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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. 18 of 55.
An engineer has two DataFrames - df1 (small) and df2 (large). To optimize the join, the engineer uses a broadcast join:
from pyspark.sql.functions import broadcast
df_result = df2.join(broadcast(df1), on="id", how="inner")
What is the purpose of using broadcast() in this scenario?

A) It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
B) It ensures that the join happens only when the id values are identical.
C) It filters the id values before performing the join.
D) It increases the partition size for df1 and df2.

2. 3 of 55. A data engineer observes that the upstream streaming source feeds the event table frequently and sends duplicate records. Upon analyzing the current production table, the data engineer found that the time difference in the event_timestamp column of the duplicate records is, at most, 30 minutes.
To remove the duplicates, the engineer adds the code:
df = df.withWatermark("event_timestamp", "30 minutes")
What is the result?

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

3. What is the benefit of Adaptive Query Execution (AQE)?

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

4. 27 of 55.
A data engineer needs to add all the rows from one table to all the rows from another, but not all the columns in the first table exist in the second table.
The error message is:
AnalysisException: UNION can only be performed on tables with the same number of columns.
The existing code is:
au_df.union(nz_df)
The DataFrame au_df has one extra column that does not exist in the DataFrame nz_df, but otherwise both DataFrames have the same column names and data types.
What should the data engineer fix in the code to ensure the combined DataFrame can be produced as expected?

A) df = au_df.unionByName(nz_df, allowMissingColumns=False)
B) df = au_df.unionAll(nz_df)
C) df = au_df.unionByName(nz_df, allowMissingColumns=True)
D) df = au_df.union(nz_df, allowMissingColumns=True)

5. 23 of 55.
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)

A) It can distribute data processing tasks across a cluster of machines, enabling horizontal scalability.
B) It has built-in fault tolerance, allowing it to recover seamlessly from node failures during computation.
C) It processes data solely on disk storage, reducing the need for memory resources.
D) It eliminates the need to write any code, automatically handling all data processing.
E) It requires specialized hardware to run, making it unsuitable for commodity hardware clusters.

Solutions: