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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Spark SQL | - Window functions and aggregations - SQL queries on DataFrames and tables |
| Topic 2: Data Processing and Performance | - Optimization techniques - Joins and data partitioning - Caching and persistence strategies |
| Topic 3: Data Ingestion and Storage | - Delta Lake basics - Reading and writing data (Parquet, JSON, CSV) |
| Topic 4: DataFrame API with PySpark | - DataFrame creation and schema management - Transformations and actions - Built-in functions and expressions |
| Topic 5: Apache Spark Fundamentals | - RDD vs DataFrame vs Dataset concepts - Spark architecture and execution model |
| Topic 6: Structured Streaming Basics | - Streaming DataFrames - Windowed aggregations in streaming |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
32 of 55.
A developer is creating a Spark application that performs multiple DataFrame transformations and actions. The developer wants to maintain optimal performance by properly managing the SparkSession.
How should the developer handle the SparkSession throughout the application?
- A. Create a new SparkSession instance before each transformation.
- B. Avoid using a SparkSession and rely on SparkContext only.
- C. Use a single SparkSession instance for the entire application.
- D. Stop and restart the SparkSession after each action.
Correct Answer: C 🗳️
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26 of 55.
A data scientist at an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user.
Before further processing, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns.
The PII columns in df_user are name, email, and birthdate.
Which code snippet can be used to meet this requirement?
- A. df_user_non_pii = df_user.dropFields("name", "email", "birthdate")
- B. df_user_non_pii = df_user.select("name", "email", "birthdate")
- C. df_user_non_pii = df_user.drop("name", "email", "birthdate")
- D. df_user_non_pii = df_user.remove("name", "email", "birthdate")
Correct Answer: C 🗳️
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35 of 55.
A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off.
How can this be achieved?
- A. By configuring the option recoveryLocation during writeStream.
- B. By configuring the option checkpointLocation during readStream.
- C. By configuring the option checkpointLocation during writeStream.
- D. By configuring the option recoveryLocation during SparkSession initialization.
Correct Answer: C 🗳️
Explanation: Only visible for GetValidTest members. You can sign-up / login (it's free).
25 of 55.
A Data Analyst is working on employees_df and needs to add a new column where a 10% tax is calculated on the salary.
Additionally, the DataFrame contains the column age, which is not needed.
Which code fragment adds the tax column and removes the age column?
- A. employees_df = employees_df.withColumn("tax", col("salary") + 0.1).drop("age")
- B. employees_df = employees_df.withColumn("tax", lit(0.1)).drop("age")
- C. employees_df = employees_df.withColumn("tax", col("salary") * 0.1).drop("age")
- D. employees_df = employees_df.dropField("age").withColumn("tax", col("salary") * 0.1)
Correct Answer: C 🗳️
Explanation: Only visible for GetValidTest 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.mode("overwrite").parquet("/data/output")
- B. df.write.mode("append").partitionBy("country").parquet("/data/output")
- C. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
- D. df.write.partitionBy("country").parquet("/data/output")
Correct Answer: C 🗳️
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