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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q22-Q27):

NEW QUESTION # 22
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 = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
B. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
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('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 # 23
Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A. psdf.to_pandas()
B. psdf.to_spark()
C. psdf.to_pyspark()
D. psdf.to_dataframe()

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Pandas API on Spark (pyspark.pandas) allows interoperability with PySpark DataFrames. To convert apyspark.pandas.DataFrameto a standard PySpark DataFrame, you use.to_spark().
Example:
df = psdf.to_spark()
This is the officially supported method as per Databricks Documentation.
Incorrect options:
B, D: Invalid or nonexistent methods.
C: Converts to a local pandas DataFrame, not a PySpark DataFrame.

NEW QUESTION # 24
A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFramedfwith columnsuser_id,product_id, andpurchase_amountand needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A. df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
B. df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
C. df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
D. df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Shuffling occurs in operations likegroupBy,reduceByKey, orjoin-which cause data to be moved across partitions. Therepartition()operation can also cause a shuffle, but in this context, it follows an aggregation.
InOption D, thegroupByfollowed byaggresults in a shuffle due to grouping across nodes.
Therepartition(10)is a partitioning transformation but does not involve a new shuffle since the data is already grouped.
This sequence - shuffle (groupBy) followed by non-shuffling (repartition) - is correct.
Option A does the opposite: thefilterdoes not cause a shuffle, butgroupBydoes - this makes it the wrong order.

NEW QUESTION # 25
In the code block below,aggDFcontains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A. complete
B. append
C. replace
D. aggregate

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct output mode for streaming aggregations that need to output the full updated results at each trigger is"complete".
From the official documentation:
"complete: The entire updated result table will be output to the sink every time there is a trigger." This is ideal for aggregations, such as counts or averages grouped by a key, where the result table changes incrementally over time.
append: only outputs newly added rows
replace and aggregate: invalid values for output mode
Reference: Spark Structured Streaming Programming Guide # Output Modes

NEW QUESTION # 26
Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?

A. It is primarily used for data ingestion into Spark from external sources
B. It provides a way to run Spark applications remotely in any programming language
C. It allows for remote execution of Spark jobs
D. It can be used to interact with any remote cluster using the REST API

Answer: C

Explanation:
Comprehensive and Detailed Explanation:
Spark Connect introduces a decoupled client-server architecture. Its key feature is enabling Spark job submission and execution from remote clients - in Python, Java, etc.
From Databricks documentation:
"Spark Connect allows remote clients to connect to a Spark cluster and execute Spark jobs without being co- located with the Spark driver." A is close, but "any language" is overstated (currently supports Python, Java, etc., not literally all).
B refers to REST, which is not Spark Connect's mechanism.
D is incorrect; Spark Connect isn't focused on ingestion.
Final Answer: C

NEW QUESTION # 27
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