VCE ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 EXAM SIMULATOR | CHEAP ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 DUMPS

VCE Associate-Developer-Apache-Spark-3.5 Exam Simulator | Cheap Associate-Developer-Apache-Spark-3.5 Dumps

VCE Associate-Developer-Apache-Spark-3.5 Exam Simulator | Cheap Associate-Developer-Apache-Spark-3.5 Dumps

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q27-Q32):

NEW QUESTION # 27
Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

  • A. Recreate the users_vw and query the data using Spark
  • B. Save the users_vw definition and query using Spark
  • C. Persist the users_vw data as a table
  • D. Query the users_vw using Spark

Answer: C

Explanation:
Temp views likecreateOrReplaceTempVieware session-scoped.
They disappear once the Spark session ends.
To retain data across sessions, it must be persisted:
df.write.saveAsTable("users_vw")
Thus, the view needs to be persisted as a table to survive session termination.
Reference:Databricks - Temp vs Global vs Permanent Views


NEW QUESTION # 28
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='bambi'), Row(name='alladin', age=20)]
  • B. [Row(name='alladin', age=20)]
  • C. [Row(name='bambi', age=None), Row(name='alladin', age=20)]
  • D. The code throws an error due to a schema mismatch.

Answer: C

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 # 29
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. Add.remote("sc://localhost")to their SparkSession.builder calls in their Spark code
  • B. Execute their pyspark shell with the option--remote "https://localhost"
  • C. Set the environment variableSPARK_REMOTE="sc://localhost"before starting the pyspark shell
  • D. Ensure the Spark propertyspark.connect.grpc.binding.portis set to 15002 in the application code
  • E. Execute their pyspark shell with the option--remote "sc://localhost"

Answer: C,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 # 30
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, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
  • D. In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs

Answer: B

Explanation:
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.


NEW QUESTION # 31
Given the schema:

event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:

  • A. groupBy without aggregation (invalid use)
  • B. dropDuplicates on all columns (wrong criteria)
  • C. dropDuplicates on the exact matching fields
  • D. dropDuplicates with no arguments (removes based on all columns)

Answer: C

Explanation:
dedup_df = iot_bronze_df.dropDuplicates(["event_ts","sensor_id","metric_value"]) dropDuplicates accepts a list of columns to use for deduplication.
This ensures only unique records based on the specified keys are retained.
Reference:DataFrame.dropDuplicates() API


NEW QUESTION # 32
......

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