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| Section | Weight | Objectives |
|---|---|---|
| Using Pandas API on Apache Spark | 5% | - Converting between Pandas and Spark structures - Overview of Pandas API on Spark - Key differences and limitations |
| Using Spark SQL | 20% | - Running SQL queries - Working with functions and expressions - Integrating Spark SQL with DataFrames - Using catalog and metadata APIs |
| Developing Apache Spark DataFrame API Applications | 30% | - Reading and writing data in various formats - Filtering, sorting, and aggregating data - Creating DataFrames and defining schemas - Joining and combining datasets - Handling missing values and data quality - Selecting, renaming, and modifying columns - User-defined functions (UDFs) - Partitioning and bucketing data |
| Using Spark Connect to Deploy Applications | 5% | - Running applications via Spark Connect - Spark Connect architecture - Connecting to remote Spark clusters |
| Structured Streaming | 10% | - Streaming concepts and architecture - Output modes and triggers - Defining streaming queries - Fault tolerance and state management |
| Apache Spark Architecture and Components | 20% | - Spark architecture overview - Execution hierarchy and lazy evaluation - Fault tolerance and garbage collection - Execution and deployment modes - Shuffling, actions, and broadcasting |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Managing memory and resource usage - Debugging and logging - Identifying performance bottlenecks - Optimizing transformations and actions |
13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?
Correct Answer: B 🗳️
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A data analyst builds a Spark application to analyze finance data and performs the following operations: filter, select, groupBy, and coalesce.
Which operation results in a shuffle?
Correct Answer: D 🗳️
Explanation: Only visible for Free4Torrent members. You can sign-up / login (it's free).
A data scientist at a financial services company is working with a Spark DataFrame containing transaction records. The DataFrame has millions of rows and includes columns for transaction_id, account_number, transaction_amount, and timestamp. Due to an issue with the source system, some transactions were accidentally recorded multiple times with identical information across all fields. The data scientist needs to remove rows with duplicates across all fields to ensure accurate financial reporting.
Which approach should the data scientist use to deduplicate the orders using PySpark?
Correct Answer: D 🗳️
Explanation: Only visible for Free4Torrent members. You can sign-up / login (it's free).
A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?
Correct Answer: A 🗳️
Explanation: Only visible for Free4Torrent members. You can sign-up / login (it's free).
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?
Correct Answer: C 🗳️
Explanation: Only visible for Free4Torrent members. You can sign-up / login (it's free).
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