Cloud Digital Leader Scaling with Google Cloud operations Practice Test 6 — 15 Questions

Question 1

Match each Google Cloud security concept to its description.

Accepted Answer

Cloud IAM: Unified access control for Google Cloud resources. The correct matches are Cloud IAM (access control), Cloud KMS (key management), Cloud Security Command Center (threat detection), and Cloud DLP (data loss prevention). Common confusions include mixing up IAM with Security Command Center or KMS with Cloud Armor.

Answer

Cloud IAM: Security monitoring and threat detection

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Cloud KMS: Web application firewall and DDoS protection

Question 2

Match each Google Cloud serverless compute option to its characteristic.

Accepted Answer

Cloud Functions: Executes code in response to events, scaling to zero when idle.. The correct matches are: Cloud Functions for event-driven execution, Cloud Run for containerized HTTP services, and App Engine for managed web applications. Common confusions involve swapping the event-driven nature of Cloud Functions with the container-based approach of Cloud Run, or pairing App Engine with event-driven triggers instead of web app hosting.

Answer

Cloud Functions: Runs containerized applications in a managed environment.

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Cloud Run: Fully managed platform that supports multiple programming languages for web apps.

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App Engine: Event-driven, short-lived execution of code in response to triggers.

Question 3

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

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Health check interval is too long

Question 4

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

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Error Reporting

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Cloud Debugger

Question 5

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. 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)

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latency at 99th percentile

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requests/success

Question 6

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 7

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. 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

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Enable VPC Flow Logs

Answer

Use Cloud Logging's aggregation view

Question 8

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

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Cloud Logging

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Cloud Debugger

Question 9

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

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Error Reporting

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Cloud Logging queries

Question 10

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. 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

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HTTP load balancing serving capacity

Answer

Instance group size

Question 11

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. 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

Question 12

Which THREE of the following are best practices for managing operations in Google Cloud? (Choose THREE.)

Accepted Answer

Set up budget alerts to monitor costs. Setting up budget alerts in Google Cloud allows you to monitor costs proactively by triggering notifications when spending exceeds defined thresholds. This is a fundamental operational best practice to avoid unexpected bills and maintain financial control over your cloud resources.

Answer

Use Cloud Logging to store all logs indefinitely to ensure compliance

Answer

Use a single project for all workloads to simplify management

Question 13

Refer to the exhibit. The autoscaler is configured to maintain a target CPU utilization of 0.6. Currently the group has 10 instances, but the autoscaler is not scaling up even though CPU utilization is above 0.8. What is the most likely reason?

Accepted Answer

The maximum number of instances is set to 10. The autoscaler is configured to maintain a target CPU utilization of 0.6, but the current CPU utilization is above 0.8. Despite this, the autoscaler is not scaling up. The most likely reason is that the maximum number of instances is set to 10, and the group has already reached that limit. In Google Cloud, the autoscaler will not create new instances beyond the configured maximum, even if the target utilization is exceeded.

Answer

The autoscaler is disabled

Answer

The instance template is misconfigured

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The autoscaler cooldown period is preventing new instances

Question 14

Refer to the exhibit. A DevOps engineer wants to create a chart showing the rate of items sold per second over time. What is a limitation of this metric for that purpose?

Accepted Answer

The metric kind is GAUGE, so it cannot be used to calculate rate. A GAUGE metric type represents a point-in-time value (e.g., current number of items), not a cumulative counter. To calculate a rate (items per second), you need a CUMULATIVE counter metric that monotonically increases, allowing Cloud Monitoring to compute the derivative over time. GAUGE metrics lack the necessary monotonicity and cumulative semantics, so they cannot be used to derive a meaningful rate of change.

Answer

The interval should include a startTime

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The metric has no labels to filter

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The value should be DOUBLE instead of INT64

Question 15

A company runs a web application on Compute Engine. During seasonal sales, traffic spikes unpredictably. The operations team wants to ensure the application scales automatically without manual intervention while minimizing cost. Which solution should they implement?

Accepted Answer

Use a managed instance group with autoscaling based on CPU utilization.. A managed instance group (MIG) with autoscaling based on CPU utilization is the correct solution because it automatically adjusts the number of VM instances in response to real-time traffic spikes, ensuring the application scales out during high demand and scales in during low demand. This eliminates manual intervention and optimizes cost by only running the necessary number of instances based on a target CPU utilization threshold (e.g., 60-80%).

Answer

Create a managed instance group with a fixed number of instances.

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Use an unmanaged instance group and manually add instances.

Answer

Use a single large VM with vertical scaling.