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Quiz 2025 Associate-Developer-Apache-Spark-3.5: Efficient Exam Dumps Databricks Certified Associate Developer for Apache Spark 3.5 - Python Zip

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q51-Q56):

NEW QUESTION # 51
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 duplicates that arrive within the 30-minute window specified by the watermark
B. It removes all duplicates regardless of when they arrive
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: A

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 # 52
A developer wants to test Spark Connect with an existing Spark application.
What are the two alternative ways the developer can start a local Spark Connect server without changing their existing application code? (Choose 2 answers)

A. Ensure the Spark propertyspark.connect.grpc.binding.portis set to 15002 in the application code
B. Execute their pyspark shell with the option--remote "sc://localhost"
C. Add.remote("sc://localhost")to their SparkSession.builder calls in their Spark code
D. Execute their pyspark shell with the option--remote "https://localhost"
E. Set the environment variableSPARK_REMOTE="sc://localhost"before starting the pyspark shell

Answer: B,E

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Spark Connect enables decoupling of the client and Spark driver processes, allowing remote access. Spark supports configuring the remote Spark Connect server in multiple ways:
From Databricks and Spark documentation:
Option B (--remote "sc://localhost") is a valid command-line argument for thepysparkshell to connect using Spark Connect.
Option C (settingSPARK_REMOTEenvironment variable) is also a supported method to configure the remote endpoint.
Option A is incorrect because Spark Connect uses thesc://protocol, nothttps://.
Option D requires modifying the code, which the question explicitly avoids.
Option E configures the port on the server side but doesn't start a client connection.
Final Answers: B and C

NEW QUESTION # 53
Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

A. [Row(name='alladin', age=20)]
B. The code throws an error due to a schema mismatch.
C. [Row(name='bambi'), Row(name='alladin', age=20)]
D. [Row(name='bambi', age=None), Row(name='alladin', age=20)]

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Spark, when a CSV row does not match the provided schema, Spark does not raise an error by default.
Instead, it returnsnullfor fields that cannot be parsed correctly.
In the first row,"hello"cannot be cast to Integer for theagefield # Spark setsage=None In the second row,"20"is a valid integer #age=20 So the output will be:
[Row(name='bambi', age=None), Row(name='alladin', age=20)]
Final Answer: C

NEW QUESTION # 54
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', mergeSchema
'true')
B. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge
'true')
C. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')
D. CREATE TABLE users USING json OPTIONS (path '/data/input.json')

Answer: A

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 # 55
An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A. Use the commandspark-submitwith the-verboseflag to print the logs to the console.
B. Fetch the logs by running a Spark job with thespark-sqlCLI tool.
C. Use the Spark UI to select the stage and view the executor logs directly from the stages tab.
D. Locate the executor logs on the Spark master node, typically under the/tmpdirectory.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The Spark UI is the standard and most effective way to inspect executor logs, task time, input size, and shuffles.
From the Databricks documentation:
"You can monitor job execution via the Spark Web UI. It includes detailed logs and metrics, including task- level execution time, shuffle reads/writes, and executor memory usage."
(Source: Databricks Spark Monitoring Guide) Option A is incorrect: logs are not guaranteed to be in/tmp, especially in cloud environments.
B).-verbosehelps during job submission but doesn't give detailed executor logs.
D).spark-sqlis a CLI tool for running queries, not for inspecting logs.
Hence, the correct method is using the Spark UI # Stages tab # Executor logs.

NEW QUESTION # 56
......

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