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Viewing questions 21-30 out of questions

Questions # 21:

For this question, refer to the Dress4Win case study.

Dress4Win has end-to-end tests covering 100% of their endpoints. They want to ensure that the move to the cloud does not introduce any new bugs. Which additional testing methods should the developers employ to prevent an outage?

Options:

They should enable Google Stackdriver Debugger on the application code to show errors in the code.

They should add additional unit tests and production scale load tests on their cloud staging environment.

They should run the end-to-end tests in the cloud staging environment to determine if the code is working as intended.

They should add canary tests so developers can measure how much of an impact the new release causes to latency.

Questions # 22:

Dress4win has end to end tests covering 100% of their endpoints.

They want to ensure that the move of cloud does not introduce any new bugs.

Which additional testing methods should the developers employ to prevent an outage?

Options:

They should run the end to end tests in the cloud staging environment to determine if the code is working as

intended.

They should enable google stack driver debugger on the application code to show errors in the code

They should add additional unit tests and production scale load tests on their cloud staging environment.

They should add canary tests so developers can measure how much of an impact the new release causes to latency

Questions # 23:

For this question, refer to the Dress4Win case study.

As part of their new application experience, Dress4Wm allows customers to upload images of themselves. The customer has exclusive control over who may view these images. Customers should be able to upload images with minimal latency and also be shown their images quickly on the main application page when they log in. Which configuration should Dress4Win use?

Options:

Store image files in a Google Cloud Storage bucket. Use Google Cloud Datastore to maintain metadata that maps each customer's ID and their image files.

Store image files in a Google Cloud Storage bucket. Add custom metadata to the uploaded images in Cloud Storage that contains the customer's unique ID.

Use a distributed file system to store customers' images. As storage needs increase, add more persistent disks and/or nodes. Assign each customer a unique ID, which sets each file's owner attribute, ensuring privacy of images.

Use a distributed file system to store customers' images. As storage needs increase, add more persistent disks and/or nodes. Use a Google Cloud SQL database to maintain metadata that maps each customer's ID to their image files.

Questions # 24:

The current Dress4win system architecture has high latency to some customers because it is located in one

data center.

As of a future evaluation and optimizing for performance in the cloud, Dresss4win wants to distribute it's system

architecture to multiple locations when Google cloud platform.

Which approach should they use?

Options:

Use regional managed instance groups and a global load balancer to increase performance because the

regional managed instance group can grow instances in each region separately based on traffic.

Use a global load balancer with a set of virtual machines that forward the requests to a closer group of

virtual machines managed by your operations team.

Use regional managed instance groups and a global load balancer to increase reliability by providing

automatic failover between zones in different regions.

Use a global load balancer with a set of virtual machines that forward the requests to a closer group of

virtual machines as part of a separate managed instance groups.

Questions # 25:

For this question, refer to the Dress4Win case study.

Dress4Win has configured a new uptime check with Google Stackdriver for several of their legacy services. The Stackdriver dashboard is not reporting the services as healthy. What should they do?

Options:

Install the Stackdriver agent on all of the legacy web servers.

In the Cloud Platform Console download the list of the uptime servers' IP addresses and create an inbound firewall rule

Configure their load balancer to pass through the User-Agent HTTP header when the value matches GoogleStackdriverMonitoring-UptimeChecks (https://cloud.google.com/monitoring)

Configure their legacy web servers to allow requests that contain user-Agent HTTP header when the value matches GoogleStackdriverMonitoring— UptimeChecks (https://cloud.google.com/monitoring)

Questions # 26:

For this question, refer to the Dress4Win case study.

You want to ensure Dress4Win's sales and tax records remain available for infrequent viewing by auditors for at least 10 years. Cost optimization is your top priority. Which cloud services should you choose?

Options:

Google Cloud Storage Coldline to store the data, and gsutil to access the data.

Google Cloud Storage Nearline to store the data, and gsutil to access the data.

Google Bigtabte with US or EU as location to store the data, and gcloud to access the data.

BigQuery to store the data, and a web server cluster in a managed instance group to access the data. Google Cloud SQL mirrored across two distinct regions to store the data, and a Redis cluster in a managed instance group to access the data.

Questions # 27:

For this question, refer to the Dress4Win case study.

At Dress4Win, an operations engineer wants to create a tow-cost solution to remotely archive copies of database backup files. The database files are compressed tar files stored in their current data center. How should he proceed?

Options:

Create a cron script using gsutil to copy the files to a Coldline Storage bucket.

Create a cron script using gsutil to copy the files to a Regional Storage bucket.

Create a Cloud Storage Transfer Service Job to copy the files to a Coldline Storage bucket.

Create a Cloud Storage Transfer Service job to copy the files to a Regional Storage bucket.

Questions # 28:

For this question, refer to the TerramEarth case study. A new architecture that writes all incoming data to

BigQuery has been introduced. You notice that the data is dirty, and want to ensure data quality on an

automated daily basis while managing cost.

What should you do?

Options:

Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.

Create a Cloud Function that reads data from BigQuery and cleans it. Trigger it. Trigger the Cloud Function from a Compute Engine instance.

Create a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.

Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.

Questions # 29:

For this question, refer to the TerramEarth case study.

You start to build a new application that uses a few Cloud Functions for the backend. One use case requires a Cloud Function func_display to invoke another Cloud Function func_query. You want func_query only to accept invocations from func_display. You also want to follow Google's recommended best practices. What should you do?

Options:

Create a token and pass it in as an environment variable to func_display. When invoking func_query, include the token in the request Pass the same token to func _query and reject the invocation if the tokens are different.

Make func_query 'Require authentication.' Create a unique service account and associate it to func_display. Grant the service account invoker role for func_query. Create an id token in func_display and include the token to the request when invoking func_query.

Make func _query 'Require authentication' and only accept internal traffic. Create those two functions in the same VPC. Create an ingress firewall rule for func_query to only allow traffic from func_display.

Create those two functions in the same project and VPC. Make func_query only accept internal traffic. Create an ingress firewall for func_query to only allow traffic from func_display. Also, make sure both functions use the same service account.

Questions # 30:

For this question, refer to the TerramEarth case study. TerramEarth has decided to store data files in Cloud Storage. You need to configure Cloud Storage lifecycle rule to store 1 year of data and minimize file storage cost.

Which two actions should you take?

Options:

Create a Cloud Storage lifecycle rule with Age: “30”, Storage Class: “Standard”, and Action: “Set to Coldline”, and create a second GCS life-cycle rule with Age: “365”, Storage Class: “Coldline”, and Action: “Delete”.

Create a Cloud Storage lifecycle rule with Age: “30”, Storage Class: “Coldline”, and Action: “Set to Nearline”, and create a second GCS life-cycle rule with Age: “91”, Storage Class: “Coldline”, and Action: “Set to Nearline”.

Create a Cloud Storage lifecycle rule with Age: “90”, Storage Class: “Standard”, and Action: “Set to Nearline”, and create a second GCS life-cycle rule with Age: “91”, Storage Class: “Nearline”, and Action: “Set to Coldline”.