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Viewing questions 31-40 out of questions

Questions # 31:

Your company is seeking a scalable solution to retain and explore application logs hosted on Compute Engine. You must be able to analyze your logs with SQL queries, and you want to be able to create charts to identify patterns and trends in your logs over time. You want to follow Google-recommended practices and minimize your operational costs. What should you do?

Options:

Use a custom script to push your application logs to Cloud SQL for exploration.

Ingest your application logs to Cloud Logging by using Ops Agent, and explore your logs in Logs Explorer.

Ingest your application logs to Cloud Logging by using Ops Agent, and explore your logs with Log Analytics.

Use a custom script to push your application logs to BigQuery for exploration.

Questions # 32:

(Your company was recently impacted by a service disruption that caused multiple Dataflow jobs to get stuck, resulting in significant downtime in downstream applications and revenue loss. You were able to resolve the issue by identifying and fixing an error you found in the code. You need to design a solution with minimal management effort to identify when jobs are stuck in the future to ensure that this issue does not occur again. What should you do?)

Options:

Set up Error Reporting to identify stack traces that indicate slowdowns in Dataflow jobs. Set up alerts based on these log entries.

Use the Personalized Service Health dashboard to identify issues with Dataflow jobs across regions.

Update the Dataflow job configurations to send messages to a Pub/Sub topic when there are delays. Configure a backup Dataflow job to process jobs that are delayed. Use Cloud Tasks to trigger an alert when messages are pushed to the Pub/Sub topic.

Set up Cloud Monitoring alerts on the data freshness metric for the Dataflow jobs to receive a notification when a certain threshold is reached.

Answer

Explanation

Comprehensive and Detailed In Depth Explanation:

The goal is to proactively identify stuck Dataflow jobs with minimal management effort. Let's analyze each option:

A. Error Reporting for slowdowns: Error Reporting primarily focuses on capturing and aggregating exceptions and errors (stack traces). While a stuck job might eventually throw an error, it might also just become unresponsive without generating explicit errors. Relying solely on Error Reporting might not provide timely detection of stuck jobs. Identifying stack traces that indicate slowdowns can also be complex and require significant manual configuration and analysis.

B. Personalized Service Health dashboard: The Personalized Service Health dashboard provides information about Google Cloud service incidents that might be affecting your resources. While it can alert you to broader Dataflow service outages, it won't specifically identify individual stuck jobs due to application-level errors or logic within your Dataflow pipeline.

C. Pub/Sub messages for delays, backup job, and Cloud Tasks alerts: This approach involves significant custom implementation and management. You would need to instrument your Dataflow jobs to detect delays, send messages to Pub/Sub, manage a backup job, and configure Cloud Tasks for alerting. This adds considerable operational overhead and complexity.

D. Cloud Monitoring alerts on data freshness metric: Dataflow provides built-in metrics, including "data freshness" (or similar metrics like "system lag" or "processing time"), which indicate how far behind the pipeline is in processing data. If a job gets stuck, the data freshness will deteriorate beyond an acceptable threshold. Cloud Monitoring allows you to easily set up alerts based on these built-in metrics. This requires minimal custom coding and leverages the platform's existing monitoring capabilities, aligning with the "minimal management effort" requirement.

Therefore, setting up Cloud Monitoring alerts on relevant Dataflow metrics like data freshness is the most efficient and recommended way to detect stuck Dataflow jobs with minimal management overhead.

Google Cloud Documentation References:

Monitoring Dataflow Pipelines: https://cloud.google.com/dataflow/docs/guides/monitoring-your-pipeline - This document details the various metrics available for monitoring Dataflow jobs in Cloud Monitoring, including metrics related to processing time, system lag, and data freshness.

Creating Alerts in Cloud Monitoring: https://cloud.google.com/monitoring/alerts/create - Explains how to set up alerts based on metrics collected by Cloud Monitoring.

Dataflow Metrics: https://cloud.google.com/dataflow/docs/reference/monitoring-metrics - Provides a comprehensive list of Dataflow metrics that can be used for monitoring and alerting.

Questions # 33:

You need a dynamic way of provisioning VMs on Compute Engine. The exact specifications will be in a dedicated configuration file. You want to follow Google’s recommended practices. Which method should you use?

Options:

Deployment Manager

Cloud Composer

Managed Instance Group

Unmanaged Instance Group

Questions # 34:

You are managing a project for the Business Intelligence (BI) department in your company. A data pipeline ingests data into BigQuery via streaming. You want the users in the BI department to be able to run the custom SQL queries against the latest data in BigQuery. What should you do?

Options:

Create a Data Studio dashboard that uses the related BigQuery tables as a source and give the BI team view access to the Data Studio dashboard.

Create a Service Account for the BI team and distribute a new private key to each member of the BI team.

Use Cloud Scheduler to schedule a batch Dataflow job to copy the data from BigQuery to the BI team's internal data warehouse.

Assign the IAM role of BigQuery User to a Google Group that contains the members of the BI team.

Questions # 35:

(You are migrating your company’s on-premises compute resources to Google Cloud. You need to deploy batch processing jobs that run every night. The jobs require significant CPU and memory for several hours but can tolerate interruptions. You must ensure that the deployment is cost-effective. What should you do?)

Options:

Containerize the batch processing jobs and deploy them on Compute Engine.

Use custom machine types on Compute Engine.

Use the M1 machine series on Compute Engine.

Use Spot VMs on Compute Engine.

Answer

Questions # 36:

Every employee of your company has a Google account. Your operational team needs to manage a large number of instances on Compute Engine. Each member of this team needs only administrative access to the servers. Your security team wants to ensure that the deployment of credentials is operationally efficient and must be able to determine who accessed a given instance. What should you do?

Options:

Generate a new SSH key pair. Give the private key to each member of your team. Configure the public key in the metadata of each instance.

Ask each member of the team to generate a new SSH key pair and to send you their public key. Use a configuration management tool to deploy those keys on each instance.

Ask each member of the team to generate a new SSH key pair and to add the public key to their Google account. Grant the “compute.osAdminLogin” role to the Google group corresponding to this team.

Generate a new SSH key pair. Give the private key to each member of your team. Configure the public key as a project-wide public SSH key in your Cloud Platform project and allow project-wide public SSH keys on each instance.

Questions # 37:

For analysis purposes, you need to send all the logs from all of your Compute Engine instances to a BigQuery dataset called platform-logs. You have already installed the Stackdriver Logging agent on all the instances. You want to minimize cost. What should you do?

Options:

1. Give the BigQuery Data Editor role on the platform-logs dataset to the service accounts used by your instances.2. Update your instances’ metadata to add the following value: logs-destination: bq://platform-logs.

1. In Stackdriver Logging, create a logs export with a Cloud Pub/Sub topic called logs as a sink.2. Create a Cloud Function that is triggered by messages in the logs topic.3. Configure that Cloud Function to drop logs that are not from Compute Engine and to insert Compute Engine logs in the platform-logs dataset.

1. In Stackdriver Logging, create a filter to view only Compute Engine logs.2. Click Create Export.3. Choose BigQuery as Sink Service, and the platform-logs dataset as Sink Destination.

1. Create a Cloud Function that has the BigQuery User role on the platform-logs dataset.2. Configure this Cloud Function to create a BigQuery Job that executes this query:INSERT INTO dataset.platform-logs (timestamp, log)SELECT timestamp, log FROM compute.logsWHERE timestamp > DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY)3. Use Cloud Scheduler to trigger this Cloud Function once a day.

Questions # 38:

You are the project owner of a GCP project and want to delegate control to colleagues to manage buckets and files in Cloud Storage. You want to follow Google-recommended practices. Which IAM roles should you grant your colleagues?

Options:

Project Editor

Storage Admin

Storage Object Admin

Storage Object Creator

Questions # 39:

You are performing a monthly security check of your Google Cloud environment and want to know who has access to view data stored in your Google Cloud

Project. What should you do?

Options:

Enable Audit Logs for all APIs that are related to data storage.

Review the IAM permissions for any role that allows for data access.

Review the Identity-Aware Proxy settings for each resource.

Create a Data Loss Prevention job.

Questions # 40:

You have two subnets (subnet-a and subnet-b) in the default VPC. Your database servers are running in subnet-a. Your application servers and web servers are running in subnet-b. You want to configure a firewall rule that only allows database traffic from the application servers to the database servers. What should you do?

Options:

* Create service accounts sa-app and sa-db.• Associate service account: sa-app with the application servers and the service account sa-db with the database servers.• Create an ingress firewall rule to allow network traffic from source service account sa-app to target service account sa-db.

• Create network tags app-server and db-server.• Add the app-server lag lo the application servers and the db-server lag to the database servers.• Create an egress firewall rule to allow network traffic from source network tag app-server to target network tag db-server.

* Create a service account sa-app and a network tag db-server.* Associate the service account sa-app with the application servers and the network tag db-server withthe database servers.• Create an ingress firewall rule to allow network traffic from source VPC IP addresses and target the subnet-a IP addresses.

• Create a network lag app-server and service account sa-db.• Add the tag to the application servers and associate the service account with the database servers.• Create an egress firewall rule to allow network traffic from source network tag app-server to target service account sa-db.

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