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【Hardware】 Dumps Associate-Developer-Apache-Spark-3.5 Discount | Reliable Associate-Develop

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q31-Q36):NEW QUESTION # 31
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:
  • A. A job contains multiple tasks, and each task contains multiple stages.
  • B. A job contains multiple stages, and each stage contains multiple tasks.
  • C. A stage contains multiple jobs, and each job contains multiple tasks.
  • D. A stage contains multiple tasks, and each task contains multiple jobs.
Answer: B
Explanation:
A Spark job is triggered by an action (e.g., count, show).
The job is broken into stages, typically one per shuffle boundary.
Each stage is divided into multiple tasks, which are distributed across worker nodes.

NEW QUESTION # 32
A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?
  • A. The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
  • B. The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame
  • C. The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
  • D. The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
Answer: C
Explanation:
In Apache Spark, broadcast variables are used to efficiently distribute large, read-only data to all worker nodes. However, broadcasting very large datasets can lead to memory issues on executors if the data does not fit into the available memory.
According to the Spark documentation:
"Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. This can greatly reduce the amount of data sent over the network." However, it also notes:
"Using the broadcast functionality available in SparkContext can greatly reduce the size of each serialized task, and the cost of launching a job over a cluster. If your tasks use any large object from the driver program inside of them (e.g., a static lookup table), consider turning it into a broadcast variable." But caution is advised when broadcasting large datasets:
"Broadcasting large variables can cause out-of-memory errors if the data does not fit in the memory of each executor." Therefore, if the broadcasted DataFrame containing millions of rows exceeds the memory capacity of the executors, the job may fail due to memory constraints.

NEW QUESTION # 33
A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:
  • A. Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.
  • B. Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
  • C. Uses trigger(continuous='5 seconds') - continuous processing mode.
  • D. Uses trigger() - default micro-batch trigger without interval.
Answer: A
Explanation:
To define a micro-batch interval, the correct syntax is:
query = df.writeStream
outputMode("append")
trigger(processingTime='5 seconds')
start()
This schedules the query to execute every 5 seconds.
Continuous mode (used in Option A) is experimental and has limited sink support.
Option D is incorrect because processingTime must be a string (not an integer).
Option B triggers as fast as possible without interval control.
Reference:Spark Structured Streaming - Triggers

NEW QUESTION # 34
Which Spark configuration controls the number of tasks that can run in parallel on the executor?
Options:
  • A. spark.driver.cores
  • B. spark.executor.memory
  • C. spark.executor.cores
  • D. spark.task.maxFailures
Answer: C
Explanation:
spark.executor.cores determines how many concurrent tasks an executor can run.
For example, if set to 4, each executor can run up to 4 tasks in parallel.
Other settings:
spark.task.maxFailures controls task retry logic.
spark.driver.cores is for the driver, not executors.
spark.executor.memory sets memory limits, not task concurrency.

NEW QUESTION # 35
38 of 55.
A data engineer is working with Spark SQL and has a large JSON file stored at /data/input.json.
The file contains records with varying schemas, and the engineer wants to create an external table in Spark SQL that:
Reads directly from /data/input.json.
Infers the schema automatically.
Merges differing schemas.
Which code snippet should the engineer use?
  • 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', mergeAll 'true');
  • C. CREATE EXTERNAL TABLE users
    USING json
    OPTIONS (path '/data/input.json', inferSchema 'true');
  • D. CREATE TABLE users
    USING json
    OPTIONS (path '/data/input.json');
Answer: A
Explanation:
To handle JSON files with evolving or differing schemas, Spark SQL supports the option mergeSchema 'true', which merges all fields across files into a unified schema.
Correct syntax:
CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeSchema 'true');
This creates an external table directly on the JSON data, inferring schema automatically and merging variations.
Why the other options are incorrect:
B: Missing schema merge configuration - fails with inconsistent files.
C: inferSchema applies to CSV/other file types, not JSON.
D: mergeAll is not a valid Spark SQL option.
Reference:
Spark SQL Data Sources - JSON file options (mergeSchema, path).
Databricks Exam Guide (June 2025): Section "Using Spark SQL" - creating external tables and schema inference for JSON data.

NEW QUESTION # 36
......
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