Free Access Google.Associate-Cloud-Engineer.v2022-04-13.q194 Practice Test (Page 11)

Question 46

You need to select and configure compute resources for a set of batch processing jobs. These jobs take around 2 hours to complete and are run nightly. You want to minimize service costs. What should you do?

A.Select Google Kubernetes Engine. Use a three-node cluster with micro instance type.

B.Select Google Kubernetes Engine. Use a single-node culster with a small instance type.

C.Select Compute Engine. Use VM instance types that support micro bursting.

D.Select Compute Engine. Use preemptible VM instances of the appropriate standard machine type.

Question 47

You will have several applications running on different Compute Engine instances in the same project. You want to specify at a more granular level the service account each instance uses when calling Google Cloud APIs. What should you do?

A.When creating the instances, assign the name of each Service Account as instance metadata

B.When creating the instances, specify a Service Account for each instance

C.After starting the instances, use gcloud compute instances update to specify a Service Account for each instance

D.After starting the instances, use gcloud compute instances update to assign the name of the relevant Service Account as instance metadata

Question 48

You are using Data Studio to visualize a table from your data warehouse that is built on top of BigQuery. Data is appended to the data warehouse during the day. At night, the daily summary is recalculated by overwriting the table. You just noticed that the charts in Data Studio are broken, and you want to analyze the problem.
What should you do?

A.Review the Error Reporting page in the Cloud Console to find any errors.

B.Use the BigQuery interface to review the nightly Job and look for any errors

C.In Cloud Logging create a filter for your Data Studio report

D.Use Cloud Debugger to find out why the data was not refreshed correctly

Question 49

Your developers have been thoroughly logging everything that happens in the API. The API allows end users to request the data as JSON, XML, CSV, and XLS. Supporting all of these formats is taking a lot of developer effort. Management would like to start tracking which options are used over the next month. Without modifying the code, what's the fastest way to be able to report on this data at the end of the month?

A.Create a custom monitoring metric in code and edit the API code to set the metric each time the API is called.

B.Export the logs to excel, and search for the different fields.

C.Create a custom counter logging metric that uses a regex to extract the data format into a label. At the end of the month, use the metric
viewer to see the group by the label.

D.Create a log sink that filters for rows that mention the data format. Export that to BigQuery, and run a query at the end of the month.

Question 50

You are asked to set up application performance monitoring on Google Cloud projects A, B, and C as a single pane of glass. You want to monitor CPU, memory, and disk. What should you do?

A.Enable API, create a workspace under project A, and then add project B and C.

B.Enable API and then give the metrics.reader role to projects A, B, and C.

C.Enable API and then use default dashboards to view all projects in sequence.

D.Enable API and then share charts from project A, B, and C.

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Question 46

Question 47

Question 48

Question 49

Question 50

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