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| Section | Weight | Objectives |
|---|---|---|
| Using Spark SQL | 20% | - Integrating Spark SQL with DataFrames - Running SQL queries - Using catalog and metadata APIs - Working with functions and expressions |
| Developing Apache Spark DataFrame API Applications | 30% | - Joining and combining datasets - Reading and writing data in various formats - Selecting, renaming, and modifying columns - Filtering, sorting, and aggregating data - Partitioning and bucketing data - User-defined functions (UDFs) - Creating DataFrames and defining schemas - Handling missing values and data quality |
| Using Spark Connect to Deploy Applications | 5% | - Running applications via Spark Connect - Spark Connect architecture - Connecting to remote Spark clusters |
| Apache Spark Architecture and Components | 20% | - Execution hierarchy and lazy evaluation - Spark architecture overview - Fault tolerance and garbage collection - Execution and deployment modes - Shuffling, actions, and broadcasting |
| Using Pandas API on Apache Spark | 5% | - Key differences and limitations - Converting between Pandas and Spark structures - Overview of Pandas API on Spark |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Managing memory and resource usage - Optimizing transformations and actions - Identifying performance bottlenecks - Debugging and logging |
| Structured Streaming | 10% | - Defining streaming queries - Fault tolerance and state management - Output modes and triggers - Streaming concepts and architecture |
14 of 55.
A developer created a DataFrame with columns color, fruit, and taste, and wrote the data to a Parquet directory using:
df.write.partitionBy("color", "taste").parquet("/path/to/output")
What is the result of this code?
Correct Answer: C 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
26 of 55.
A data scientist at an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user.
Before further processing, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns.
The PII columns in df_user are name, email, and birthdate.
Which code snippet can be used to meet this requirement?
Correct Answer: D 🗳️
Explanation: Only visible for PracticeDump 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: C 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?
Correct Answer: C 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?
Correct Answer: D 🗳️
Explanation: Only visible for PracticeDump members. You can sign-up / login (it's free).
Viewing page 1 out of 27 pages.
Viewing questions 1-5 out of 135 questions.
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