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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Structured Streaming | 10% | - Streaming Applications
|
| Topic 2: Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Topic 3: Using Pandas API on Spark | 5% | - Pandas API
|
| Topic 4: Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Topic 5: Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Topic 6: Using Spark SQL | 20% | - Spark SQL Operations
|
| Topic 7: Troubleshooting and Tuning | 10% | - Performance Optimization
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
7 of 55.
A developer has been asked to debug an issue with a Spark application. The developer identified that the data being loaded from a CSV file is being read incorrectly into a DataFrame.
The CSV file has been read using the following Spark SQL statement:
CREATE TABLE locations
USING csv
OPTIONS (path '/data/locations.csv')
The first lines of the command SELECT * FROM locations look like this:
| city | lat | long |
| ALTI Sydney | -33... | ... |
Which parameter can the developer add to the OPTIONS clause in the CREATE TABLE statement to read the CSV data correctly again?
- A. 'sep' '|'
- B. 'sep' ','
- C. 'header' 'false'
- D. 'header' 'true'
Correct Answer: D 🗳️
Explanation: Only visible for BraindumpsIT 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: [pd.Series]) -> pd.Series:
- C. def shake_256(raw: str) -> str:
- D. def shake_256(raw: pd.Series) -> pd.Series:
Correct Answer: D 🗳️
Explanation: Only visible for BraindumpsIT members. You can sign-up / login (it's free).
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. An error message of an invalid parameter will be returned
- C. The same start date will be returned
- D. The number of days specified will be added to the start date
Correct Answer: D 🗳️
Explanation: Only visible for BraindumpsIT members. You can sign-up / login (it's free).
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")
Correct Answer: D 🗳️
Explanation: Only visible for BraindumpsIT members. You can sign-up / login (it's free).
Given this code:
.withWatermark("event_time", "10 minutes")
.groupBy(window("event_time", "15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:
- A. The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.
- B. Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
- C. Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
- D. Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
Correct Answer: D 🗳️
Explanation: Only visible for BraindumpsIT members. You can sign-up / login (it's free).
