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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q30-Q35):

NEW QUESTION # 30
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 # 31
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A. By configuring the optionrecoveryLocationduringwriteStream
B. By configuring the optioncheckpointLocationduringreadStream
C. By configuring the optionrecoveryLocationduring the SparkSession initialization
D. By configuring the optioncheckpointLocationduringwriteStream

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To enable a Structured Streaming query to recover from failures or intentional shutdowns, it is essential to specify thecheckpointLocationoption during thewriteStreamoperation. This checkpoint location stores the progress information of the streaming query, allowing it to resume from where it left off.
According to the Databricks documentation:
"You must specify thecheckpointLocationoption before you run a streaming query, as in the following example:
option("checkpointLocation", "/path/to/checkpoint/dir")
toTable("catalog.schema.table")
- Databricks Documentation: Structured Streaming checkpoints
By setting thecheckpointLocationduringwriteStream, Spark can maintain state information and ensure exactly- once processing semantics, which are crucial for reliable streaming applications.

NEW QUESTION # 32
A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:

The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*")
.select('*','_metadata.file_path')
Which change will ensure one record per file?
Options:

A. Add the option lineSep=' ' to the text() function
B. Add the option lineSep=", " to the text() function
C. Add the option wholetext=False to the text() function
D. Add the option wholetext=True to the text() function

Answer: D

Explanation:
To read each file as a single record, use:
spark.read.text(path, wholetext=True)
This ensures that Spark reads the entire file contents into one row.
Reference:Spark read.text() with wholetext

NEW QUESTION # 33
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. Shuffle join because no broadcast hints were provided
B. Shuffle join, because AQE is not enabled, and Spark uses a static query plan
C. Broadcast join, as df2 is smaller than the default broadcast threshold
D. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently

Answer: C

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 # 34
A Data Analyst is working on the DataFramesensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits therecordcolumn into separate columns and has one array item per row?
A)

A. exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
B. exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")
C. exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
D. exploded_df = exploded_df.select("record_datetime", "record_exploded")

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To flatten an array of structs into individual rows and access fields within each struct, you must:
Useexplode()to expand the array so each struct becomes its own row.
Access the struct fields via dot notation (e.g.,record_exploded.sensor_id).
Option C does exactly that:
First, explode therecordarray column into a new columnrecord_exploded.
Then, access fields of the struct using the dot syntax inselect.
This is standard practice in PySpark for nested data transformation.
Final Answer: C

NEW QUESTION # 35
......

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