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Associate-Developer-Apache-Spark-3.5 Reliable Exam Test, Detailed Associate-Developer-Apache-Spark-3.5 Study Plan

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q84-Q89):

NEW QUESTION # 84
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 accepts watermarks in seconds and the code results in an error
C. It is not able to handle deduplication in this scenario
D. It removes all duplicates regardless of when they arrive

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 # 85
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

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

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 # 86
Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?

A. spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
B. spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
C. spark.conf.set("spark.sql.execution.arrow.enabled", "true")
D. spark.conf.set("spark.pandas.arrow.enabled", "true")

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Apache Arrow is used under the hood to optimize conversion between Pandas and PySpark DataFrames. The correct configuration setting is:
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
From the official documentation:
"This configuration must be enabled to allow for vectorized execution and efficient conversion between Pandas and PySpark using Arrow." Option B is correct.
Options A, C, and D are invalid config keys and not recognized by Spark.
Final Answer: B

NEW QUESTION # 87
What is the benefit of Adaptive Query Execution (AQE)?

A. It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
B. It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
C. It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
D. It allows Spark to optimize the query plan before execution but does not adapt during runtime.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Adaptive Query Execution (AQE) is a powerful optimization framework introduced in Apache Spark 3.0 and enabled by default since Spark 3.2. It dynamically adjusts query execution plans based on runtime statistics, leading to significant performance improvements. The key benefits of AQE include:
Dynamic Join Strategy Selection: AQE can switch join strategies at runtime. For instance, it can convert a sort-merge join to a broadcast hash join if it detects that one side of the join is small enough to be broadcasted, thus optimizing the join operation .
Handling Skewed Data: AQE detects skewed partitions during join operations and splits them into smaller partitions. This approach balances the workload across tasks, preventing scenarios where certain tasks take significantly longer due to data skew .
Coalescing Post-Shuffle Partitions: AQE dynamically coalesces small shuffle partitions into larger ones based on the actual data size, reducing the overhead of managing numerous small tasks and improving overall query performance .
These runtime optimizations allow Spark to adapt to the actual data characteristics during query execution, leading to more efficient resource utilization and faster query processing times.

NEW QUESTION # 88
Given a DataFramedfthat has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?

A. 0
B. 1
C. 2
D. Same number as the cluster executors

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The.coalesce(numPartitions)function is used to reduce the number of partitions in a DataFrame. It does not increase the number of partitions. If the specified number of partitions is greater than the current number, it will not have any effect.
From the official Spark documentation:
"coalesce() results in a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim one or more of the current partitions." However, if you try to increase partitions using coalesce (e.g., from 10 to 20), the number of partitions remains unchanged.
Hence,df.coalesce(20)will still return a DataFrame with 10 partitions.
Reference: Apache Spark 3.5 Programming Guide # RDD and DataFrame Operations # coalesce()

NEW QUESTION # 89
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