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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q17-Q22):

NEW QUESTION # 17
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 A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
  • C. DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
  • D. DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B

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 # 18
A data analyst builds a Spark application to analyze finance data and performs the following operations:filter, select,groupBy, andcoalesce.
Which operation results in a shuffle?

  • A. coalesce
  • B. select
  • C. filter
  • D. groupBy

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
ThegroupBy()operation causes a shuffle because it requires all values for a specific key to be brought together, which may involve moving data across partitions.
In contrast:
filter()andselect()are narrow transformations and do not cause shuffles.
coalesce()tries to reduce the number of partitions and avoids shuffling by moving data to fewer partitions without a full shuffle (unlikerepartition()).
Reference:Apache Spark - Understanding Shuffle


NEW QUESTION # 19
An engineer has a large ORC file located at/file/test_data.orcand wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e.,col1,col2, during the reading process?

  • A. spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")
  • B. spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")
  • C. spark.read.orc("/file/test_data.orc").selected("col1", "col2")
  • D. spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct way to load specific columns from an ORC file is to first load the file using.load()and then apply.
select()on the resulting DataFrame. This is valid with.read.format("orc")or the shortcut.read.orc().
df = spark.read.format("orc").load("/file/test_data.orc").select("col1","col2") Why others are incorrect:
Aperforms selection after filtering, but doesn't match the intention to minimize memory at load.
Bincorrectly tries to use.select()before.load(), which is invalid.
Cuses a non-existent.selected()method.
Dcorrectly loads and then selects.
Reference:Apache Spark SQL API - ORC Format


NEW QUESTION # 20
A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columnssensor_id,temperature, andtimestamp. The engineer needs to calculate the average temperature for each sensor over the last 5 minutes while the data is streaming.
Which code implementation achieves the requirement?
Options from the images provided:

  • A.
  • B.
  • C.
  • D.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct answer isDbecause it uses proper time-based window aggregation along with watermarking, which is the required pattern in Spark Structured Streaming for time-based aggregations over event-time data.
From the Spark 3.5 documentation on structured streaming:
"You can define sliding windows on event-time columns, and usegroupByalong withwindow()to compute aggregates over those windows. To deal with late data, you usewithWatermark()to specify how late data is allowed to arrive." (Source:Structured Streaming Programming Guide) In optionD, the use of:
python
CopyEdit
groupBy("sensor_id", window("timestamp","5 minutes"))
agg(avg("temperature").alias("avg_temp"))
ensures that for eachsensor_id, the average temperature is calculated over 5-minute event-time windows. To complete the logic, it is assumed thatwithWatermark("timestamp", "5 minutes")is used earlier in the pipeline to handle late events.
Explanation of why other options are incorrect:
Option AusesWindow.partitionBywhich applies to static DataFrames or batch queries and is not suitable for streaming aggregations.
Option Bdoes not apply a time window, thus does not compute the rolling average over 5 minutes.
Option Cincorrectly applieswithWatermark()after an aggregation and does not include any time window, thus missing the time-based grouping required.
Therefore,Option Dis the only one that meets all requirements for computing a time-windowed streaming aggregation.


NEW QUESTION # 21
A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function that is not available in the standard Spark functions library. The existing UDF code is:

import hashlib
import pyspark.sql.functions as sf
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = sf.udf(shake_256, StringType())
The developer wants to replace this existing UDF with a Pandas UDF to improve performance. The developer changes the definition ofshake_256_udfto this:CopyEdit shake_256_udf = sf.pandas_udf(shake_256, StringType()) However, the developer receives the error:
What should the signature of theshake_256()function be changed to in order to fix this error?

  • A. def shake_256(df: pd.Series) -> str:
  • B. def shake_256(df: pd.Series) -> pd.Series:
  • C. def shake_256(raw: str) -> str:
  • D. def shake_256(df: Iterator[pd.Series]) -> Iterator[pd.Series]:

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When converting a standard PySpark UDF to a Pandas UDF for performance optimization, the function must operate on a Pandas Series as input and return a Pandas Series as output.
In this case, the original function signature:
def shake_256(raw: str) -> str
is scalar - not compatible with Pandas UDFs.
According to the official Spark documentation:
"Pandas UDFs operate onpandas.Seriesand returnpandas.Series. The function definition should be:
def my_udf(s: pd.Series) -> pd.Series:
and it must be registered usingpandas_udf(...)."
Therefore, to fix the error:
The function should be updated to:
def shake_256(df: pd.Series) -> pd.Series:
return df.apply(lambda x: hashlib.shake_256(x.encode()).hexdigest(20))
This will allow Spark to efficiently execute the Pandas UDF in vectorized form, improving performance compared to standard UDFs.
Reference: Apache Spark 3.5 Documentation # User-Defined Functions # Pandas UDFs


NEW QUESTION # 22
......

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