Cloud Digital Leader Scaling with Google Cloud operations Practice Test 5 — 15 Questions

Question 1

Drag and drop the steps to set up a Cloud SQL for MySQL instance with a private IP address into the correct order.

Question 2

Drag and drop the steps to recover a Compute Engine VM from a snapshot in the correct order.

Question 3

Match each Google Cloud security concept to its description.

Question 4

Match each Google Cloud serverless compute option to its characteristic.

Question 5

A company runs a web application on Compute Engine instances behind a managed instance group with autoscaling based on CPU utilization. After a marketing campaign, traffic spikes and the autoscaler adds instances quickly, but the application becomes slow. What is the most likely cause?

Accepted Answer

Autoscaler uses CPU utilization but the application is memory-bound. The autoscaler adds instances based on CPU utilization, but if the application is memory-bound, adding more instances does not alleviate memory pressure. Each new instance still runs the same memory-intensive workload, so CPU may remain low while memory is exhausted, causing slowdowns. The autoscaler fails to address the actual bottleneck, leading to poor performance despite scaling out.

Answer

Instances are in different zones causing inter-zone latency

Answer

Autoscaling cooldown period is too short

Answer

Health check interval is too long

Question 6

A developer needs to debug a production issue by analyzing logs from multiple microservices. Which Google Cloud service should they use to filter and search logs in real time?

Accepted Answer

Cloud Logging. Cloud Logging (formerly Stackdriver Logging) is the correct service because it provides a centralized log management system that can ingest logs from multiple microservices, filter them using advanced queries, and search them in real time. Its Logs Explorer interface supports custom filters, labels, and timestamps, enabling developers to pinpoint production issues across distributed services without delay.

Answer

Cloud Monitoring

Answer

Error Reporting

Answer

Cloud Debugger

Question 7

A company wants to implement SLOs for their API service. They need to measure the proportion of successful requests over a 30-day window. Which metric should they use?

Accepted Answer

SLI = good events / total events. Option D is correct because an SLI (Service Level Indicator) is defined as the ratio of good events to total events, which directly measures the proportion of successful requests over a 30-day window. This aligns with the requirement to track request success rate, not just system uptime. In Google Cloud operations, SLOs are built on SLIs that count discrete events like HTTP 200 responses versus all requests.

Answer

availability (uptime)

Answer

latency at 99th percentile

Answer

requests/success

Question 8

A company is migrating to Google Cloud and wants to reduce operational overhead for managing their infrastructure. Which Google Cloud service allows them to define infrastructure as code and automate provisioning?

Accepted Answer

Cloud Deployment Manager. Cloud Deployment Manager is the correct answer because it is a Google Cloud service that allows you to define your infrastructure as code using declarative templates (in YAML, Python, or Jinja2). It automates the provisioning and management of Google Cloud resources, reducing manual operational overhead by enabling repeatable, version-controlled deployments.

Answer

Google Cloud SDK

Answer

Cloud Console

Answer

Cloud Shell

Question 9

An organization has multiple projects and wants to aggregate logs from all projects into a single bucket for long-term retention and compliance. What should they do?

Accepted Answer

Use log sinks to route logs to a Cloud Storage bucket in a central project. Option D is correct because log sinks in Cloud Logging can route logs from multiple source projects to a centralized Cloud Storage bucket in a separate project. This meets the requirement for long-term retention and compliance, as Cloud Storage provides durable, cost-effective archival storage with lifecycle management policies.

Answer

Use log sinks to route logs to a BigQuery dataset

Answer

Enable VPC Flow Logs

Answer

Use Cloud Logging's aggregation view

Question 10

A developer is troubleshooting a slow response from a Cloud Run service. Which Google Cloud service can they use to trace requests across microservices?

Accepted Answer

Cloud Trace. Cloud Trace is the correct service because it is specifically designed for distributed tracing, collecting latency data from applications and displaying it in a trace timeline. It can trace requests as they propagate across multiple microservices, including Cloud Run services, by using trace context propagation headers (e.g., `X-Cloud-Trace-Context`). This allows the developer to identify bottlenecks and slow components in a request path.

Answer

Cloud Profiler

Answer

Cloud Logging

Answer

Cloud Debugger

Question 11

A company uses Cloud Functions and notices that some functions are taking longer than expected. They want to identify which functions have the highest latency. What should they use?

Accepted Answer

Cloud Monitoring metrics. Cloud Monitoring metrics, specifically the 'execution_time' metric for Cloud Functions, provide the precise latency data needed to identify functions with the highest execution duration. Unlike logs or error reports, metrics are designed for numerical aggregation and can be used to create dashboards or alerts that rank functions by their p50, p95, or p99 latency values.

Answer

Cloud Audit Logs

Answer

Error Reporting

Answer

Cloud Logging queries

Question 12

A team wants to set up alerts for when the error budget of their service is exhausted. The service has an SLO of 99.9% availability over a 30-day rolling window. Which condition should they use in Cloud Monitoring alerting?

Accepted Answer

error budget remaining < 10%. Option A is correct because the error budget is the amount of allowable downtime over the 30-day window (0.1% of total time). Setting an alert when the remaining budget drops below 10% gives the team early warning before the budget is fully exhausted, allowing proactive remediation. Cloud Monitoring alerting policies can use the `error_budget_remaining` metric to trigger on a threshold like < 10%.

Answer

burn rate > 1 over 1 hour

Answer

latency > 100ms

Answer

SLI < 99.9%

Question 13

A company wants to automatically scale their Compute Engine managed instance group based on the number of requests per second. Which metric should they use?

Accepted Answer

custom metric from Cloud Monitoring. Option D is correct because the company needs to scale based on requests per second, which is a custom application-level metric. Cloud Monitoring allows you to create custom metrics from your application, and managed instance groups can use these custom metrics for autoscaling, enabling precise scaling based on actual request throughput rather than proxy indicators.

Answer

CPU utilization

Answer

HTTP load balancing serving capacity

Answer

Instance group size

Question 14

A site reliability engineer is implementing SRE practices in Google Cloud. Which TWO of the following are key principles of SRE? (Choose TWO.)

Accepted Answer

Using error budgets to balance reliability and feature velocity. Option A is correct because error budgets are a core SRE principle that define the acceptable level of failure (e.g., 99.9% uptime allows 0.1% errors). This budget is used to balance the tension between releasing new features (velocity) and maintaining system reliability, allowing teams to halt deployments when the budget is exhausted.

Answer

Measuring everything with SLIs, SLOs, and SLAs

Answer

Centralizing all operations in a single team

Answer

Deploying changes only during maintenance windows

Question 15

A company uses Cloud Monitoring to collect metrics from their applications running on Google Kubernetes Engine (GKE). They want to create custom dashboards and set up alerting policies. Which THREE capabilities are available in Cloud Monitoring? (Choose THREE.)

Accepted Answer

Define custom metrics via the Monitoring API. Option C is correct because the Cloud Monitoring API allows you to define and write custom metrics, which can then be used in dashboards and alerting policies. This is essential for capturing application-specific data that is not automatically collected by the default GKE integration, such as business KPIs or custom performance counters.

Answer

Query logs using Logging Query Language

Answer

Automatically remediate incidents with Cloud Functions