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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

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

2. 17 of 55.
A data engineer has noticed that upgrading the Spark version in their applications from Spark 3.0 to Spark 3.5 has improved the runtime of some scheduled Spark applications.
Looking further, the data engineer realizes that Adaptive Query Execution (AQE) is now enabled.
Which operation should AQE be implementing to automatically improve the Spark application performance?

A) Optimizing the layout of Delta files on disk
B) Dynamically switching join strategies
C) Collecting persistent table statistics and storing them in the metastore for future use
D) Improving the performance of single-stage Spark jobs

3. A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data. The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by the market_time field.
Which line of Spark code will produce a Parquet table that meets these requirements?

A) final_df \
.sort("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
B) final_df \
.sortWithinPartitions("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
C) final_df \
.orderBy("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
D) final_df \
.sort("market_time") \
.coalesce(1) \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")

4. A developer wants to test Spark Connect with an existing Spark application.
What are the two alternative ways the developer can start a local Spark Connect server without changing their existing application code? (Choose 2 answers)

A) Execute their pyspark shell with the option --remote "sc://localhost"
B) Set the environment variable SPARK_REMOTE="sc://localhost" before starting the pyspark shell
C) Execute their pyspark shell with the option --remote "https://localhost"
D) Add .remote("sc://localhost") to their SparkSession.builder calls in their Spark code
E) Ensure the Spark property spark.connect.grpc.binding.port is set to 15002 in the application code

5. Given the following code snippet in my_spark_app.py:

What is the role of the driver node?

A) The driver node only provides the user interface for monitoring the application
B) The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes
C) The driver node holds the DataFrame data and performs all computations locally
D) The driver node stores the final result after computations are completed by worker nodes

Solutions: