Cloud Digital Leader Practice Test 26 — 15 Questions

Question 1

Refer to the exhibit. A team deployed this Cloud Run service. During a load test, the service receives high traffic, but the number of container instances never exceeds 10. What is the most likely cause?

Accepted Answer

The maxScale annotation limits the maximum number of instances to 10.. The `maxScale` annotation in Cloud Run directly caps the maximum number of container instances that can be created. When the service receives high traffic but never exceeds 10 instances, it indicates that the `maxScale` annotation is set to 10, preventing further scaling even if demand increases. This is the most direct and likely cause among the options.

Answer

The minScale of 2 forces at least two instances, but not the max.

Answer

The containerConcurrency of 80 limits the number of concurrent requests per instance.

Answer

The CPU limit of 1 vCPU is too low to handle the traffic.

Question 2

A company runs a web application on Google Kubernetes Engine (GKE) that experiences sudden traffic spikes. The operations team notices that the application's response time increases significantly during these spikes despite having Horizontal Pod Autoscaler (HPA) configured. They want to ensure consistent performance. What should they do?

Accepted Answer

Configure the HPA to use custom metrics based on request latency.. Option B is correct because configuring the HPA to use custom metrics based on request latency allows the autoscaler to react directly to the application's performance degradation. Unlike CPU-based metrics, which may not reflect actual user-facing latency during traffic spikes, custom metrics like request latency provide a more accurate signal for scaling decisions, ensuring consistent response times.

Answer

Increase the CPU request limit for all pods.

Answer

Create multiple node pools with different machine types.

Answer

Manually scale the deployment during expected spikes.

Question 3

A company wants to grant a data analyst read-only access to specific BigQuery datasets, but only if the request comes from within the corporate network. Which two Google Cloud tools should they combine to enforce this?

Accepted Answer

IAM and VPC Service Controls. IAM defines the read-only role (e.g., roles/bigquery.dataViewer) for the data analyst, while VPC Service Controls create a security perimeter that restricts access to the BigQuery API from only the corporate network IP range. Together, they ensure the request is both authorized by IAM and originates from within the allowed VPC perimeter, blocking any access from outside the corporate network even if the IAM role is granted.

Answer

IAM and Cloud IAP

Answer

Cloud Armor and IAM

Answer

Organization Policies and Cloud Audit Logs

Question 4

A company wants to innovate quickly by leveraging machine learning without building models from scratch. Which Google Cloud service allows them to use pre-trained models via APIs?

Accepted Answer

Cloud Vision API. Cloud Vision API is correct because it provides pre-trained machine learning models via REST APIs, allowing the company to integrate image recognition capabilities (e.g., label detection, OCR, face detection) without building or training any models. This directly meets the requirement of leveraging ML without building from scratch, as the API abstracts all model training and deployment.

Answer

BigQuery ML

Answer

AI Platform

Answer

AutoML

Question 5

A financial services company must comply with strict data residency regulations. Which TWO cloud features help meet compliance requirements?

Accepted Answer

Data location controls. Data location controls (Option A) allow administrators to explicitly restrict where data is stored and processed, ensuring compliance with data residency regulations that mandate data remain within specific geographic boundaries. Customer-managed encryption keys (Option E) enable the organization to control who has access to the encryption keys, ensuring that even if data is stored in a cloud provider's infrastructure, the provider cannot decrypt it without the customer's permission, which is critical for meeting regulatory requirements.

Answer

Open source software

Answer

Public internet access

Answer

Single data center footprint

Question 6

A startup is building a web application and wants to avoid managing servers or operating systems. They need to deploy code quickly and scale automatically. Which cloud service model best meets these requirements?

Accepted Answer

Platform as a Service (PaaS). Platform as a Service (PaaS) is the correct choice because it abstracts the underlying infrastructure (servers, OS, storage) while providing a managed platform for deploying and scaling web applications. The startup can focus on writing code and pushing updates without managing operating systems or servers, and PaaS platforms like Google App Engine or AWS Elastic Beanstalk offer automatic scaling based on demand.

Answer

Function as a Service (FaaS)

Answer

Software as a Service (SaaS)

Answer

Infrastructure as a Service (IaaS)

Question 7

A developer needs to run a custom analysis script on a large dataset once a month. The script runs for about 10 minutes. They want to avoid provisioning servers and only pay for the actual compute time used. Which Google Cloud compute option should they choose?

Accepted Answer

Cloud Functions. Cloud Functions is the correct choice because it is a serverless, event-driven compute service that automatically scales to zero when not in use, charging only for the actual compute time consumed during execution. The 10-minute monthly script fits within Cloud Functions' 9-minute maximum timeout (recently extended to 60 minutes for HTTP-triggered functions in some regions), making it ideal for infrequent, short-lived tasks without provisioning servers.

Answer

App Engine Standard Environment

Answer

Compute Engine preemptible VM

Answer

Google Kubernetes Engine with a single pod

Question 8

A startup needs to quickly deploy a web application with minimal infrastructure management. They want to focus on code, not servers. Which Google Cloud service model is most appropriate?

Accepted Answer

Platform as a Service (PaaS) using App Engine. App Engine is a fully managed Platform as a Service (PaaS) that abstracts away the underlying infrastructure, allowing developers to deploy web applications without managing servers or operating systems. It automatically handles scaling, load balancing, and patching, which aligns with the startup's requirement to focus on code rather than infrastructure management.

Answer

Software as a Service (SaaS) using Gmail

Answer

Function as a Service (FaaS) using Cloud Functions

Answer

Infrastructure as a Service (IaaS) using Compute Engine

Question 9

A small business wants to host a static website with minimal management overhead. They want high availability and low cost. Which Google Cloud service should they use?

Accepted Answer

Cloud Storage with a load balancer. Cloud Storage with a load balancer is ideal for hosting a static website because it serves content directly from object storage, requires no server management, and the load balancer provides high availability by distributing traffic across multiple regions. This combination offers low cost (pay only for storage and egress) and minimal operational overhead, as there are no virtual machines or application runtimes to maintain.

Answer

Compute Engine with Apache

Answer

App Engine Standard Environment

Answer

Cloud Run

Question 10

A data analytics team needs to run a one-time transformation on 10 TB of data stored in Cloud Storage, then load the results into BigQuery. The transformation is a custom Java application that reads files, processes them, and writes to a new location. Which service should they use to minimize operational overhead?

Accepted Answer

Dataproc Serverless with Spark job. Option C (Dataproc Serverless with Spark job) is correct because it provides a fully managed, serverless execution environment for running custom Java transformations on large datasets (10 TB) without provisioning or managing clusters. Dataproc Serverless automatically scales resources based on the job's needs, minimizing operational overhead while supporting Spark jobs that can read from Cloud Storage and write results to BigQuery.

Answer

Cloud Dataflow with Apache Beam Java SDK

Answer

Google Kubernetes Engine (GKE) with a custom container

Answer

Cloud Functions triggered by Cloud Storage events

Question 11

Which TWO of the following are common use cases for deploying workloads to the cloud? (Choose 2)

Accepted Answer

Running a global SaaS application for customers worldwide. Option D is correct because cloud platforms like AWS, Azure, and GCP provide global infrastructure with multiple regions and edge locations, enabling SaaS applications to serve customers worldwide with low latency via CDNs and load balancers. Option E is correct because cloud storage services (e.g., Amazon S3, Azure Blob Storage) offer durable, cost-effective off-site backup and automated disaster recovery through replication across Availability Zones or Regions, often with 99.999999999% durability.

Answer

High-frequency trading requiring microsecond latency

Answer

Air-gapped, classified workloads with no internet connection

Answer

Running a legacy mainframe application that requires dedicated hardware

Question 12

Which THREE of the following are common benefits of adopting a cloud infrastructure compared to on-premises? (Choose 3)

Accepted Answer

Ability to scale globally in minutes. Option A is correct because cloud infrastructure enables rapid global scaling by provisioning resources across multiple geographic regions within minutes, leveraging automated orchestration and APIs. This is a fundamental advantage over on-premises setups, which require lengthy procurement, shipping, and manual configuration to expand capacity.

Answer

Elimination of all security vulnerabilities

Answer

Complete transfer of security responsibility to the provider

Question 13

A large enterprise is evaluating moving its data analytics workloads to Google Cloud. Which TWO factors should they consider when comparing on-premises costs to cloud costs? (Choose two.)

Accepted Answer

Elimination of hardware maintenance labor costs. Option A is correct because moving analytics workloads to Google Cloud eliminates the need for the enterprise to maintain its own hardware, including servers, storage, and networking equipment. This removes direct labor costs for tasks such as hardware troubleshooting, firmware updates, and physical repairs, which are instead handled by Google's infrastructure teams. This is a direct operational expenditure (OpEx) saving compared to the capital expenditure (CapEx) and ongoing maintenance of on-premises hardware.

Answer

Increased latency due to network distance

Answer

Licensing costs for software that may not be compatible with cloud

Answer

Requirement to redesign applications for cloud-native services

Question 14

Refer to the exhibit. A data processing job must complete within 2 hours. The job can be interrupted but must resume from the last checkpoint. Which two instances should be used to minimize cost while meeting the requirement?

Accepted Answer

worker-2 and worker-4. Worker-2 and worker-4 are preemptible (spot) instances, which are significantly cheaper than standard instances. The job can be interrupted and resume from the last checkpoint, so preemptible instances are suitable. This combination minimizes cost while meeting the 2-hour completion requirement because preemptible instances have a maximum runtime of 24 hours and can be terminated earlier, but the checkpointing allows the job to complete within the 2-hour window even if interrupted.

Answer

worker-1 and worker-2

Answer

worker-2 and worker-3

Answer

worker-1 and worker-3

Question 15

A company wants to ensure that no employee can accidentally delete critical data stored in Cloud Storage. They need a solution that protects against accidental deletion even by users with full permissions. Which approach should they use?

Accepted Answer

Enable Object Versioning and set a retention policy. Object Versioning in Cloud Storage preserves non-current object versions, allowing recovery from accidental overwrites or deletions. A retention policy prevents object deletion (including version deletion) until the retention period expires, even for users with full permissions like `storage.objects.delete`. Together, they provide a defense-in-depth against accidental data loss.

Answer

Use customer-managed encryption keys

Answer

Set up Cloud Audit Logs for data access

Answer

Restrict IAM roles to Viewer only

Cloud Digital Leader Practice Test 26 — 15 Questions

Exhibit

Cloud Digital Leader Practice Test 26 — 15 Questions

Exhibit