect:
Amay help but does not directly address the driver memory shortage.
Bis not a valid action; GC cannot be disabled.
Dincreases memory usage, worsening the problem.


NEW QUESTION # 65
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'), 'left')
  • B. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
  • C. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
  • D. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))

Answer: A

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 # 66
A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.
Which code snippet can be used to meet this requirement?

  • A. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
  • B. df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")
  • C. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
  • D. df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")

Answer: A

Explanation:
Comprehensive and Detailed Explanation:
To remove specific columns from a PySpark DataFrame, the drop() method is used. This method returns a new DataFrame without the specified columns. The correct syntax for dropping multiple columns is to pass each column name as a separate argument to the drop() method.
Correct Usage:
df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate") This line of code will return a new DataFrame df_user_non_pii that excludes the specified PII columns.
Explanation of Options:
A).Correct. Uses the drop() method with multiple column names passed as separate arguments, which is the standard and correct usage in PySpark.
B).Although it appears similar to Option A, if the column names are not enclosed in quotes or if there's a syntax error (e.g., missing quotes or incorrect variable names), it would result in an error. However, as written, it's identical to Option A and thus also correct.
C).Incorrect. The dropfields() method is not a method of the DataFrame class in PySpark. It's used with StructType columns to drop fields from nested structures, not top-level DataFrame columns.
D).Incorrect. Passing a single string with comma-separated column names to dropfields() is not valid syntax in PySpark.
References:
PySpark Documentation:DataFrame.drop
Stack Overflow Discussion:How to delete columns in PySpark DataFrame


NEW QUESTION # 67
......

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