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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q89-Q94):

NEW QUESTION # 89
A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

A. Use the distinct() transformation to combine similar partitions
B. Use the sortBy() transformation to reorganize the data
C. Use the repartition() transformation with a lower number of partitions
D. Use the coalesce() transformation with a lower number of partitions

Answer: D

Explanation:
coalesce(n) reduces the number of partitions without triggering a full shuffle, unlike repartition().
This is ideal when reducing partition count, especially during write operations.
Reference:Spark API - coalesce

NEW QUESTION # 90
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 in event_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

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

Answer: B

Explanation:
The method dropDuplicatesWithinWatermark() 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 # 91
In the code block below, aggDF contains 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. aggregate
B. complete
C. replace
D. append

Answer: B

Explanation:
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

NEW QUESTION # 92
Which command overwrites an existing JSON file when writing a DataFrame?

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

Answer: D

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 # 93
What is a feature of Spark Connect?

A. It has built-in authentication
B. It supports only PySpark applications
C. It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
D. Supports DataFrame, Functions, Column, SparkContext PySpark APIs

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Spark Connect is a client-server architecture introduced in Apache Spark 3.4, designed to decouple the client from the Spark driver, enabling remote connectivity to Spark clusters.
According to the Spark 3.5.5 documentation:
"Majority of the Streaming API is supported, including DataStreamReader, DataStreamWriter, StreamingQuery and StreamingQueryListener." This indicates that Spark Connect supports key components of Structured Streaming, allowing for robust streaming data processing capabilities.
Regarding other options:
B).While Spark Connect supports DataFrame, Functions, and Column APIs, it does not support SparkContext and RDD APIs.
C).Spark Connect supports multiple languages, including PySpark and Scala, not just PySpark.
D).Spark Connect does not have built-in authentication but is designed to work seamlessly with existing authentication infrastructures.

NEW QUESTION # 94
......

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