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Databricks-Certified-Professional-Data-Engineer試験の準備方法｜素敵なDatabricks-Certified-Professional-Data-Engineer資格問題対応試験｜更新するDatabricks Certified Professional Data Engineer Exam予想試験
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>> Databricks-Certified-Professional-Data-Engineer資格問題対応 <<
Databricks-Certified-Professional-Data-Engineer資格問題対応｜信頼に値するDatabricks-Certified-Professional-Data-Engineer予想試験いい評価Databricks Certified Professional Data Engineer Exam
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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q106-Q111):
質問 # 106
Which of the following developer operations in CI/CD flow can be implemented in Databricks Re-pos?
- A. Delete a branch
- B. Pull request and review process
- C. Resolve merge conflicts
- D. Trigger Databricks Repos API to pull the latest version of code into production folder
- E. Merge when code is committed
正解：D
解説：
Explanation
See the below diagram to understand the role Databricks Repos and Git provider plays when building a CI/CD workflow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure DevOps
質問 # 107
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?
- A. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
- B. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
- C. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
- D. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
- E. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
正解：D
解説：
Explanation
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees. Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Choose a region" section.
質問 # 108
Which of the following statements can be used to test the functionality of code to test number of rows in the table equal to 10 in python?
row_count = spark.sql("select count(*) from table").collect()[0][0]
- A. assert (row_count = 10, "Row count did not match")
- B. assert row_count = 10, "Row count did not match"
- C. assert if (row_count = 10, "Row count did not match")
- D. assert row_count == 10, "Row count did not match"
- E. assert if row_count == 10, "Row count did not match"
正解：D
解説：
Explanation
The answer is assert row_count == 10, "Row count did not match"
Review below documentation
質問 # 109
You noticed that colleague is manually copying the notebook with _bkp to store the previous ver-sions, which of the following feature would you recommend instead.
- A. Databricks notebooks can be exported into dbc archive files and stored in data lake
- B. Databricks notebook can be exported as HTML and imported at a later time
- C. Databricks notebooks should be copied to a local machine and setup source control locally to version the notebooks
- D. Databricks notebooks support change tracking and versioning
正解：D
解説：
Explanation
Answer is Databricks notebooks support automatic change tracking and versioning.
When you are editing the notebook on the right side check version history to view all the changes, every change you are making is captured and saved.
質問 # 110
The operations team is interested in monitoring the recently launched product, team wants to set up an email alert when the number of units sold increases by more than 10,000 units. They want to monitor this every 5 mins.
Fill in the below blanks to finish the steps we need to take
* Create ___ query that calculates total units sold
* Setup ____ with query on trigger condition Units Sold > 10,000
* Setup ____ to run every 5 mins
* Add destination ______
- A. SQL, Job, Refresh, email address
- B. Python, Job, SQL Cluster, email address
- C. SQL, Alert, Refresh, email address
- D. SQL, Job, SQL Cluster, email address
- E. Python, Job, Refresh, email address
正解：C
解説：
Explanation
The answer is SQL, Alert, Refresh, email address
Here the steps from Databricks documentation,
Create an alert
Follow these steps to create an alert on a single column of a query.
1.Do one of the following:
*Click Create in the sidebar and select Alert.
*Click Alerts in the sidebar and click the + New Alert button.
2.Search for a target query.
Graphical user interface, text, application Description automatically generated
To alert on multiple columns, you need to modify your query. See Alert on multiple col-umns.
3.In the Trigger when field, configure the alert.
*The Value column drop-down controls which field of your query result is evaluated.
*The Condition drop-down controls the logical operation to be applied.
*The Threshold text input is compared against the Value column using the Condition you specify.
Note
If a target query returns multiple records, Databricks SQL alerts act on the first one. As you change the Value column setting, the current value of that field in the top row is shown beneath it.
4.In the When triggered, send notification field, select how many notifications are sent when your alert is triggered:
*Just once: Send a notification when the alert status changes from OK to TRIGGERED.
*Each time alert is evaluated: Send a notification whenever the alert status is TRIGGERED regardless of its status at the previous evaluation.
*At most every: Send a notification whenever the alert status is TRIGGERED at a spe-cific interval. This choice lets you avoid notification spam for alerts that trigger of-ten.
Regardless of which notification setting you choose, you receive a notification whenever the status goes from OK to TRIGGERED or from TRIGGERED to OK. The schedule settings affect how many notifications you will receive if the status remains TRIGGERED from one execution to the next. For details, see Notification frequency.
5.In the Template drop-down, choose a template:
*Use default template: Alert notification is a message with links to the Alert configuration screen and the Query screen.
*Use custom template: Alert notification includes more specific information about the alert.
a.A box displays, consisting of input fields for subject and body. Any static content is valid, and you can incorporate built-in template variables:
*ALERT_STATUS: The evaluated alert status (string).
*ALERT_CONDITION: The alert condition operator (string).
*ALERT_THRESHOLD: The alert threshold (string or number).
*ALERT_NAME: The alert name (string).
*ALERT_URL: The alert page URL (string).
*QUERY_NAME: The associated query name (string).
*QUERY_URL: The associated query page URL (string).
*QUERY_RESULT_VALUE: The query result value (string or number).
*QUERY_RESULT_ROWS: The query result rows (value array).
*QUERY_RESULT_COLS: The query result columns (string array).
An example subject, for instance, could be: Alert "{{ALERT_NAME}}" changed status to
{{ALERT_STATUS}}.
b.Click the Preview toggle button to preview the rendered result.
Important
The preview is useful for verifying that template variables are rendered cor-rectly. It is not an accurate representation of the eventual notification content, as each alert destination can display notifications differently.
c.Click the Save Changes button.
6.In Refresh, set a refresh schedule. An alert's refresh schedule is independent of the query's refresh schedule.
*If the query is a Run as owner query, the query runs using the query owner's cre-dential on the alert's refresh schedule.
*If the query is a Run as viewer query, the query runs using the alert creator's cre-dential on the alert's refresh schedule.
7.Click Create Alert.
8.Choose an alert destination.
Important
If you skip this step you will not be notified when the alert is triggered.
質問 # 111
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