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最新的 Databricks Certification Associate-Developer-Apache-Spark-3.5 免費考試真題 (Q42-Q47):

問題 #42
A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?

A. In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
B. In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
C. In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
D. In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.

答案：B

解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark's cluster mode:
"The driver program runs on the cluster's worker node instead of the client's local machine. This allows the driver to be close to the data and other executors, reducing network overhead and improving fault tolerance for production jobs." (Source: Apache Spark documentation -Cluster Mode Overview) This deployment is ideal for production environments where the job is submitted from a gateway node, and Spark manages the driver lifecycle on the cluster itself.
Option A is partially true but less specific than D.
Option B is incorrect: the driver never executes all tasks; executors handle distributed tasks.
Option C describes client mode, not cluster mode.

問題 #43
A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?

A. Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
B. Use spark.read.json() with the inferSchema option set to true
C. Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.
D. Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.

答案：A

解題說明：
The most efficient and correct approach is to define a schema using StructType and pass it to spark.read.schema(...).
This avoids schema inference overhead and ensures proper data types are enforced during read.
Example:
from pyspark.sql.types import StructType, StructField, StringType, DoubleType schema = StructType([ StructField("order_id", StringType(), True), StructField("amount", DoubleType(), True),
...
])
df = spark.read.schema(schema).json("path/to/json")
- Source: Databricks Guide - Read JSON with predefined schema

問題 #44
43 of 55.
An organization has been running a Spark application in production and is considering disabling the Spark History Server to reduce resource usage.
What will be the impact of disabling the Spark History Server in production?

A. Improved job execution speed due to reduced logging overhead
B. Prevention of driver log accumulation during long-running jobs
C. Enhanced executor performance due to reduced log size
D. Loss of access to past job logs and reduced debugging capability for completed jobs

答案：D

解題說明：
The Spark History Server provides a web UI for viewing past completed applications, including event logs, stages, and performance metrics.
If disabled:
Spark jobs still run normally,
But users lose the ability to review historical job metrics, DAGs, or logs after completion.
Thus, debugging, performance analysis, and audit capabilities are lost.
Why the other options are incorrect:
A: Disabling History Server doesn't manage logs.
B/D: Minimal overhead; disabling doesn't improve runtime speed or executor performance.
Reference:
Databricks Exam Guide (June 2025): Section "Apache Spark Architecture and Components" - Spark UI, History Server, and event logging.
Spark Administration Docs - History Server functionality and configuration.

問題 #45
44 of 55.
A data engineer is working on a real-time analytics pipeline using Spark Structured Streaming.
They want the system to process incoming data in micro-batches at a fixed interval of 5 seconds.
Which code snippet fulfills this requirement?

A. query = df.writeStream
.outputMode("append")
.trigger(processingTime="5 seconds")
.start()
B. query = df.writeStream
.outputMode("append")
.trigger(once=True)
.start()
C. query = df.writeStream
.outputMode("append")
.trigger(continuous="5 seconds")
.start()
D. query = df.writeStream
.outputMode("append")
.start()

答案：A

解題說明：
To process data in fixed micro-batch intervals, use the .trigger(processingTime="interval") option in Structured Streaming.
Correct usage:
query = df.writeStream
.outputMode("append")
.trigger(processingTime="5 seconds")
.start()
This instructs Spark to process available data every 5 seconds.
Why the other options are incorrect:
B: continuous triggers are for continuous processing mode (different execution model).
C: once=True runs the stream a single time (batch mode).
D: Default trigger runs as fast as possible, not fixed intervals.
Reference:
PySpark Structured Streaming Guide - Trigger types: processingTime, once, continuous.
Databricks Exam Guide (June 2025): Section "Structured Streaming" - controlling streaming triggers and batch intervals.

問題 #46
10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?

A. It runs on a single node only, utilizing memory efficiently.
B. It is available only with Python, thereby reducing the learning curve.
C. It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
D. It computes results immediately using eager execution.

答案：C

解題說明：
Pandas API on Spark provides a distributed implementation of the Pandas DataFrame API on top of Apache Spark.
Advantages:
Executes transformations in parallel across all nodes and cores in the cluster.
Maintains Pandas-like syntax, making it easy for Python users to transition.
Enables scaling of existing Pandas code to handle large datasets without memory limits.
Therefore, it combines Pandas usability with Spark's distributed power, offering both speed and scalability.
Why the other options are incorrect:
B: While it uses Python, that's not its main advantage.
C: It runs distributed across the cluster, not on a single node.
D: Pandas API on Spark uses lazy evaluation, not eager computation.
Reference:
PySpark Pandas API Overview - advantages of distributed execution.
Databricks Exam Guide (June 2025): Section "Using Pandas API on Apache Spark" - explains the benefits of Pandas API integration for scalable transformations.

問題 #47
......

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