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Databricks Associate-Developer-Apache-Spark-3.5 Exams & Valid Associate-Developer-Apache-Spark-3.5 Exam Answers

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q40-Q45):

NEW QUESTION # 40
A data engineer needs to write a DataFramedfto a Parquet file, partitioned by the columncountry, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
B. df.write.mode("append").partitionBy("country").parquet("/data/output")
C. df.write.partitionBy("country").parquet("/data/output")
D. df.write.mode("overwrite").parquet("/data/output")

Answer: A

Explanation:
The.mode("overwrite")ensures that existing files at the path will be replaced.
partitionBy("country")optimizes queries by writing data into partitioned folders.
Correct syntax:
df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
- Source:Spark SQL, DataFrames and Datasets Guide

NEW QUESTION # 41
Given the code:

df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp")," ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?

A. When the groupBy transformation is applied
B. When the count action is applied
C. When the filter transformation is applied
D. When the show action is applied

Answer: B

Explanation:
Spark uses lazy evaluation. Transformations like filter, select, and groupBy only define the DAG (Directed Acyclic Graph). No execution occurs until an action is triggered.
The first action in the code is:reduced_df.count()
So Spark starts processing data at this line.
Reference:Apache Spark Programming Guide - Lazy Evaluation

NEW QUESTION # 42
What is the benefit of using Pandas on Spark for data transformations?
Options:

A. It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
B. It computes results immediately using eager execution, making it simple to use.
C. It is available only with Python, thereby reducing the learning curve.
D. It runs on a single node only, utilizing the memory with memory-bound DataFrames and hence cost- efficient.

Answer: A

Explanation:
Pandas API on Spark (formerly Koalas) offers:
Familiar Pandas-like syntax
Distributed execution using Spark under the hood
Scalability for large datasets across the cluster
It provides the power of Spark while retaining the productivity of Pandas.
Reference:Pandas API on Spark Guide

NEW QUESTION # 43
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 = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
C. exploded_df = exploded_df.select("record_datetime", "record_exploded")
D. exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")

Answer: A

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 # 44
Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?

A. It is primarily used for data ingestion into Spark from external sources
B. It provides a way to run Spark applications remotely in any programming language
C. It can be used to interact with any remote cluster using the REST API
D. It allows for remote execution of Spark jobs

Answer: D

Explanation:
Comprehensive and Detailed Explanation:
Spark Connect introduces a decoupled client-server architecture. Its key feature is enabling Spark job submission and execution from remote clients - in Python, Java, etc.
From Databricks documentation:
"Spark Connect allows remote clients to connect to a Spark cluster and execute Spark jobs without being co- located with the Spark driver." A is close, but "any language" is overstated (currently supports Python, Java, etc., not literally all).
B refers to REST, which is not Spark Connect's mechanism.
D is incorrect; Spark Connect isn't focused on ingestion.
Final Answer: C

NEW QUESTION # 45
......

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