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| Section | Objectives |
|---|---|
| Data Processing and Performance | - Caching and persistence strategies - Optimization techniques - Joins and data partitioning |
| Data Ingestion and Storage | - Delta Lake basics - Reading and writing data (Parquet, JSON, CSV) |
| Apache Spark Fundamentals | - RDD vs DataFrame vs Dataset concepts - Spark architecture and execution model |
| 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 |
| Spark SQL | - SQL queries on DataFrames and tables - Window functions and aggregations |
A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data. The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by the market_time field.
Which line of Spark code will produce a Parquet table that meets these requirements?
Correct Answer: A 🗳️
Explanation: Only visible for PrepAwayTest members. You can sign-up / login (it's free).
8 of 55.
A data scientist at a large e-commerce company needs to process and analyze 2 TB of daily customer transaction data. The company wants to implement real-time fraud detection and personalized product recommendations.
Currently, the company uses a traditional relational database system, which struggles with the increasing data volume and velocity.
Which feature of Apache Spark effectively addresses this challenge?
Correct Answer: A 🗳️
Explanation: Only visible for PrepAwayTest 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?
Correct Answer: B 🗳️
Explanation: Only visible for PrepAwayTest 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: D 🗳️
Explanation: Only visible for PrepAwayTest members. You can sign-up / login (it's free).
47 of 55.
A data engineer has written the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv")
df2 = spark.read.csv("product_data.csv")
df_joined = df1.join(df2, df1.product_id == df2.product_id)
The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.
Which join strategy will Spark use?
Correct Answer: B 🗳️
Explanation: Only visible for PrepAwayTest members. You can sign-up / login (it's free).
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