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

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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:

Question #1
Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A. Persist the users_vw data as a table
B. Recreate the users_vw and query the data using Spark
C. Save the users_vw definition and query using Spark
D. Query the users_vw using Spark

Question #2
35 of 55.
A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off.
How can this be achieved?

A. By configuring the option checkpointLocation during writeStream.
B. By configuring the option recoveryLocation during SparkSession initialization.
C. By configuring the option recoveryLocation during writeStream.
D. By configuring the option checkpointLocation during readStream.

Question #3
A data engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A. df.write.partitionBy("country").parquet("/data/output")
B. df.write.mode("append").partitionBy("country").parquet("/data/output")
C. df.write.mode("overwrite").parquet("/data/output")
D. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")

Question #4
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 has built-in fault tolerance, allowing it to recover seamlessly from node failures during computation.
B. It requires specialized hardware to run, making it unsuitable for commodity hardware clusters.
C. It processes data solely on disk storage, reducing the need for memory resources.
D. It can distribute data processing tasks across a cluster of machines, enabling horizontal scalability.
E. It eliminates the need to write any code, automatically handling all data processing.

Question #5
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 optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
C. It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
D. It allows Spark to optimize the query plan before execution but does not adapt during runtime.

Solutions: