- 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 | Objectives |
|---|---|
| DataFrame API with PySpark | - Built-in functions and expressions - Transformations and actions - DataFrame creation and schema management |
| Structured Streaming Basics | - Streaming DataFrames - Windowed aggregations in streaming |
| Data Processing and Performance | - Caching and persistence strategies - Joins and data partitioning - Optimization techniques |
| Data Ingestion and Storage | - Delta Lake basics - Reading and writing data (Parquet, JSON, CSV) |
| Spark SQL | - SQL queries on DataFrames and tables - Window functions and aggregations |
| Apache Spark Fundamentals | - RDD vs DataFrame vs Dataset concepts - Spark architecture and execution model |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?
- A. REPLACE
- B. AGGREGATE
- C. APPEND
- D. COMPLETE
Correct Answer: D 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
A developer is trying to join two tables, sales.purchases_fct and sales.customer_dim, using the following code:
fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in the purchases_fct table that do not exist in the customer_dim table are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?
- A. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
- B. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
- C. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
- D. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
Correct Answer: B 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?
- A. Use an accumulator to record the maximum time on the driver
- B. Broadcast a variable to share the maximum time among workers
- C. Configure the Spark UI to automatically collect maximum times
- D. Use an RDD action like reduce() to compute the maximum time
Correct Answer: D 🗳️
Explanation: Only visible for ITPassLeader members. You can sign-up / login (it's free).
20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?
- A. cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.
- B. persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.
- C. Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
- D. Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).
Correct Answer: A 🗳️
Explanation: Only visible for ITPassLeader 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: C 🗳️
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