Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Structured Streaming | 10% | - Streaming concepts and architecture - Fault tolerance and state management - Output modes and triggers - Defining streaming queries |
| Using Pandas API on Apache Spark | 5% | - Key differences and limitations - Overview of Pandas API on Spark - Converting between Pandas and Spark structures |
| Using Spark SQL | 20% | - Using catalog and metadata APIs - Integrating Spark SQL with DataFrames - Working with functions and expressions - Running SQL queries |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Debugging and logging - Managing memory and resource usage - Optimizing transformations and actions - Identifying performance bottlenecks |
| Apache Spark Architecture and Components | 20% | - Shuffling, actions, and broadcasting - Execution hierarchy and lazy evaluation - Fault tolerance and garbage collection - Execution and deployment modes - Spark architecture overview |
| Developing Apache Spark DataFrame API Applications | 30% | - Partitioning and bucketing data - Selecting, renaming, and modifying columns - Creating DataFrames and defining schemas - Reading and writing data in various formats - Joining and combining datasets - Handling missing values and data quality - Filtering, sorting, and aggregating data - User-defined functions (UDFs) |
| Using Spark Connect to Deploy Applications | 5% | - Running applications via Spark Connect - Spark Connect architecture - Connecting to remote Spark clusters |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?
- A. Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
- B. Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).
- C. cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.
- D. persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.
Correct Answer: C 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0. The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?
- A.
result_df = prices_df \
.agg(F.min("spot_price"), F.max("spot_price")) - B.
result_df = prices_df \
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price))) - C.
result_df = prices_df \
.agg(F.count("spot_price").alias("spot_price")) \
.filter(F.col("spot_price") > F.lit("min_price")) - D.
result_df = prices_df \
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0))
Correct Answer: B 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?
- A. The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
- B. The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.
- C. The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
- D. Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.
Correct Answer: A 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?
- A. The operation will fail if the Pandas DataFrame exceeds 1000 rows
- B. The operation will load all data into the driver's memory, potentially causing memory overflow
- C. Data will be lost during conversion
- D. The conversion will automatically distribute the data across worker nodes
Correct Answer: B 🗳️
Explanation: Only visible for TestKingFree 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?
- A. Support for SQL queries on structured data
- B. Ability to process small datasets efficiently
- C. Built-in machine learning libraries
- D. In-memory computation and parallel processing capabilities
Correct Answer: D 🗳️
Explanation: Only visible for TestKingFree members. You can sign-up / login (it's free).
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