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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q45-Q50):

NEW QUESTION # 45
A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:

Which block of Spark code can be used to achieve this requirement?
Options:

A. filtered_df = users_raw_df.na.drop(how='all')
B. filtered_df = users_raw_df.na.drop(thresh=0)
C. filtered_df = users_raw_df.na.drop(how='any')
D. filtered_df = users_raw_df.na.drop(how='all', thresh=None)

Answer: C

Explanation:
na.drop(how='any')drops any row that has at least one null value.
This is exactly what's needed when the goal is to retain only fully complete records.
Usage:CopyEdit
filtered_df = users_raw_df.na.drop(how='any')
Explanation of incorrect options:
A: thresh=0 is invalid - thresh must be # 1.
B: how='all' drops only rows where all columns are null (too lenient).
D: spark.na.drop doesn't support mixing how and thresh in that way; it's incorrect syntax.
Reference:PySpark DataFrameNaFunctions.drop()

NEW QUESTION # 46
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: B

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 # 47
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.dropfields("first_name, last_name, email, birthdate")
B. df_user_non_pii = df_user.drop("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: C

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 # 48
A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

A. 0
B. 1
C. 2
D. 3

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
If each of the 10 nodes runs 4 executors, and each executor is assigned 4 CPU cores:
Executors per node = 4
Cores per executor = 4
Total executors = 4 * 10 = 40
Total cores = 40 executors * 4 cores = 160 cores
However, Spark uses 1 core for overhead on each node when managing multiple executors. Thus, the practical allocation is:
Total usable executors = 4 executors/node × 10 nodes = 40
Total cores = 4 cores × 40 executors = 160
Answer is A - but the question asks specifically about "CPU cores used by the application," assuming all
allocated cores are usable (as Spark typically runs executors without internal core reservation unless explicitly configured).
However, if you are considering 4 executors/node × 4 cores = 16 cores per node, across 10 nodes, that's 160.
Final Answer: A

NEW QUESTION # 49
What is the difference betweendf.cache()anddf.persist()in Spark DataFrame?

A. persist()- Persists the DataFrame with the default storage level (MEMORY_AND_DISK_SER) andcache()- Can be used to set different storage levels to persist the contents of the DataFrame.
B. cache()- Persists the DataFrame with the default storage level (MEMORY_AND_DISK) andpersist()- Can be used to set different storage levels to persist the contents of the DataFrame
C. Bothcache()andpersist()can be used to set the default storage level (MEMORY_AND_DISK_SER)
D. Both functions perform the same operation. Thepersist()function provides improved performance asits default storage level isDISK_ONLY.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
df.cache()is shorthand fordf.persist(StorageLevel.MEMORY_AND_DISK)
df.persist()allows specifying any storage level such asMEMORY_ONLY,DISK_ONLY, MEMORY_AND_DISK_SER, etc.
By default,persist()usesMEMORY_AND_DISK, unless specified otherwise.
Reference:Spark Programming Guide - Caching and Persistence

NEW QUESTION # 50
......

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