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Databricks-Certified-Professional-Data-Engineer日本語的中対策 & Databricks-Certified-Professional-Data-Engineer試験時間

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Databricks Databricks-Certified-Professional-Data-Engineer試験時間 & Databricks-Certified-Professional-Data-Engineer日本語解説集

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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q95-Q100):

質問 # 95
A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

A. The five Minute Load Average remains consistent/flat
B. Bytes Received never exceeds 80 million bytes per second
C. Total Disk Space remains constant
D. Network I/O never spikes
E. Overall cluster CPU utilization is around 25%

正解：E

解説：
Explanation
This is the correct answer because it indicates a bottleneck caused by code executing on the driver. A bottleneck is a situation where the performance or capacity of a system is limited by a single component or resource. A bottleneck can cause slow execution, high latency, or low throughput. A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor. When evaluating the Ganglia Metrics for this cluster, one can look for indicators that show how the cluster resources are being utilized, such as CPU, memory, disk, or network. If the overall cluster CPU utilization is around 25%, it means that only one out of the four nodes (driver + 3 executors) is using its full CPU capacity, while the other three nodes are idle or underutilized. This suggests that the code executing on the driver is taking too long or consuming too much CPU resources, preventing the executors from receiving tasks or data to process. This can happen when the code has driver-side operations that are not parallelized or distributed, such as collecting large amounts of data to the driver, performing complex calculations on the driver, or using non-Spark libraries on the driver. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "View cluster status and event logs - Ganglia metrics" section; Databricks Documentation, under "Avoid collecting large RDDs" section.

質問 # 96
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

A. return spark.readStream.table("bronze")
B. return spark.readStream.load("bronze")
C. return spark.read.option("readChangeFeed", "true").table ("bronze")
D.

正解：D

解説：
https://docs.databricks.com/en/delta/delta-change-data-feed.html

質問 # 97
A data engineering team is in the process of converting their existing data pipeline to utilize Auto Loader for
incremental processing in the ingestion of JSON files. One data engineer comes across the following code
block in the Auto Loader documentation:
1. (streaming_df = spark.readStream.format("cloudFiles")
2. .option("cloudFiles.format", "json")
3. .option("cloudFiles.schemaLocation", schemaLocation)
4. .load(sourcePath))
Assuming that schemaLocation and sourcePath have been set correctly, which of the following changes does
the data engineer need to make to convert this code block to use Auto Loader to ingest the data?

A. The data engineer needs to add the .autoLoader line before the .load(sourcePath) line
B. There is no change required. Databricks automatically uses Auto Loader for streaming reads
C. There is no change required. The data engineer needs to ask their administrator to turn on Auto Loader
D. There is no change required. The inclusion of format("cloudFiles") enables the use of Auto Loader
E. The data engineer needs to change the format("cloudFiles") line to format("autoLoader")

正解：D

質問 # 98
A nightly job ingests data into a Delta Lake table using the following code:

A. return spark.readStream.table("bronze")
B.
C. return spark.readStream.load("bronze")
D. return spark.read.option("readChangeFeed", "true").table ("bronze")
E.

正解：E

解説：
https://docs.databricks.com/en/delta/delta-change-data-feed.html

質問 # 99
The data engineering team noticed that one of the job fails randomly as a result of using spot in-stances, what feature in Jobs/Tasks can be used to address this issue so the job is more stable when using spot instances?

A. Add a retry policy to the task
B. Use Jobs runs, active runs UI section to monitor and restart the job
C. Use Databrick REST API to monitor and restart the job
D. Add second task and add a check condition to rerun the first task if it fails
E. Restart the job cluster, job automatically restarts

正解：A

解説：
Explanation
The answer is, Add a retry policy to the task
Tasks in Jobs support Retry Policy, which can be used to retry a failed tasks, especially when using spot instance it is common to have failed executors or driver.

質問 # 100
......

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