Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Using Pandas API on Apache Spark | 5% | - Converting between Pandas and Spark structures - Key differences and limitations - Overview of Pandas API on Spark |
| Structured Streaming | 10% | - Defining streaming queries - Fault tolerance and state management - Streaming concepts and architecture - Output modes and triggers |
| Using Spark SQL | 20% | - Working with functions and expressions - Integrating Spark SQL with DataFrames - Using catalog and metadata APIs - Running SQL queries |
| Apache Spark Architecture and Components | 20% | - Fault tolerance and garbage collection - Execution and deployment modes - Shuffling, actions, and broadcasting - Execution hierarchy and lazy evaluation - Spark architecture overview |
| Developing Apache Spark DataFrame API Applications | 30% | - Reading and writing data in various formats - Filtering, sorting, and aggregating data - Creating DataFrames and defining schemas - Joining and combining datasets - Selecting, renaming, and modifying columns - Partitioning and bucketing data - User-defined functions (UDFs) - Handling missing values and data quality |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Debugging and logging - Managing memory and resource usage - Optimizing transformations and actions - Identifying performance bottlenecks |
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture - Running applications via Spark Connect - Connecting to remote Spark clusters |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
33 of 55.
The data engineering team created a pipeline that extracts data from a transaction system.
The transaction system stores timestamps in UTC, and the data engineers must now transform the transaction_datetime field to the "America/New_York" timezone for reporting.
Which code should be used to convert the timestamp to the target timezone?
- A. raw.withColumn("transaction_datetime", from_utc_timestamp(col("transaction_datetime"), "America/New_York"))
- B. raw.withColumn("transaction_datetime", to_utc_timestamp(col("transaction_datetime"), "America/New_York"))
- C. raw.withColumn("transaction_datetime", convert_timezone(col("transaction_datetime"), "America/New_York"))
- D. raw.withColumn("transaction_datetime", date_format(col("transaction_datetime"), "America/New_York"))
Correct Answer: A 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
44 of 55.
A data engineer is working on a real-time analytics pipeline using Spark Structured Streaming.
They want the system to process incoming data in micro-batches at a fixed interval of 5 seconds.
Which code snippet fulfills this requirement?
- A. query = df.writeStream \
.outputMode("append") \
.trigger(once=True) \
.start() - B. query = df.writeStream \
.outputMode("append") \
.trigger(processingTime="5 seconds") \
.start() - C. query = df.writeStream \
.outputMode("append") \
.start() - D. query = df.writeStream \
.outputMode("append") \
.trigger(continuous="5 seconds") \
.start()
Correct Answer: B 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:
- A. The Spark engine requires manual intervention to start executing transformations.
- B. Only actions trigger the execution of the transformation pipeline.
- C. The Spark engine optimizes the execution plan during the transformations, causing delays.
- D. Transformations are executed immediately to build the lineage graph.
- E. Transformations are evaluated lazily.
Correct Answer: B,E 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
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: float) -> float:
return t * 3.14159 - B. @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159 - C. @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159 - D. @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159
Correct Answer: B 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?
- A. streaming_df.select(countDistinct("name"))
- B. streaming_df.count()
- C. streaming_df.filter("count < 30")
- D. streaming_df.show()
Correct Answer: C 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
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