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100% Pass Professional Associate-Developer-Apache-Spark-3.5 - Valid Databricks Certified Associate Developer for Apache Spark 3.5 - Python Exam Syllabus

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q65-Q70):

NEW QUESTION # 65
A data scientist is working on a project that requires processing large amounts of structured data, performing SQL queries, and applying machine learning algorithms. The data scientist is considering using Apache Spark for this task.
Which combination of Apache Spark modules should the data scientist use in this scenario?
Options:

A. Spark DataFrames, Structured Streaming, and GraphX
B. Spark DataFrames, Spark SQL, and MLlib
C. Spark Streaming, GraphX, and Pandas API on Spark
D. Spark SQL, Pandas API on Spark, and Structured Streaming

Answer: B

Explanation:
Comprehensive Explanation:
To cover structured data processing, SQL querying, and machine learning in Apache Spark, the correct combination of components is:
Spark DataFrames: for structured data processing
Spark SQL: to execute SQL queries over structured data
MLlib: Spark's scalable machine learning library
This trio is designed for exactly this type of use case.
Why other options are incorrect:
A: GraphX is for graph processing - not needed here.
B: Pandas API on Spark is useful, but MLlib is essential for ML, which this option omits.
C: Spark Streaming is legacy; GraphX is irrelevant here.
Reference:Apache Spark Modules Overview

NEW QUESTION # 66
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 Ignore
B. save with mode ErrorIfExists
C. saveAsTable with mode Overwrite
D. saveAsTable with mode ErrorIfExists

Answer: D

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 # 67
Given a DataFramedfthat has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?

A. 0
B. 1
C. 2
D. Same number as the cluster executors

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The.coalesce(numPartitions)function is used to reduce the number of partitions in a DataFrame. It does not increase the number of partitions. If the specified number of partitions is greater than the current number, it will not have any effect.
From the official Spark documentation:
"coalesce() results in a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim one or more of the current partitions." However, if you try to increase partitions using coalesce (e.g., from 10 to 20), the number of partitions remains unchanged.
Hence,df.coalesce(20)will still return a DataFrame with 10 partitions.
Reference: Apache Spark 3.5 Programming Guide # RDD and DataFrame Operations # coalesce()

NEW QUESTION # 68
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('customer_id') == F.col('custid'), 'right_outer')
C. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
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 # 69
The following code fragment results in an error:

Which code fragment should be used instead?

Answer: B

NEW QUESTION # 70
......

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