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 | Objectives |
|---|---|
| Topic 1: Data Ingestion and Storage | - Delta Lake basics - Reading and writing data (Parquet, JSON, CSV) |
| Topic 2: Spark SQL | - SQL queries on DataFrames and tables - Window functions and aggregations |
| Topic 3: DataFrame API with PySpark | - DataFrame creation and schema management - Built-in functions and expressions - Transformations and actions |
| Topic 4: Data Processing and Performance | - Caching and persistence strategies - Optimization techniques - Joins and data partitioning |
| 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:
54 of 55.
What is the benefit of Adaptive Query Execution (AQE)?
- A. It allows Spark to optimize the query plan before execution but does not adapt during runtime.
- B. It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
- C. It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
- D. It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
Correct Answer: D 🗳️
Explanation: Only visible for PremiumVCEDump members. You can sign-up / login (it's free).
24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?
- A. spark.read.parquet("events.parquet").printSchema()
- B. spark.read.format("parquet").load("events.parquet").show()
- C. spark.sql("SELECT schema FROM events.parquet").show()
- D. spark.sql("SELECT * FROM events.parquet").show()
Correct Answer: A 🗳️
Explanation: Only visible for PremiumVCEDump members. You can sign-up / login (it's free).
A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?
- A. DataFrame.withColumn()
- B. DataFrame.filter()
- C. DataFrame.groupBy().agg()
- D. DataFrame.select()
Correct Answer: C 🗳️
Explanation: Only visible for PremiumVCEDump members. You can sign-up / login (it's free).
A data scientist of 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, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.
Which code snippet can be used to meet this requirement?
- A. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
- B. df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")
- C. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
- D. df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")
Correct Answer: A 🗳️
Explanation: Only visible for PremiumVCEDump members. You can sign-up / login (it's free).
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.remove("name", "email", "birthdate")
- B. df_user_non_pii = df_user.select("name", "email", "birthdate")
- C. df_user_non_pii = df_user.dropFields("name", "email", "birthdate")
- D. df_user_non_pii = df_user.drop("name", "email", "birthdate")
Correct Answer: D 🗳️
Explanation: Only visible for PremiumVCEDump members. You can sign-up / login (it's free).
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