Exam Associate Cloud Engineer topic 1 question 193 discussion

B. Use the BigQuery interface to review the nightly job and look for any errors. Since the problem is related to the data in the data warehouse, it would be useful to check the status of the nightly job that recalculates the data and overwrites the table. By reviewing the job in the BigQuery interface, you can see if it completed successfully and if there were any errors that may have caused the charts in Data Studio to break. Reviewing the Error Reporting page in the Cloud Console, using Cloud Debugger and creating a filter in Cloud Logging may not be directly related to the problem with the data.

for those who says 'C' is the answer : Cloud Debugger was deprecated on May 16, 2022 and the service was shut down on May 31, 2023. You can continue to use the open source Snapshot Debugger. Snapshot Debugger was archived on September 7, 2023, so it is not receiving bug fixes or security patches. Snapshot Debugger remains available for use. You can also fork the repository and maintain your own version. Cloud Debugger was deprecated on May 16, 2022 and the service was shut down on May 31, 2023. You can continue to use the open source Snapshot Debugger. Snapshot Debugger was archived on September 7, 2023, so it is not receiving bug fixes or security patches. Snapshot Debugger remains available for use. You can also fork the repository and maintain your own version. https://cloud.google.com/stackdriver/docs/deprecations/debugger-deprecation

So which on is the correct answer?

Since the issue involves a table in BigQuery, which is overwritten nightly with recalculated data, it's most likely that something went wrong during this nightly job. Reviewing the job in the BigQuery interface allows you to check for errors, job failures, or any issues during the table overwrite process, which could be causing the charts in Data Studio to break.

Answer: C Cloud Debugger provides targeted debugging capabilities for Data Studio, allowing you to focus on the specific report or charts that are experiencing issues.

You should use the BigQuery interface to review the nightly job and look for any errors. The reason for this is that the nightly job is responsible for recalculating the daily summary by overwriting the table. If there are any errors in the nightly job, it could cause the charts in Data Studio to be broken. By reviewing the nightly job, you can identify and fix any errors that may be causing the problem. The other options are not as likely to be helpful in this situation. The Error Reporting page in the Cloud Console is not likely to be helpful because it will only show errors that have been reported by other users. The Cloud Debugger is not likely to be helpful because it is used to debug code, not to troubleshoot problems with data. The Cloud Logging filter is not likely to be helpful because it is used to filter logs, not to troubleshoot problems with data.

B. Use the BigQuery interface to review the nightly job and look for any errors. Since the charts in Data Studio are broken, and the data is being appended to the data warehouse during the day and the daily summary is being recalculated at night by overwriting the table, the most likely cause of the problem is an error in the nightly job.

The other options are less likely to provide the information you need for this specific situation: A. "Review the Error Reporting page in the Cloud Console" is more about application errors in code, rather than issues with BigQuery job executions. C. "Use Cloud Debugger" is not applicable here as it's used for debugging applications written in languages like Java, Python, etc., and doesn't work with BigQuery SQL or data processing tasks. D. "In Cloud Logging, create a filter for your Data Studio report" might not be helpful because, while Cloud Logging does capture a wide variety of logs, it doesn't provide the direct, detailed job execution information that the BigQuery interface does. Furthermore, Data Studio reporting errors may not be related to the underlying data processing job.

B. Use the BigQuery interface to review the nightly job and look for any errors. Here’s why this approach is appropriate: Job Information: BigQuery logs information about every job executed, including data loads or query jobs. If the nightly job that refreshes your data warehouse table encountered an error, this would be captured and could be viewed in the job's information in the BigQuery console. Error Details: If the job failed or encountered issues, the BigQuery interface would provide details about the error, which can help you understand if there were problems with the query syntax, the data, or any other aspect of the operation. Immediate Feedback: Reviewing the job's execution details can give you immediate insights without needing to wait for logs or errors to propagate through other systems, which can be time-consuming and might not provide the direct feedback you need.

It needs to be B because it is the only way to proper investigate the issue.

The correct answer in this scenario would be option B: Use the BigQuery interface to review the nightly job and look for any errors. When the charts in Data Studio are broken after the nightly recalculation of the table, it indicates that there might be an issue with the data refresh or update process in BigQuery. By reviewing the nightly job in the BigQuery interface, you can check for any errors or anomalies that occurred during the recalculation process.

I am sorry to say that there is no filter for Data Studio in Cloud Logging (see: https://cloud.google.com/logging/docs/api/v2/resource-list#resource-indexes). So the correct answer is B.

I'll select D intuitively since I'm not using Data Studio and bigQuery that much

D makes sense.

i think the answer is D

My ans: D

There might be many issues in Google Data Studio, the fastest process might be to check Cloud Logging. Hence option D should be correct.