NEWEST DATABRICKS CERTIFIED ASSOCIATE DEVELOPER FOR APACHE SPARK 3.5 - PYTHON VALID QUESTIONS - ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 UPDATED TORRENT & ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 RELIABLE TRAINING

Newest Databricks Certified Associate Developer for Apache Spark 3.5 - Python Valid Questions - Associate-Developer-Apache-Spark-3.5 Updated Torrent & Associate-Developer-Apache-Spark-3.5 Reliable Training

Newest Databricks Certified Associate Developer for Apache Spark 3.5 - Python Valid Questions - Associate-Developer-Apache-Spark-3.5 Updated Torrent & Associate-Developer-Apache-Spark-3.5 Reliable Training

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q28-Q33):

NEW QUESTION # 28
A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference inevent_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

  • A. It removes all duplicates regardless of when they arrive
  • B. It removes duplicates that arrive within the 30-minute window specified by the watermark
  • C. It accepts watermarks in seconds and the code results in an error
  • D. It is not able to handle deduplication in this scenario

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The methoddropDuplicatesWithinWatermark()in Structured Streaming drops duplicate records based on a specified column and watermark window. The watermark defines the threshold for how late data is considered valid.
From the Spark documentation:
"dropDuplicatesWithinWatermark removes duplicates that occur within the event-time watermark window." In this case, Spark will retain the first occurrence and drop subsequent records within the 30-minute watermark window.
Final Answer: B


NEW QUESTION # 29
A data engineer wants to create an external table from a JSON file located at/data/input.jsonwith the following requirements:
Create an external table namedusers
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:

  • A. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge
    'true')
  • B. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')
  • C. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema
    'true')
  • D. CREATE TABLE users USING json OPTIONS (path '/data/input.json')

Answer: C

Explanation:
To create an external table and enable schema merging, the correct syntax is:
CREATEEXTERNALTABLEusers
USINGjson
OPTIONS (
path'/data/input.json',
mergeSchema'true'
)
mergeSchemais the correct option key (notschemaMerge)
EXTERNALallows Spark to query files without managing their lifecycle
Reference:Spark SQL DDL - JSON and Schema Merging


NEW QUESTION # 30
A data engineer wants to create a Streaming DataFrame that reads from a Kafka topic called feed.

Which code fragment should be inserted in line 5 to meet the requirement?
Code context:
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers","host1:port1,host2:port2")
.[LINE5]
.load()
Options:

  • A. .option("subscribe.topic", "feed")
  • B. .option("subscribe", "feed")
  • C. .option("kafka.topic", "feed")
  • D. .option("topic", "feed")

Answer: B

Explanation:
Comprehensive and Detailed Explanation:
To read from a specific Kafka topic using Structured Streaming, the correct syntax is:
python
CopyEdit
option("subscribe","feed")
This is explicitly defined in the Spark documentation:
"subscribe - The Kafka topic to subscribe to. Only one topic can be specified for this option." (Source:Apache Spark Structured Streaming + Kafka Integration Guide)
B)."subscribe.topic" is invalid.
C)."kafka.topic" is not a recognized option.
D)."topic" is not valid for Kafka source in Spark.


NEW QUESTION # 31
A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

B)

C)

D)

Which code fragment should be inserted to meet the requirement?

  • A. .format("parquet")
    .option("location", "path/to/destination/dir")
  • B. CopyEdit
    .option("format", "parquet")
    .option("destination", "path/to/destination/dir")
  • C. .format("parquet")
    .option("path", "path/to/destination/dir")
  • D. .option("format", "parquet")
    .option("location", "path/to/destination/dir")

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To write a structured streaming DataFrame to Parquet files, the correct way to specify the format and output directory is:
writeStream
format("parquet")
option("path", "path/to/destination/dir")
According to Spark documentation:
"When writing to file-based sinks (like Parquet), you must specify the path using the .option("path", ...) method. Unlike batch writes, .save() is not supported." Option A incorrectly uses.option("location", ...)(invalid for Parquet sink).
Option B incorrectly sets the format via.option("format", ...), which is not the correct method.
Option C repeats the same issue.
Option D is correct:.format("parquet")+.option("path", ...)is the required syntax.
Final Answer: D


NEW QUESTION # 32
A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?

  • A. Increase the value of spark.sql.shuffle.partitions
  • B. Enable dynamic resource allocation to scale resources as needed
  • C. Increase the size of the dataset to create more partitions
  • D. Reduce the value of spark.sql.shuffle.partitions

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The number of tasks is controlled by the number of partitions. By default,spark.sql.shuffle.partitionsis 200. If stages are showing very few tasks (less than total cores), you may not be leveraging full parallelism.
From the Spark tuning guide:
"To improve performance, especially for large clusters, increasespark.sql.shuffle.partitionsto create more tasks and parallelism." Thus:
A is correct: increasing shuffle partitions increases parallelism
B is wrong: it further reduces parallelism
C is invalid: increasing dataset size doesn't guarantee more partitions D is irrelevant to task count per stage Final Answer: A


NEW QUESTION # 33
......

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