Databricks Certified Associate Developer for Apache Spark 3.5 - Python : Associate-Developer-Apache-Spark-3.5

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Databricks Associate-Developer-Apache-Spark-3.5 考試大綱主題:

最新的 Databricks Certification Associate-Developer-Apache-Spark-3.5 免費考試真題：

1. A data scientist is working on a project that requires processing large amounts of structured data, performing SQL queries, and applying machine learning algorithms. The data scientist is considering using Apache Spark for this task.
Which combination of Apache Spark modules should the data scientist use in this scenario?
Options:

A) Spark DataFrames, Spark SQL, and MLlib
B) Spark Streaming, GraphX, and Pandas API on Spark
C) Spark DataFrames, Structured Streaming, and GraphX
D) Spark SQL, Pandas API on Spark, and Structured Streaming

2. Given the schema:

event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:

A) groupBy without aggregation (invalid use)
B) dropDuplicates on the exact matching fields
C) dropDuplicates with no arguments (removes based on all columns)
D) dropDuplicates on all columns (wrong criteria)

3. 23 of 55.
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)

A) It requires specialized hardware to run, making it unsuitable for commodity hardware clusters.
B) It can distribute data processing tasks across a cluster of machines, enabling horizontal scalability.
C) It has built-in fault tolerance, allowing it to recover seamlessly from node failures during computation.
D) It eliminates the need to write any code, automatically handling all data processing.
E) It processes data solely on disk storage, reducing the need for memory resources.

4. 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 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.
C) 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.
D) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.

5. An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e., col1, col2, during the reading process?

A) spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")
B) spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")
C) spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")
D) spark.read.orc("/file/test_data.orc").selected("col1", "col2")

問題與答案：