Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
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Corresponding Certification: Databricks Certification
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Debugging and logging - Optimizing transformations and actions - Identifying performance bottlenecks - Managing memory and resource usage |
| Topic 2: Developing Apache Spark DataFrame API Applications | 30% | - Joining and combining datasets - Creating DataFrames and defining schemas - User-defined functions (UDFs) - Selecting, renaming, and modifying columns - Reading and writing data in various formats - Partitioning and bucketing data - Handling missing values and data quality - Filtering, sorting, and aggregating data |
| Topic 3: 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 |
| Topic 4: Structured Streaming | 10% | - Output modes and triggers - Streaming concepts and architecture - Defining streaming queries - Fault tolerance and state management |
| Topic 5: Using Pandas API on Apache Spark | 5% | - Overview of Pandas API on Spark - Converting between Pandas and Spark structures - Key differences and limitations |
| Topic 6: Using Spark SQL | 20% | - Integrating Spark SQL with DataFrames - Running SQL queries - Working with functions and expressions - Using catalog and metadata APIs |
| Topic 7: 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:
What is the behavior for function date_sub(start, days) if a negative value is passed into the days parameter?
- A. The number of days specified will be removed from the start date
- B. The number of days specified will be added to the start date
- C. The same start date will be returned
- D. An error message of an invalid parameter will be returned
Correct Answer: B 🗳️
Explanation: Only visible for TestKingIT 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(processingTime="5 seconds") \
.start() - B. query = df.writeStream \
.outputMode("append") \
.trigger(once=True) \
.start() - C. query = df.writeStream \
.outputMode("append") \
.start() - D. query = df.writeStream \
.outputMode("append") \
.trigger(continuous="5 seconds") \
.start()
Correct Answer: A 🗳️
Explanation: Only visible for TestKingIT members. You can sign-up / login (it's free).
19 of 55.
A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function not available in the standard Spark functions library.
The existing UDF code is:
import hashlib
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = udf(shake_256, StringType())
The developer replaces this UDF with a Pandas UDF for better performance:
@pandas_udf(StringType())
def shake_256(raw: str) -> str:
return hashlib.shake_256(raw.encode()).hexdigest(20)
However, the developer receives this error:
TypeError: Unsupported signature: (raw: str) -> str
What should the signature of the shake_256() function be changed to in order to fix this error?
- A. def shake_256(raw: [str]) -> [str]:
- B. def shake_256(raw: str) -> str:
- C. def shake_256(raw: pd.Series) -> pd.Series:
- D. def shake_256(raw: [pd.Series]) -> pd.Series:
Correct Answer: C 🗳️
Explanation: Only visible for TestKingIT members. You can sign-up / login (it's free).
A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?
- A. The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.
- B. The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
- C. The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
- D. Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.
Correct Answer: B 🗳️
Explanation: Only visible for TestKingIT members. You can sign-up / login (it's free).
9 of 55.
Given the code fragment:
import pyspark.pandas as ps
pdf = ps.DataFrame(data)
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?
- A. pdf.to_spark()
- B. pdf.spark()
- C. pdf.to_pandas()
- D. pdf.to_dataframe()
Correct Answer: A 🗳️
Explanation: Only visible for TestKingIT members. You can sign-up / login (it's free).
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