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Valid Associate-Developer-Apache-Spark-3.5 Valid Test Testking–The Best Valid Exam Registration for Associate-Developer-Apache-Spark-3.5: Databricks Certified Associate Developer for Apache Spark 3.5 - Python

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q78-Q83):

NEW QUESTION # 78
A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing agroupByoperation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)

C)

D)

  • A. Use theapplyInPandasAPI:
    df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()
  • B. Use themapInPandasAPI:
    df.mapInPandas(mean_func, schema="user_id long, value double").show()
  • C. Use a regular Spark UDF:
    from pyspark.sql.functions import mean
    df.groupBy("user_id").agg(mean("value")).show()
  • D. Use a Pandas UDF:
    @pandas_udf("double")
    def mean_func(value: pd.Series) -> float:
    return value.mean()
    df.groupby("user_id").agg(mean_func(df["value"])).show()

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct approach to perform a parallelizedgroupByoperation across Spark worker nodes using Pandas API is viaapplyInPandas. This function enables grouped map operations using Pandas logic in a distributed Spark environment. It applies a user-defined function to each group of data represented as a Pandas DataFrame.
As per the Databricks documentation:
"applyInPandas()allows for vectorized operations on grouped data in Spark. It applies a user-defined function to each group of a DataFrame and outputs a new DataFrame. This is the recommended approach for using Pandas logic across grouped data with parallel execution." Option A is correct and achieves this parallel execution.
Option B (mapInPandas) applies to the entire DataFrame, not grouped operations.
Option C uses built-in aggregation functions, which are efficient but not customizable with Pandas logic.
Option D creates a scalar Pandas UDF which does not perform a group-wise transformation.
Therefore, to run agroupBywith parallel Pandas logic on Spark workers, Option A usingapplyInPandasis the only correct answer.
Reference: Apache Spark 3.5 Documentation # Pandas API on Spark # Grouped Map Pandas UDFs (applyInPandas)


NEW QUESTION # 79
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. groupBy
  • B. coalesce
  • C. select
  • D. filter

Answer: A

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 # 80
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(how='all', thresh=None)
  • C. filtered_df = users_raw_df.na.drop(how='any')
  • D. filtered_df = users_raw_df.na.drop(thresh=0)

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 # 81
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 = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
  • B. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
  • C. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
  • 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 # 82
What is a feature of Spark Connect?

  • A. It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
  • B. Supports DataFrame, Functions, Column, SparkContext PySpark APIs
  • C. It has built-in authentication
  • D. It supports only PySpark applications

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Spark Connect is a client-server architecture introduced in Apache Spark 3.4, designed to decouple the client from the Spark driver, enabling remote connectivity to Spark clusters.
According to the Spark 3.5.5 documentation:
"Majority of the Streaming API is supported, including DataStreamReader, DataStreamWriter, StreamingQuery and StreamingQueryListener." This indicates that Spark Connect supports key components of Structured Streaming, allowing for robust streaming data processing capabilities.
Regarding other options:
B).While Spark Connect supports DataFrame, Functions, and Column APIs, it does not support SparkContext and RDD APIs.
C).Spark Connect supports multiple languages, including PySpark and Scala, not just PySpark.
D).Spark Connect does not have built-in authentication but is designed to work seamlessly with existing authentication infrastructures.


NEW QUESTION # 83
......

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