100% Pass Quiz 2025 Databricks Associate-Developer-Apache-Spark-3.5–Marvelous VCE Dumps

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q70-Q75):

NEW QUESTION # 70
A data engineer writes the following code to join two DataFramesdf1anddf2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

A. Broadcast join, as df2 is smaller than the default broadcast threshold
B. Shuffle join because no broadcast hints were provided
C. Shuffle join, because AQE is not enabled, and Spark uses a static query plan
D. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The default broadcast join threshold in Spark is:
spark.sql.autoBroadcastJoinThreshold = 10MB
Sincedf2is only 8 MB (less than 10 MB), Spark will automatically apply a broadcast join without requiring explicit hints.
From the Spark documentation:
"If one side of the join is smaller than the broadcast threshold, Spark will automatically broadcast it to all executors." A is incorrect because Spark does support auto broadcast even with static plans.
B is correct: Spark will automatically broadcast df2.
C and D are incorrect because Spark's default logic handles this optimization.
Final Answer: B

NEW QUESTION # 71
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: A

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 # 72
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.filter()
B. DataFrame.groupBy().agg()
C. DataFrame.withColumn()
D. DataFrame.select()

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 # 73
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.drop("first_name", "last_name", "email", "birthdate")
C. df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")
D. df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")

Answer: B

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 # 74
You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A. DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A
B. DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
C. DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
D. DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself

Answer: A

Explanation:
Comprehensive and Detailed Explanation:
Broadcast joins work by sending the smaller DataFrame to all executors, eliminating the shuffle of the larger DataFrame.
From Spark documentation:
"Broadcast joins are efficient when one DataFrame is small enough to fit in memory. Spark avoids shuffling the larger table." DataFrame B (1 GB) fits within the default threshold and should be broadcasted.
It eliminates the need to shuffle the large DataFrame A.
Final Answer: B

NEW QUESTION # 75
......

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100% Pass Quiz 2025 Databricks Associate-Developer-Apache-Spark-3.5–Marvelous VCE Dumps

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q70-Q75):

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