- Exam Code: Associate-Developer-Apache-Spark-3.5
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
- Certification Provider: Databricks
- Corresponding Certification:Databricks Certification
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Topic 2: Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Topic 3: Structured Streaming | 10% | - Streaming Applications
|
| Topic 4: Using Pandas API on Spark | 5% | - Pandas API
|
| Topic 5: Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Topic 6: Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Topic 7: Using Spark SQL | 20% | - Spark SQL Operations
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
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_dataframe()
- B. pdf.spark()
- C. pdf.to_spark()
- D. pdf.to_pandas()
Correct Answer: C 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columns sensor_id, temperature, and timestamp. The engineer needs to calculate the average temperature for each sensor over the last 5 minutes while the data is streaming.
Which code implementation achieves the requirement?
Options from the images provided:
- A.
- B.
- C.
- D.
Correct Answer: D 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
27 of 55.
A data engineer needs to add all the rows from one table to all the rows from another, but not all the columns in the first table exist in the second table.
The error message is:
AnalysisException: UNION can only be performed on tables with the same number of columns.
The existing code is:
au_df.union(nz_df)
The DataFrame au_df has one extra column that does not exist in the DataFrame nz_df, but otherwise both DataFrames have the same column names and data types.
What should the data engineer fix in the code to ensure the combined DataFrame can be produced as expected?
- A. df = au_df.unionAll(nz_df)
- B. df = au_df.unionByName(nz_df, allowMissingColumns=True)
- C. df = au_df.union(nz_df, allowMissingColumns=True)
- D. df = au_df.unionByName(nz_df, allowMissingColumns=False)
Correct Answer: B 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
Given the code fragment:
import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?
- A. psdf.to_spark()
- B. psdf.to_pyspark()
- C. psdf.to_pandas()
- D. psdf.to_dataframe()
Correct Answer: A 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
Which Spark configuration controls the number of tasks that can run in parallel on the executor?
Options:
- A. spark.executor.cores
- B. spark.executor.memory
- C. spark.driver.cores
- D. spark.task.maxFailures
Correct Answer: A 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
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