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

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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. The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?

A) @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159
B) @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159
C) @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159
D) @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159

2. A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A) The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
B) The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame
C) The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
D) The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted

3. In the code block below, aggDF contains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) complete
B) aggregate
C) append
D) replace

4. A Spark application is experiencing performance issues in client mode because the driver is resource-constrained.
How should this issue be resolved?

A) Add more executor instances to the cluster
B) Switch the deployment mode to local mode
C) Switch the deployment mode to cluster mode
D) Increase the driver memory on the client machine

5. 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 all duplicates regardless of when they arrive.
D) It removes duplicates that arrive within the 30-minute window specified by the watermark.

Solutions: