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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Spark Connect to Deploy Applications | 5% | - Running applications via Spark Connect - Connecting to remote Spark clusters - Spark Connect architecture |
| Topic 2: Using Pandas API on Apache Spark | 5% | - Key differences and limitations - Converting between Pandas and Spark structures - Overview of Pandas API on Spark |
| Topic 3: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Optimizing transformations and actions - Identifying performance bottlenecks - Managing memory and resource usage - Debugging and logging |
| Topic 4: Developing Apache Spark DataFrame API Applications | 30% | - User-defined functions (UDFs) - Handling missing values and data quality - Filtering, sorting, and aggregating data - Reading and writing data in various formats - Partitioning and bucketing data - Selecting, renaming, and modifying columns - Joining and combining datasets - Creating DataFrames and defining schemas |
| Topic 5: Using Spark SQL | 20% | - Working with functions and expressions - Integrating Spark SQL with DataFrames - Using catalog and metadata APIs - Running SQL queries |
| Topic 6: Structured Streaming | 10% | - Fault tolerance and state management - Output modes and triggers - Defining streaming queries - Streaming concepts and architecture |
| Topic 7: Apache Spark Architecture and Components | 20% | - Execution hierarchy and lazy evaluation - Shuffling, actions, and broadcasting - Execution and deployment modes - Spark architecture overview - Fault tolerance and garbage collection |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
12 of 55.
A data scientist has been investigating user profile data to build features for their model. After some exploratory data analysis, the data scientist identified that some records in the user profiles contain NULL values in too many fields to be useful.
The schema of the user profile table looks like this:
user_id STRING,
username STRING,
date_of_birth DATE,
country STRING,
created_at TIMESTAMP
The data scientist decided that if any record contains a NULL value in any field, they want to remove that record from the output before further processing.
Which block of Spark code can be used to achieve these requirements?
- A. filtered_users = raw_users.dropna(how="all")
- B. filtered_users = raw_users.na.drop("any")
- C. filtered_users = raw_users.na.drop("all")
- D. filtered_users = raw_users.dropna(how="any")
Correct Answer: D 🗳️
Explanation: Only visible for DumpsTorrent 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_pandas()
- B. psdf.to_spark()
- C. psdf.to_dataframe()
- D. psdf.to_pyspark()
Correct Answer: B 🗳️
Explanation: Only visible for DumpsTorrent members. You can sign-up / login (it's free).
22 of 55.
A Spark application needs to read multiple Parquet files from a directory where the files have differing but compatible schemas.
The data engineer wants to create a DataFrame that includes all columns from all files.
Which code should the data engineer use to read the Parquet files and include all columns using Apache Spark?
- A. spark.read.option("mergeSchema", True).parquet("/data/parquet/")
- B. spark.read.parquet("/data/parquet/")
- C. spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")
- D. spark.read.parquet("/data/parquet/").option("mergeAllCols", True)
Correct Answer: A 🗳️
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A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?
- A. df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
- B. df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
- C. df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
- D. df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
Correct Answer: D 🗳️
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Given a DataFrame df that has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?
- A. 10
- B. 20
- C. Same number as the cluster executors
- D. 1
Correct Answer: A 🗳️
Explanation: Only visible for DumpsTorrent members. You can sign-up / login (it's free).
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