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

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

1. A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

A) 160
B) 64
C) 40
D) 80

2. A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?

A) Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
B) Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.
C) Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.
D) Use spark.read.json() with the inferSchema option set to true

3. A data scientist is working with a Spark DataFrame called customerDF that contains customer information. The DataFrame has a column named email with customer email addresses. The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A) customerDF.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
B) customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
)
C) customerDF.withColumn("username", substring_index(col("email"), "@", 1)) \
.withColumn("domain", substring_index(col("email"), "@", -1))
D) customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
)

4. A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

A) Cache large DataFrames to persist them in memory.
B) Increase the memory allocated to the Spark Driver.
C) Modify the Spark configuration to disable garbage collection
D) Optimize the data processing logic by repartitioning the DataFrame.

5. Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?

A) WARN, NONE, ERROR, FATAL
B) ALL, DEBUG, FAIL, INFO
C) FATAL, NONE, INFO, DEBUG
D) ERROR, WARN, TRACE, OFF

問題與答案：