ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 EXAM DUMPS PROVIDER & CERTIFICATION ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 DUMP

Associate-Developer-Apache-Spark-3.5 Exam Dumps Provider & Certification Associate-Developer-Apache-Spark-3.5 Dump

Associate-Developer-Apache-Spark-3.5 Exam Dumps Provider & Certification Associate-Developer-Apache-Spark-3.5 Dump

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q35-Q40):

NEW QUESTION # 35
Which command overwrites an existing JSON file when writing a DataFrame?

  • A. df.write.overwrite.json("path/to/file")
  • B. df.write.mode("overwrite").json("path/to/file")
  • C. df.write.json("path/to/file", overwrite=True)
  • D. df.write.format("json").save("path/to/file", mode="overwrite")

Answer: B

Explanation:
The correct way to overwrite an existing file using the DataFrameWriter is:
df.write.mode("overwrite").json("path/to/file")
Option D is also technically valid, but Option A is the most concise and idiomatic PySpark syntax.
Reference:PySpark DataFrameWriter API


NEW QUESTION # 36
A data engineer is working on the DataFrame:

(Referring to the table image: it has columnsId,Name,count, andtimestamp.) Which code fragment should the engineer use to extract the unique values in theNamecolumn into an alphabetically ordered list?

  • A. df.select("Name").orderBy(df["Name"].asc())
  • B. df.select("Name").distinct().orderBy(df["Name"])
  • C. df.select("Name").distinct()
  • D. df.select("Name").distinct().orderBy(df["Name"].desc())

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To extract unique values from a column and sort them alphabetically:
distinct()is required to remove duplicate values.
orderBy()is needed to sort the results alphabetically (ascending by default).
Correct code:
df.select("Name").distinct().orderBy(df["Name"])
This is directly aligned with standard DataFrame API usage in PySpark, as documented in the official Databricks Spark APIs. Option A is incorrect because it may not remove duplicates. Option C omits sorting.
Option D sorts in descending order, which doesn't meet the requirement for alphabetical (ascending) order.


NEW QUESTION # 37
The following code fragment results in an error:

Which code fragment should be used instead?

  • A.
  • B.
  • C.
  • D.

Answer: D


NEW QUESTION # 38
A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

  • A. saveAsTable with mode Overwrite
  • B. save with mode ErrorIfExists
  • C. saveAsTable with mode ErrorIfExists
  • D. save with mode Ignore

Answer: C

Explanation:
Comprehensive and Detailed Explanation:
The methodsaveAsTable()creates a new table and optionally fails if the table exists.
From Spark documentation:
"The mode 'ErrorIfExists' (default) will throw an error if the table already exists." Thus:
Option A is correct.
Option B (Overwrite) would overwrite existing data - not acceptable here.
Option C and D usesave(), which doesn't create a managed table with metadata in the metastore.
Final Answer: A


NEW QUESTION # 39
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, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
  • B. In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
  • 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, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.

Answer: D

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 # 40
......

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