Associate-Developer-Apache-Spark-3.5試験無料問題集（85題）「Databricks Certified Associate Developer for Apache Spark 3.5

出題：1

A developer initializes a SparkSession:

spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes thesparkSparkSession?

A. A SparkSession is unique for eachappName, and callinggetOrCreate()with the same name will return an existing SparkSession once it has been created.

B. ThegetOrCreate()method explicitly destroys any existing SparkSession and creates a new one.

C. A new SparkSession is created every time thegetOrCreate()method is invoked.

D. If a SparkSession already exists, this code will return the existing session instead of creating a new one.

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：2

A Spark application is experiencing performance issues in client mode because the driver is resource- constrained.
How should this issue be resolved?

A. Add more executor instances to the cluster

B. Increase the driver memory on the client machine

C. Switch the deployment mode to cluster mode

D. Switch the deployment mode to local mode

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：3

A Spark DataFramedfis cached using theMEMORY_AND_DISKstorage 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 will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.

B. Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.

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.

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：4

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 may fail because the driver does not have enough CPU cores to serialize the large DataFrame

C. The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors

D. The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：5

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?

A. [Row(name='alladin', age=20)]

B. The code throws an error due to a schema mismatch.

C. [Row(name='bambi'), Row(name='alladin', age=20)]

D. [Row(name='bambi', age=None), Row(name='alladin', age=20)]

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：6

A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A. Uses trigger() - default micro-batch trigger without interval.

B. Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.

C. Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.

D. Uses trigger(continuous='5 seconds') - continuous processing mode.

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：7

A developer is trying to join two tables,sales.purchases_fctandsales.customer_dim, using the following code:

fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in thepurchases_fcttable that do not exist in thecustomer_dimtable are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?

A. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')

B. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))

C. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')

D. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：8

A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?

A. Collecting persistent table statistics and storing them in the metastore for future use

B. Dynamically switching join strategies

C. Improving the performance of single-stage Spark jobs

D. Optimizing the layout of Delta files on disk

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

Associate-Developer-Apache-Spark-3.5試験無料問題集「Databricks Certified Associate Developer for Apache Spark 3.5 - Python 認定」