PURCHASE DATABRICKS ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 EXAM QUESTIONS TODAY FOR HASSLE-FREE PREPARATION

Purchase Databricks Associate-Developer-Apache-Spark-3.5 Exam Questions Today for Hassle-Free Preparation

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q64-Q69):

NEW QUESTION # 64
In the code block below,aggDFcontains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

  • A. append
  • B. replace
  • C. complete
  • D. aggregate

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct output mode for streaming aggregations that need to output the full updated results at each trigger is"complete".
From the official documentation:
"complete: The entire updated result table will be output to the sink every time there is a trigger." This is ideal for aggregations, such as counts or averages grouped by a key, where the result table changes incrementally over time.
append: only outputs newly added rows
replace and aggregate: invalid values for output mode
Reference: Spark Structured Streaming Programming Guide # Output Modes


NEW QUESTION # 65
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

  • A. The conversion will automatically distribute the data across worker nodes
  • B. Data will be lost during conversion
  • C. The operation will load all data into the driver's memory, potentially causing memory overflow
  • D. The operation will fail if the Pandas DataFrame exceeds 1000 rows

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When you convert a largepyspark.pandas(aka Pandas API on Spark) DataFrame to a local Pandas DataFrame using.toPandas(), Spark collects all partitions to the driver.
From the Spark documentation:
"Be careful when converting large datasets to Pandas. The entire dataset will be pulled into the driver's memory." Thus, for large datasets, this can cause memory overflow or out-of-memory errors on the driver.
Final Answer: D


NEW QUESTION # 66
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 removes duplicates that arrive within the 30-minute window specified by the watermark
  • B. It removes all duplicates regardless of when they arrive
  • C. It is not able to handle deduplication in this scenario
  • D. It accepts watermarks in seconds and the code results in an error

Answer: A

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 # 67
A data engineer wants to create an external table from a JSON file located at/data/input.jsonwith the following requirements:
Create an external table namedusers
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:

  • A. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema
    'true')
  • B. CREATE TABLE users USING json OPTIONS (path '/data/input.json')
  • C. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')
  • D. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge
    'true')

Answer: A

Explanation:
To create an external table and enable schema merging, the correct syntax is:
CREATEEXTERNALTABLEusers
USINGjson
OPTIONS (
path'/data/input.json',
mergeSchema'true'
)
mergeSchemais the correct option key (notschemaMerge)
EXTERNALallows Spark to query files without managing their lifecycle
Reference:Spark SQL DDL - JSON and Schema Merging


NEW QUESTION # 68
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 program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
  • B. In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
  • C. In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
  • D. In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs

Answer: A

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 # 69
......

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