Databricks Certified Associate Developer for Apache Spark 3.5 - Python 認定 Associate-Developer-Apache-Spark-3.5 試験問題:
1. 3 of 55. A data engineer observes that the upstream streaming source feeds the event table frequently and sends duplicate records. Upon analyzing the current production table, the data engineer found that the time difference in the event_timestamp column of the duplicate records is, at most, 30 minutes.
To remove the duplicates, the engineer adds the code:
df = df.withWatermark("event_timestamp", "30 minutes")
What is the result?
A) It accepts watermarks in seconds and the code results in an error.
B) It is not able to handle deduplication in this scenario.
C) It removes duplicates that arrive within the 30-minute window specified by the watermark.
D) It removes all duplicates regardless of when they arrive.
2. 6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?
A) Driver Nodes
B) Executors
C) CPU Cores
D) Worker Nodes
3. A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?
A) Set the number of partitions equal to the total number of CPUs in the cluster
B) Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB
C) Set the number of partitions to a fixed value, such as 200
D) Set the number of partitions equal to the number of nodes in the cluster
4. 28 of 55.
A data analyst builds a Spark application to analyze finance data and performs the following operations:
filter, select, groupBy, and coalesce.
Which operation results in a shuffle?
A) select
B) coalesce
C) filter
D) groupBy
5. How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:
A) Configure the application to run in cluster mode instead of local mode.
B) Increase the number of local threads based on the number of CPU cores.
C) Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
D) Set the spark.executor.memory property to a large value.
質問と回答：
| 質問 # 1 正解： C | 質問 # 2 正解： B | 質問 # 3 正解： B | 質問 # 4 正解： D | 質問 # 5 正解： B |
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