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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q58-Q63):

質問 # 58
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. Use Databrick REST API to monitor and restart the job
B. Use Jobs runs, active runs UI section to monitor and restart the job
C. Add second task and add a check condition to rerun the first task if it fails
D. Restart the job cluster, job automatically restarts
E. Add a retry policy to the task

正解：E

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 # 59
You are working on a table called orders which contains data for 2021 and you have the second table called orders_archive which contains data for 2020, you need to combine the data from two tables and there could be a possibility of the same rows between both the tables, you are looking to combine the results from both the tables and eliminate the duplicate rows, which of the following SQL statements helps you accomplish this?

A. SELECT * FROM orders UNION ALL SELECT * FROM orders_archive
B. SELECT * FROM orders_archive MINUS SELECT * FROM orders
C. SELECT * FROM orders INTERSECT SELECT * FROM orders_archive
D. SELECT * FROM orders UNION SELECT * FROM orders_archive
(Correct)
E. SELECT distinct * FROM orders JOIN orders_archive on order.id = or-ders_archive.id

正解：D

解説：
Explanation
Answer is SELECT * FROM orders UNION SELECT * FROM orders_archive
UNION and UNION ALL are set operators,
UNION combines the output from both queries but also eliminates the duplicates.
UNION ALL combines the output from both queries.

質問 # 60
Which of the following Structured Streaming queries is performing a hop from a Bronze table to a Silver
table?

A. 1. (spark.read.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales")
6. )
B. 1. (spark.table("sales")
2. .withColumn("avgPrice", col("sales") / col("units"))
3. .writeStream
4. .option("checkpointLocation", checkpointPath)
5. .outputMode("append")
6. .table("cleanedSales")
7.)
C. 1. (spark.readStream.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales")
6. )
D. 1. (spark.table("sales")
2. .agg(sum("sales"),
3. sum("units"))
4. .writeStream
5. .option("checkpointLocation", checkpointPath)
6. .outputMode("complete")
7. .table("aggregatedSales")
8. )
E. 1. (spark.table("sales")
2. .groupBy("store")
3. .agg(sum("sales"))
4. .writeStream
5. .option("checkpointLocation", checkpointPath)
6. .outputMode("complete")
7. .table("aggregatedSales")
8.)

正解：B

質問 # 61
A data analyst has provided a data engineering team with the following Spark SQL query:
1.SELECT district,
2.avg(sales)
3.FROM store_sales_20220101
4.GROUP BY district;
The data analyst would like the data engineering team to run this query every day. The date at the end of the
table name (20220101) should automatically be replaced with the current date each time the query is run.
Which of the following approaches could be used by the data engineering team to efficiently auto-mate this
process?

A. They could wrap the query using PySpark and use Python's string variable system to automatically
update the table name
B. They could pass the table into PySpark and develop a robustly tested module on the existing query
C. They could request that the data analyst rewrites the query to be run less frequently
D. They could manually replace the date within the table name with the current day's date
E. They could replace the string-formatted date in the table with a timestamp-formatted date

正解：A

質問 # 62
You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion so your job can run immediatley?

A. Disable auto termination so the cluster is always running
B. Use the Databricks cluster pools feature to reduce the startup time
C. Pin the cluster in the cluster UI page so it is always available to the jobs
D. Setup an additional job to run ahead of the actual job so the cluster is running second job starts
E. Use Databricks Premium edition instead of Databricks standard edition

正解：B

解説：
Explanation
Cluster pools allow us to reserve VM's ahead of time, when a new job cluster is created VM are grabbed from the pool. Note: when the VM's are waiting to be used by the cluster only cost incurred is Azure. Databricks run time cost is only billed once VM is allocated to a cluster.
Here is a demo of how to setup a pool and follow some best practices,
Graphical user interface, text Description automatically generated

質問 # 63
......

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