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| Section | Objectives |
|---|---|
| DataFrame API with PySpark | - Transformations and actions - Built-in functions and expressions - DataFrame creation and schema management |
| Data Ingestion and Storage | - Delta Lake basics - Reading and writing data (Parquet, JSON, CSV) |
| Data Processing and Performance | - Joins and data partitioning - Caching and persistence strategies - Optimization techniques |
| Apache Spark Fundamentals | - Spark architecture and execution model - RDD vs DataFrame vs Dataset concepts |
| Spark SQL | - SQL queries on DataFrames and tables - Window functions and aggregations |
| Structured Streaming Basics | - Streaming DataFrames - Windowed aggregations in streaming |
1. The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?
A) @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159
B) @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159
C) @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159
D) @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159
2. 11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?
A) spark.executor.cores
B) spark.task.maxFailures
C) spark.sql.shuffle.partitions
D) spark.executor.memory
3. A developer runs:
What is the result?
Options:
A) It throws an error if there are null values in either partition column.
B) It stores all data in a single Parquet file.
C) It creates separate directories for each unique combination of color and fruit.
D) It appends new partitions to an existing Parquet file.
4. 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:
A) users.write.saveAsTable("default_table", path="/some/path")
B) users.write.option("path", "/some/path").saveAsTable("default_table")
C) users.write(path="/some/path").saveAsTable("default_table")
D) users.write.saveAsTable("default_table").option("path", "/some/path")
5. A data engineer wants to create an external table from a JSON file located at /data/input.json with the following requirements:
Create an external table named users
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:
A) CREATE TABLE users USING json OPTIONS (path '/data/input.json')
B) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema 'true')
C) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge 'true')
D) CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: B |
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