Cloud Digital Leader Practice Test 29 — 15 Questions

Question 1

A large enterprise runs a critical application on Google Cloud consisting of Compute Engine instances behind a TCP load balancer. The application experiences intermittent slow response times that last for about 10 minutes before returning to normal. This pattern has been occurring every few days at random times. The operations team has configured Cloud Monitoring alerts for CPU and memory, but no alerts have fired. They have also reviewed the load balancer logs and see no errors, but the latency spikes. The application logs show no errors during these periods. The team suspects a resource bottleneck but cannot find it. Further investigation reveals that the application makes synchronous calls to an external authentication service for each request. What is the most likely cause and corrective action?

Accepted Answer

The application is making synchronous calls to an external authentication service that occasionally has latency spikes; implement caching and asynchronous processing.. The intermittent latency spikes lasting ~10 minutes, with no errors in application or load balancer logs and no CPU/memory alerts, point to an external dependency issue. The synchronous calls to the external authentication service are the likely bottleneck: if that service experiences transient latency, every request is blocked, causing the application's response time to spike. Caching authentication tokens and using asynchronous processing (e.g., a queue or background refresh) decouples the application from the external service's variability, eliminating the cascading latency.

Answer

The TCP load balancer is experiencing connection draining issues; switch to a proxy-based load balancer.

Answer

The instance group's autoscaler is configured with a cooldown period that is too long; reduce the cooldown period.

Answer

The virtual machine instances are suffering from CPU throttling due to sustained use of burstable CPU; move to a machine type with more CPUs.

Question 2

A healthcare company runs its critical application on Google Cloud. The application uses Cloud SQL for patient records, Cloud Storage for medical images, and Pub/Sub for data ingestion. The security team requires that all data at rest be encrypted with a key that is managed and rotated by their on-premises HSM. They also need to ensure that any potential data exfiltration is immediately detected and prevented. Recently, a vulnerability scan revealed that a Cloud SQL instance had a public IP. The team wants to enforce that no Cloud SQL instance can be created with a public IP across the entire organization. Additionally, they need to implement a solution to monitor and alert on any suspicious activity, such as a large download from Cloud Storage. They have a limited budget and cannot afford complex custom solutions. Which combination of Google Cloud services should they use to meet these requirements?

Accepted Answer

Use Cloud External Key Manager (EKM) for encryption, define an Organization Policy constraint to prohibit public IPs on Cloud SQL, deploy Security Command Center with Event Threat Detection to monitor for data exfiltration, and implement VPC Service Controls to limit data access.. Option B is correct because Cloud External Key Manager (EKM) allows you to use an external key management system (on-premises HSM) for encrypting data at rest in Google Cloud services like Cloud SQL, Cloud Storage, and Pub/Sub. The Organization Policy constraint `constraints/sql.restrictPublicIp` can enforce that no Cloud SQL instance is created with a public IP. Security Command Center with Event Threat Detection provides out-of-the-box monitoring and alerting for suspicious activities like large downloads from Cloud Storage, while VPC Service Controls adds a data exfiltration prevention layer by restricting data movement outside a defined service perimeter.

Answer

Use CMEK with Cloud KMS for encryption, set an Organization Policy to restrict public IPs on Cloud SQL, and configure Cloud Audit Logs with alerting via Cloud Monitoring to detect data exfiltration.

Answer

Use default encryption with Google-managed keys, set an IAM condition to deny public IP on Cloud SQL, and configure Cloud Data Loss Prevention to detect sensitive data exfiltration.

Answer

Use Cloud HSM for encryption, create a VPC firewall rule to block all incoming traffic to Cloud SQL, and use Cloud Armor to protect against data exfiltration.

Question 3

A financial services company runs a multi-tier application on Google Kubernetes Engine (GKE). The application consists of a frontend service, a backend service, and a database on Cloud SQL. Recently, they noticed that the backend service experiences high latency during peak trading hours, causing the frontend to time out. The backend service is CPU-intensive and currently runs with a single replica. The team wants to reduce latency and ensure high availability without over-provisioning resources. They have enabled Horizontal Pod Autoscaling (HPA) based on CPU utilization with a target of 80% and default stabilization windows. However, during sudden traffic spikes, the HPA takes over 5 minutes to scale up because of the scale-up stabilization window and the time to trigger. The company cannot tolerate latency spikes during scaling. Which course of action should they take to minimize latency during traffic spikes?

Accepted Answer

Set the HPA target CPU utilization to 70% and remove the scale-up stabilization window (set to 0 seconds).. Option C is correct because reducing the HPA target CPU utilization to 70% provides a larger buffer before the threshold is crossed, allowing earlier scaling initiation. Removing the scale-up stabilization window (setting it to 0 seconds) eliminates the default 5-minute delay, enabling the HPA to react immediately to CPU spikes. This directly addresses the latency issue during sudden traffic spikes by reducing the time to add replicas.

Answer

Increase the HPA scale-up stabilization window to 10 minutes to prevent thrashing.

Answer

Enable session affinity on the backend service to keep users on the same pod.

Answer

Use a CronJob to proactively scale the backend service before expected peak hours.

Question 4

A large online retailer operates a microservices-based e-commerce platform on Google Kubernetes Engine (GKE) across multiple zones. The application consists of several stateless services that handle customer traffic, inventory, and order processing. Recently, the company migrated its relational database to Cloud Spanner to achieve global scalability and strong consistency. After the migration, during peak shopping periods (e.g., Black Friday), the application experiences significant performance degradation. The operations team monitors CPU utilization of the pods and finds it consistently below 60% even under heavy load. However, Cloud Spanner metrics show high query latency and increased number of transactions waiting for lock conflicts. The team suspects that the bottleneck is now the database, not the compute. The application is designed to scale horizontally by adding more pod replicas. The team wants to ensure that scaling decisions are based on the actual performance bottleneck. What should they do?

Accepted Answer

Modify the Horizontal Pod Autoscaler (HPA) to scale based on a custom metric that reflects Cloud Spanner query latency.. Option D is correct because the Horizontal Pod Autoscaler (HPA) can be configured to scale based on custom metrics, such as Cloud Spanner query latency. Since the bottleneck is the database, scaling pods based on CPU utilization (which remains low) would not resolve the issue; instead, scaling based on Spanner latency ensures that the application adds replicas only when the database can handle more connections, reducing lock contention and improving overall performance.

Answer

Scale the GKE cluster to use larger node instances.

Answer

Increase the CPU request limit for the pods to allow higher CPU usage.

Answer

Reduce the number of pods to decrease Spanner load.

Question 5

A mid-sized logistics company runs its core tracking application on a single on-premises server. The application is critical for real-time package tracking and customer notifications. Recently, during a regional power outage, the server went down for 6 hours, causing significant customer dissatisfaction and loss of revenue. The company wants to move to Google Cloud to improve availability and disaster recovery. They have a limited IT team with minimal cloud experience and a tight budget. The application is a monolithic Java application that currently runs on a Linux server with a MySQL database. The company needs a solution that minimizes operational overhead and provides automatic failover across regions. Which course of action should they take?

Accepted Answer

Deploy the application on Compute Engine with a regional managed instance group and use Cloud SQL with high availability.. Option C is correct because it provides automatic failover across regions using a regional managed instance group (MIG) for the Compute Engine VMs and Cloud SQL with high availability (HA). The regional MIG distributes instances across multiple zones within a region, and Cloud SQL HA uses synchronous replication to a standby in a different zone, ensuring automatic failover without requiring application refactoring. This minimizes operational overhead for a small IT team with limited cloud experience and fits a tight budget by avoiding complex container orchestration or multi-cluster setups.

Answer

Refactor the application to run on App Engine and use Cloud SQL with a cross-region replica for failover.

Answer

Lift and shift the application to a single Compute Engine VM and use a Cloud SQL instance in the same region.

Answer

Containerize the application and run it on Google Kubernetes Engine with a multi-cluster setup across regions.

Question 6

A retail company experiences heavy traffic during holiday sales and low traffic at other times. Which cloud computing characteristic is most beneficial for handling this variable workload?

Accepted Answer

Elasticity. Option A is correct because elasticity allows resources to scale up and down automatically based on demand. Option B is wrong because pay-as-you-go is a pricing model, not a scaling characteristic. Option C is wrong because high availability focuses on uptime, not scaling. Option D is wrong because broad network access is about accessibility via the internet.

Answer

Broad network access

Answer

High availability

Answer

Pay-as-you-go pricing

Question 7

A company plans to run a batch processing job every night for 2 hours. They want to minimize costs while ensuring the job completes within a 4-hour window. Which pricing model should they choose?

Accepted Answer

Preemptible VM instances. Preemptible VM instances (B) are ideal for fault-tolerant, short-lived batch jobs that can handle interruptions. They offer significantly lower cost than on-demand instances, and because the job runs for only 2 hours within a 4-hour window, it can be restarted if preempted, ensuring completion at minimal expense.

Answer

On-demand pricing

Answer

Committed use discounts

Answer

Sustained use discounts

Question 8

A global enterprise must store customer data in specific geographic regions to comply with data residency laws. They also need to be able to analyze data across all regions without centralizing it. Which approach best balances compliance and analytics?

Accepted Answer

Use regional Cloud Storage buckets per region and BigQuery with federated queries.. Option C is correct because a multi-region deployment with BigQuery's federated queries allows querying data across regions without moving it. Option A is wrong because storing all data in a single region violates residency laws. Option B is wrong because using VMs in each region requires managing infrastructure and does not inherently provide cross-region analytics. Option D is wrong because Cloud Storage's multi-region bucket stores data in multiple regions, which may violate data residency if data is replicated outside the required region.

Answer

Deploy identical Compute Engine instances with databases in each region.

Answer

Store all data in a single region and use Cloud CDN for fast access.

Answer

Use a single Cloud Storage multi-region bucket with granular access controls.

Question 9

Which TWO of the following are key characteristics of cloud computing as defined by NIST? (Choose 2)

Accepted Answer

Measured service. Measured service is a key characteristic of cloud computing as defined by NIST (SP 800-145). It means that cloud systems automatically control and optimize resource usage by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). This allows both the provider and consumer to monitor, control, and report usage, providing transparency for billing and chargeback.

Answer

Virtualization

Answer

Multi-tenancy

Answer

Serverless computing

Question 10

A company is migrating its on-premises data center to Google Cloud. They want to avoid large upfront hardware costs and only pay for the resources they consume. Which cloud benefit does this represent?

Accepted Answer

Pay-as-you-go pricing. Option B is correct because the scenario describes a shift from capital expenditure (CapEx) to operational expenditure (OpEx), which is the core of pay-as-you-go pricing. This model allows the company to avoid large upfront hardware costs and only pay for the compute, storage, and network resources actually consumed, aligning with Google Cloud's consumption-based billing model.

Answer

Security

Answer

High availability

Answer

Scalability

Question 11

A small business runs a single Linux server on-premises for file storage and a simple static website. They experience frequent power outages causing server downtime, and they want to improve availability with minimal cost and management overhead. They have limited IT staff. What should they do?

Accepted Answer

Use Cloud Storage for the static website and Cloud Filestore for file storage. Option B is correct because Cloud Storage provides highly available static website hosting with automatic replication, and Cloud Filestore offers managed file storage, both requiring minimal management. Option A still has a single point of failure. Option C requires additional hardware and doesn't eliminate power issues. Option D adds cost without reducing management overhead.

Answer

Implement a load balancer across two on-premises servers

Answer

Migrate to a single Compute Engine VM in Google Cloud

Answer

Use a third-party colocation facility

Question 12

A company has a production database running on Cloud SQL. They need to perform maintenance on the primary instance without downtime. Which feature allows this?

Accepted Answer

Read replicas with failover. Read replicas with failover allow you to promote a read replica to a new primary instance in the event of a planned maintenance or failure, enabling zero-downtime operations. Cloud SQL uses this feature to redirect traffic seamlessly by updating the DNS record to point to the promoted replica, ensuring the database remains available during maintenance.

Answer

Database import/export

Answer

Multi-region deployment

Answer

Automated backups

Question 13

A mid-size company runs a batch processing application on a single on-premises server. The processing time varies; during month-end, the workload spikes and the server becomes overloaded, causing delays. The company wants a solution that automatically scales resources for peak times and only pays for extra capacity when used. They are considering migrating to Google Cloud. Which approach meets their needs?

Accepted Answer

Deploy the application on a managed instance group in Compute Engine with autoscaling. Option C is correct because Google Cloud offers autoscaling groups in Compute Engine that can add instances during peak load and remove them when demand drops, with pay-per-second billing. Option A lacks automatic scaling. Option B provides managed services but may not directly solve scaling of their specific application. Option D is for serverless functions, not ideal for a batch processing app that may run for hours.

Answer

Migrate the server to a single, larger Compute Engine instance with a static IP

Answer

Rewrite the batch processing application as a Cloud Function

Answer

Use Cloud SQL for the database and a single Compute Engine instance for processing

Question 14

A company runs a batch processing job every night that takes 6 hours on a fixed number of virtual machines. They want to reduce costs without increasing job duration. Which strategy should they use?

Accepted Answer

Use preemptible VMs. Option C is correct because preemptible VMs are up to 80% cheaper and suitable for fault-tolerant batch jobs. Option A is wrong because persistent disks add cost. Option B is wrong because more VMs increase cost. Option D is wrong because it is a reserved capacity commitment, which is for predictable workloads and not cheaper for short jobs.

Answer

Increase the number of VMs

Answer

Purchase committed use contracts

Answer

Use larger persistent disks

Question 15

Which TWO of the following are important considerations when designing a cloud solution for data residency compliance? (Choose exactly 2.)

Accepted Answer

Enabling Cloud Data Residency policies. Options A and D are correct. A is correct because data must stay in specific geographic boundaries. D is correct because Cloud Data Residency policies enforce location constraints. B is incorrect because storing data in a single zone does not guarantee residency; zones are within a region. C is incorrect because encryption does not change data location. E is incorrect because storage class affects availability, not residency.

Answer

Choosing the lowest-cost storage class

Answer

Using a single availability zone for all data

Answer

Encrypting all data at rest