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| Section | Weight | Objectives |
|---|---|---|
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Optimizing transformations and actions - Debugging and logging - Identifying performance bottlenecks - Managing memory and resource usage |
| Apache Spark Architecture and Components | 20% | - Shuffling, actions, and broadcasting - Fault tolerance and garbage collection - Execution hierarchy and lazy evaluation - Execution and deployment modes - Spark architecture overview |
| Structured Streaming | 10% | - Fault tolerance and state management - Defining streaming queries - Output modes and triggers - Streaming concepts and architecture |
| Using Pandas API on Apache Spark | 5% | - Converting between Pandas and Spark structures - Key differences and limitations - Overview of Pandas API on Spark |
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture - Running applications via Spark Connect - Connecting to remote Spark clusters |
| Developing Apache Spark DataFrame API Applications | 30% | - Handling missing values and data quality - Reading and writing data in various formats - Selecting, renaming, and modifying columns - Filtering, sorting, and aggregating data - Joining and combining datasets - User-defined functions (UDFs) - Creating DataFrames and defining schemas - Partitioning and bucketing data |
| Using Spark SQL | 20% | - Running SQL queries - Working with functions and expressions - Using catalog and metadata APIs - Integrating Spark SQL with DataFrames |
A DataFrame df has columns name, age, and salary. The developer needs to sort the DataFrame by age in ascending order and salary in descending order.
Which code snippet meets the requirement of the developer?
Correct Answer: A 🗳️
Explanation: Only visible for BraindumpsVCE members. You can sign-up / login (it's free).
15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?
Correct Answer: D 🗳️
Explanation: Only visible for BraindumpsVCE members. You can sign-up / login (it's free).
A data analyst wants to add a column date derived from a timestamp column.
Options:
Correct Answer: B 🗳️
Explanation: Only visible for BraindumpsVCE members. You can sign-up / login (it's free).
27 of 55.
A data engineer needs to add all the rows from one table to all the rows from another, but not all the columns in the first table exist in the second table.
The error message is:
AnalysisException: UNION can only be performed on tables with the same number of columns.
The existing code is:
au_df.union(nz_df)
The DataFrame au_df has one extra column that does not exist in the DataFrame nz_df, but otherwise both DataFrames have the same column names and data types.
What should the data engineer fix in the code to ensure the combined DataFrame can be produced as expected?
Correct Answer: A 🗳️
Explanation: Only visible for BraindumpsVCE members. You can sign-up / login (it's free).
A data engineer is working on a Streaming DataFrame streaming_df with the given streaming data:
Which operation is supported with streamingdf ?
Correct Answer: C 🗳️
Explanation: Only visible for BraindumpsVCE members. You can sign-up / login (it's free).
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