Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
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Databricks : Associate-Developer-Apache-Spark-3.5 Exam Premium Dumps
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Structured Streaming | 10% | - Defining streaming queries - Fault tolerance and state management - Output modes and triggers - Streaming concepts and architecture |
| Topic 2: Using Pandas API on Apache Spark | 5% | - Overview of Pandas API on Spark - Converting between Pandas and Spark structures - Key differences and limitations |
| Topic 3: 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 |
| Topic 4: Using Spark Connect to Deploy Applications | 5% | - Running applications via Spark Connect - Connecting to remote Spark clusters - Spark Connect architecture |
| Topic 5: Using Spark SQL | 20% | - Working with functions and expressions - Using catalog and metadata APIs - Integrating Spark SQL with DataFrames - Running SQL queries |
| Topic 6: Developing Apache Spark DataFrame API Applications | 30% | - Joining and combining datasets - Selecting, renaming, and modifying columns - Handling missing values and data quality - Reading and writing data in various formats - Creating DataFrames and defining schemas - Partitioning and bucketing data - User-defined functions (UDFs) - Filtering, sorting, and aggregating data |
| Topic 7: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Optimizing transformations and actions - Identifying performance bottlenecks - Debugging and logging - Managing memory and resource usage |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
A data engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?
- A. df.write.mode("append").partitionBy("country").parquet("/data/output")
- B. df.write.mode("overwrite").parquet("/data/output")
- C. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
- D. df.write.partitionBy("country").parquet("/data/output")
Correct Answer: C 🗳️
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2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?
- A. df.write.option("overwrite").json("path/to/file")
- B. df.write.mode("append").json("path/to/file")
- C. df.write.json("path/to/file")
- D. df.write.mode("overwrite").json("path/to/file")
Correct Answer: D 🗳️
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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?
- A. The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
- B. The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
- C. The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
- D. The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame
Correct Answer: A 🗳️
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A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?
- A. Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
- B. Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
- C. Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.
- D. Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
Correct Answer: B 🗳️
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16 of 55.
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior? (Choose 2 answers)
- A. Only actions trigger the execution of the transformation pipeline.
- B. The Spark engine optimizes the execution plan during the transformations, causing delays.
- C. Transformations are evaluated lazily.
- D. Transformations are executed immediately to build the lineage graph.
- E. The Spark engine requires manual intervention to start executing transformations.
Correct Answer: A,C 🗳️
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