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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q82-Q87):
NEW QUESTION # 82
Given the code fragment:
import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?
Answer: D
Explanation:
Pandas API on Spark (pyspark.pandas) allows interoperability with PySpark DataFrames. To convert a pyspark.pandas.DataFrame to a standard PySpark DataFrame, you use .to_spark().
Example:
df = psdf.to_spark()
This is the officially supported method as per Databricks Documentation.
Incorrect options:
B, D: Invalid or nonexistent methods.
C: Converts to a local pandas DataFrame, not a PySpark DataFrame.
NEW QUESTION # 83
28 of 55.
A data analyst builds a Spark application to analyze finance data and performs the following operations:
filter, select, groupBy, and coalesce.
Which operation results in a shuffle?
Answer: D
Explanation:
In Spark, a shuffle occurs when data needs to be redistributed across partitions or nodes, typically due to operations that require grouping, joining, or sorting.
groupBy triggers a shuffle because it aggregates data based on key values, requiring data from multiple partitions to be moved across the cluster.
Operations like filter and select are narrow transformations (no shuffle).
coalesce can reduce the number of partitions without a full shuffle (unlike repartition).
Why the other options are incorrect:
A (filter) - Narrow transformation, no shuffle.
B (select) - Simple projection, no data movement.
D (coalesce) - Reduces partitions locally without shuffle.
Reference:
Spark Architecture - Transformations and Actions; narrow vs. wide transformations.
Databricks Exam Guide (June 2025): Section "Apache Spark Architecture and Components" - explains shuffle operations and execution hierarchy.
NEW QUESTION # 84
Given the schema:
event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:
Answer: B
Explanation:
dedup_df = iot_bronze_df.dropDuplicates(["event_ts","sensor_id","metric_value"]) dropDuplicates accepts a list of columns to use for deduplication.
This ensures only unique records based on the specified keys are retained.
Reference:DataFrame.dropDuplicates() API
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?
Answer: B
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
A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:
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.
NEW QUESTION # 87
......
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