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| Section | Weight | Objectives |
|---|---|---|
| Developing Apache Spark DataFrame API Applications | 30% | - Selecting, renaming, and modifying columns - Handling missing values and data quality - Creating DataFrames and defining schemas - Partitioning and bucketing data - User-defined functions (UDFs) - Reading and writing data in various formats - Filtering, sorting, and aggregating data - Joining and combining datasets |
| Using Spark SQL | 20% | - Integrating Spark SQL with DataFrames - Running SQL queries - Using catalog and metadata APIs - Working with functions and expressions |
| Apache Spark Architecture and Components | 20% | - Fault tolerance and garbage collection - Execution and deployment modes - Spark architecture overview - Shuffling, actions, and broadcasting - Execution hierarchy and lazy evaluation |
| Using Spark Connect to Deploy Applications | 5% | - Running applications via Spark Connect - Connecting to remote Spark clusters - Spark Connect architecture |
| Using Pandas API on Apache Spark | 5% | - Overview of Pandas API on Spark - Key differences and limitations - Converting between Pandas and Spark structures |
| Structured Streaming | 10% | - Output modes and triggers - Defining streaming queries - Fault tolerance and state management - Streaming concepts and architecture |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Identifying performance bottlenecks - Optimizing transformations and actions - Debugging and logging - Managing memory and resource usage |
3 of 55. A data engineer observes that the upstream streaming source feeds the event table frequently and sends duplicate records. Upon analyzing the current production table, the data engineer found that the time difference in the event_timestamp column of the duplicate records is, at most, 30 minutes.
To remove the duplicates, the engineer adds the code:
df = df.withWatermark("event_timestamp", "30 minutes")
What is the result?
Correct Answer: C 🗳️
Explanation: Only visible for TestkingPass members. You can sign-up / login (it's free).
A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:
Correct Answer: A 🗳️
Explanation: Only visible for TestkingPass members. You can sign-up / login (it's free).
Given a CSV file with the content:
And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?
Correct Answer: B 🗳️
Explanation: Only visible for TestkingPass members. You can sign-up / login (it's free).
A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?
Correct Answer: D 🗳️
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34 of 55.
A data engineer is investigating a Spark cluster that is experiencing underutilization during scheduled batch jobs.
After checking the Spark logs, they noticed that tasks are often getting killed due to timeout errors, and there are several warnings about insufficient resources in the logs.
Which action should the engineer take to resolve the underutilization issue?
Correct Answer: B 🗳️
Explanation: Only visible for TestkingPass members. You can sign-up / login (it's free).
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