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| Section | Objectives |
|---|---|
| Topic 1: Structured Streaming Basics | - Streaming DataFrames - Windowed aggregations in streaming |
| Topic 2: Data Processing and Performance | - Optimization techniques - Joins and data partitioning - Caching and persistence strategies |
| Topic 3: Data Ingestion and Storage | - Delta Lake basics - Reading and writing data (Parquet, JSON, CSV) |
| Topic 4: Spark SQL | - Window functions and aggregations - SQL queries on DataFrames and tables |
| Topic 5: DataFrame API with PySpark | - Built-in functions and expressions - DataFrame creation and schema management - Transformations and actions |
| Topic 6: Apache Spark Fundamentals | - RDD vs DataFrame vs Dataset concepts - Spark architecture and execution model |
Given a DataFrame df that has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?
Correct Answer: B 🗳️
Explanation: Only visible for Exams4Collection members. You can sign-up / login (it's free).
A data engineer replaces the exact percentile() function with approx_percentile() to improve performance, but the results are drifting too far from expected values.
Which change should be made to solve the issue?
Correct Answer: C 🗳️
Explanation: Only visible for Exams4Collection members. You can sign-up / login (it's free).
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 Exams4Collection members. You can sign-up / login (it's free).
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?
Correct Answer: B 🗳️
Explanation: Only visible for Exams4Collection 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 Exams4Collection members. You can sign-up / login (it's free).
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