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Google Professional-Cloud-DevOps-Engineer Exam Syllabus Topics:

Section	Objectives
Optimize performance and continuous delivery	- System performance optimization 1. Scaling strategies and load handling - Monitoring and observability 1. Performance tuning and feedback loops 2. Cloud Monitoring and Logging
Implement security and compliance	- Compliance and governance 1. Audit logging and policy enforcement - Secure CI/CD pipelines 1. Secret and credential management 2. IAM and least privilege access
Implement site reliability engineering (SRE) practices	- Define and manage SLI, SLO, and SLA 1. Error budgets and monitoring strategies - Incident management and postmortems 1. Alerting and on-call practices
Develop and implement CI/CD pipelines	- Automate build, test, and deployment processes 1. Release management practices 2. Deployment automation strategies - Build and manage CI/CD pipelines using Google Cloud tools 1. Cloud Build pipeline design 2. Artifact repository management

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q137-Q142):

NEW QUESTION # 137
You support a multi-region web service running on Google Kubernetes Engine (GKE) behind a Global HTTP'S Cloud Load Balancer (CLB). For legacy reasons, user requests first go through a third-party Content Delivery Network (CDN). which then routes traffic to the CLB. You have already implemented an availability Service Level Indicator (SLI) at the CLB level. However, you want to increase coverage in case of a potential load balancer misconfiguration. CDN failure, or other global networking catastrophe. Where should you measure this new SLI?
Choose 2 answers

A. Metrics exported from the application servers
B. Instrumentation coded directly in the client
C. Your application servers' logs
D. GKE health checks for your application servers
E. A synthetic client that periodically sends simulated user requests

Answer: B,E

NEW QUESTION # 138
Your company runs an ecommerce website built with JVM-based applications and microservice architecture in Google Kubernetes Engine (GKE) The application load increases during the day and decreases during the night Your operations team has configured the application to run enough Pods to handle the evening peak load You want to automate scaling by only running enough Pods and nodes for the load What should you do?

A. Configure the Horizontal Pod Autoscaler and enable the cluster autoscaler
B. Configure the Vertical Pod Autoscaler but keep the node pool size static
C. Configure the Horizontal Pod Autoscaler but keep the node pool size static
D. Configure the Vertical Pod Autoscaler and enable the cluster autoscaler

Answer: A

NEW QUESTION # 139
Your team is designing a new application for deployment into Google Kubernetes Engine (GKE). You need to set up monitoring to collect and aggregate various application-level metrics in a centralized location. You want to use Google Cloud Platform services while minimizing the amount of work required to set up monitoring. What should you do?

A. Install the Cloud Pub/Sub client libraries, push various metrics from the application to various topics, and then observe the aggregated metrics in Stackdriver.
B. Install the OpenTelemetry client libraries in the application, configure Stackdriver as the export destination for the metrics, and then observe the application's metrics in Stackdriver.
C. Emit all metrics in the form of application-specific log messages, pass these messages from the containers to the Stackdriver logging collector, and then observe metrics in Stackdriver.
D. Publish various melrics from the application directly to the Slackdriver Monitoring API, and then observe these custom metrics in Stackdriver.

Answer: D

NEW QUESTION # 140
Your company runs applications in Google Kubernetes Engine (GKE). Several applications rely on ephemeral volumes. You noticed some applications were unstable due to the DiskPressure node condition on the worker nodes. You need to identify which Pods are causing the issue, but you do not have execute access to workloads and nodes.
What should you do?

A. Check the node/ephemeral_storage/used_bytes metric by using Metrics Explorer.
B. Locate all the Pods with emptyDir volumes. use the df-h command to measure volume disk usage.
C. Locate all the Pods with emptyDir volumes. Use the du -sh * command to measure volume disk usage.
D. Check the metric by using Metrics Explorer.

Answer: A

Explanation:
The correct answer is A. Check the node/ephemeral_storage/used_bytes metric by using Metrics Explorer.
The node/ephemeral_storage/used_bytes metric reports the total amount of ephemeral storage used by Pods on each node1.You can use Metrics Explorer to query and visualize this metric and filter it by node name, namespace, or Pod name2. This way, you can identify which Pods are consuming the most ephemeral storage and causing disk pressure on the nodes. You do not need to have execute access to the workloads or nodes to use Metrics Explorer.
The other options are incorrect because they require execute access to the workloads or nodes, which you do not have.The df -h and du -sh * commands are Linux commands that can measure disk usage, but you need to run them inside the Pods or on the nodes, which is not possible in your scenario34.
Reference:
Monitoring metrics for Kubernetes system components, Node metrics, node/ephemeral_storage/used_bytes.
Using Metrics Explorer, Querying metrics.How do I find out disk space utilization information using Linux command line?, df command.How to check disk space in Linux from the command line, du command.

NEW QUESTION # 141
You support a high-traffic web application and want to ensure that the home page loads in a timely manner. As a first step, you decide to implement a Service Level Indicator (SLI) to represent home page request latency with an acceptable page load time set to 100 ms. What is the Google-recommended way of calculating this SLI?

A. Buckelize Ihe request latencies into ranges, and then compute the percentile at 100 ms.
B. Count the number of home page requests that load in under 100 ms. and then divide by the total number of all web application requests.
C. Bucketize the request latencies into ranges, and then compute the median and 90th percentiles.
D. Count the number of home page requests that load in under 100 ms, and then divide by the total number of home page requests.

Answer: D

NEW QUESTION # 142
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