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Associate-Developer-Apache-Spark-3.5 Exam Questions Answers, Exam Associate-Developer-Apache-Spark-3.5 Fee

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q34-Q39):

NEW QUESTION # 34
Given the following code snippet inmy_spark_app.py:

What is the role of the driver node?

A. The driver node stores the final result after computations are completed by worker nodes
B. The driver node only provides the user interface for monitoring the application
C. The driver node holds the DataFrame data and performs all computations locally
D. The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In the Spark architecture, the driver node is responsible for orchestrating the execution of a Spark application.
It converts user-defined transformations and actions into a logical plan, optimizes it into a physical plan, and then splits the plan into tasks that are distributed to the executor nodes.
As per Databricks and Spark documentation:
"The driver node is responsible for maintaining information about the Spark application, responding to a user's program or input, and analyzing, distributing, and scheduling work across the executors." This means:
Option A is correct because the driver schedules and coordinates the job execution.
Option B is incorrect because the driver does more than just UI monitoring.
Option C is incorrect since data and computations are distributed across executor nodes.
Option D is incorrect; results are returned to the driver but not stored long-term by it.
Reference: Databricks Certified Developer Spark 3.5 Documentation # Spark Architecture # Driver vs Executors.

NEW QUESTION # 35
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", "feed")
B. .option("kafka.topic", "feed")
C. .option("topic", "feed")
D. .option("subscribe.topic", "feed")

Answer: A

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 # 36
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('customer_id') == F.col('custid'), 'right_outer')
B. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
C. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
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 # 37
Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?

A. WARN, NONE, ERROR, FATAL
B. ALL, DEBUG, FAIL, INFO
C. ERROR, WARN, TRACE, OFF
D. FATAL, NONE, INFO, DEBUG

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
ThesetLogLevel()method ofSparkContextsets the logging level on the driver, which controls the verbosity of logs emitted during job execution. Supported levels are inherited from log4j and include the following:
ALL
DEBUG
ERROR
FATAL
INFO
OFF
TRACE
WARN
According to official Spark and Databricks documentation:
"Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, and WARN." Among the choices provided, only option B (ERROR, WARN, TRACE, OFF) includes four valid log levels and excludes invalid ones like "FAIL" or "NONE".
Reference: Apache Spark API docs # SparkContext.setLogLevel

NEW QUESTION # 38
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 accepts watermarks in seconds and the code results in an error
B. It removes duplicates that arrive within the 30-minute window specified by the watermark
C. It is not able to handle deduplication in this scenario
D. It removes all duplicates regardless of when they arrive

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 # 39
......

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