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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q82-Q87):

NEW QUESTION # 82
A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?

A. .trigger(continuous=True)
B. .trigger(continuous='1 second')
C. .trigger(processingTime='1 second')
D. .trigger(availableNow=True)

Answer: C

Explanation:
Comprehensive and Detailed Explanation:
Exactly-once guarantees in Spark Structured Streaming require micro-batch mode (default), not continuous mode.
Continuous mode (.trigger(continuous=...)) only supports at-least-once semantics and lacks full fault- tolerance.
trigger(availableNow=True)is a batch-style trigger, not suited for low-latency streaming.
So:
Option A uses micro-batching with a tight trigger interval # minimal latency + exactly-once guarantee.
Final Answer: A

NEW QUESTION # 83
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 is not able to handle deduplication in this scenario
B. It removes duplicates that arrive within the 30-minute window specified by the watermark
C. It accepts watermarks in seconds and the code results in an error
D. It removes all duplicates regardless of when they arrive

Answer: B

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 # 84
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").distinct().orderBy(df["Name"].desc())
B. df.select("Name").orderBy(df["Name"].asc())
C. df.select("Name").distinct().orderBy(df["Name"])
D. df.select("Name").distinct()

Answer: C

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 # 85
A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

B)

C)

D)

Which code fragment should be inserted to meet the requirement?

A. CopyEdit
.option("format", "parquet")
.option("destination", "path/to/destination/dir")
B. .format("parquet")
.option("path", "path/to/destination/dir")
C. .option("format", "parquet")
.option("location", "path/to/destination/dir")
D. .format("parquet")
.option("location", "path/to/destination/dir")

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To write a structured streaming DataFrame to Parquet files, the correct way to specify the format and output directory is:
writeStream
format("parquet")
option("path", "path/to/destination/dir")
According to Spark documentation:
"When writing to file-based sinks (like Parquet), you must specify the path using the .option("path", ...) method. Unlike batch writes, .save() is not supported." Option A incorrectly uses.option("location", ...)(invalid for Parquet sink).
Option B incorrectly sets the format via.option("format", ...), which is not the correct method.
Option C repeats the same issue.
Option D is correct:.format("parquet")+.option("path", ...)is the required syntax.
Final Answer: D

NEW QUESTION # 86
A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set forspark.sql.
adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A. A broadcast nested loop join
B. A sort-merge join
C. A Cartesian join
D. A shuffled hash join

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Adaptive Query Execution (AQE) dynamically selects join strategies based on actual data sizes at runtime. If the size of post-shuffle partitions is below the threshold set by:
spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold
then Spark prefers to use a shuffled hash join.
From the Spark documentation:
"AQE selects a shuffled hash join when the size of post-shuffle data is small enough to fit within the configured threshold, avoiding more expensive sort-merge joins." Therefore:
A is wrong - Cartesian joins are only used with no join condition.
B is correct - this is the optimized join for small partitioned shuffle data under AQE.
C and D are used under other scenarios but not for this case.
Final Answer: B

NEW QUESTION # 87
......

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