Cloud Digital Leader Google Cloud products, services, and solutions Practice Test 5 — 15 Questions

Question 1

A startup's web application is being targeted by a denial-of-service attack that is flooding its servers with millions of fake requests per second. Which Google Cloud product provides automatic DDoS protection for the application?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Cloud Armor, which provides DDoS protection and WAF capabilities to detect and mitigate volumetric attacks against the application. Cloud Armor is the correct answer because it is Google Cloud's managed DDoS protection and Web Application Firewall (WAF) service. It uses Google's global infrastructure to absorb and filter volumetric attacks (e.g., SYN floods, UDP reflection attacks) at the edge, before traffic reaches the application. It integrates with Cloud Load Balancing to inspect and drop malicious requests based on pre-configured or adaptive rules.

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Cloud Storage, by distributing the static content of the application across multiple storage regions

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Cloud IAM, by revoking permissions for the IP addresses generating attack traffic

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Cloud Monitoring, by alerting the team so they can manually scale up servers to absorb the attack

Question 2

A developer needs to store application secrets (database passwords, API keys, OAuth tokens) securely so that they are not hardcoded in source code or environment variables. The secrets should be automatically versioned and rotatable. Which Google Cloud product is designed for this secure secrets management requirement?

Accepted Answer

Secret Manager, which stores secrets encrypted at rest with automatic versioning, rotation support, and fine-grained IAM access control. Secret Manager is Google Cloud's dedicated service for storing application secrets such as database passwords, API keys, and OAuth tokens. It provides encryption at rest using AES-256, automatic versioning (each new secret version is immutable and timestamped), and built-in rotation support via scheduled rotation policies. Fine-grained IAM roles (e.g., roles/secretmanager.secretAccessor) ensure least-privilege access without exposing secrets in source code or environment variables.

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Cloud Storage, by storing secrets in an encrypted bucket with restricted IAM access

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Cloud KMS, which generates encryption keys for encrypting application data

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Cloud SQL, by storing secrets in an encrypted database table with restricted access

Question 3

A company's analytics team wants to enable business users to create their own reports and dashboards from a governed set of BigQuery data, without writing SQL. At the same time, the data engineering team must maintain centralized control over how key metrics (like 'revenue' or 'active users') are defined. Which Google Cloud product architecture best meets both requirements?

Accepted Answer

Looker with LookML semantic layer: data engineers centrally govern metric definitions in LookML, business users create self-service reports through Looker's interface using those governed definitions — no SQL required. Looker with LookML provides a semantic layer where data engineers centrally define governed metric definitions (e.g., 'revenue' as SUM(price * quantity) with specific filters). Business users can then create self-service reports and dashboards via Looker's drag-and-drop interface without writing SQL, ensuring consistency and control over key metrics.

Answer

Looker Studio connected directly to BigQuery, allowing each business user to create their own metric definitions

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Sharing BigQuery query templates with business users and training them to modify them for their reports

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Building a custom web application that wraps BigQuery APIs and presents data to business users

Question 4

A company is building a new application that needs to send transactional emails (order confirmations, password resets, account notifications) to customers. Google Cloud does not provide a native SMTP email service. Which approach is standard for sending transactional emails from Google Cloud applications?

Accepted Answer

Integrating a third-party transactional email service (such as SendGrid, Mailgun, or Postmark) via API from the Cloud Run or Cloud Functions application. Google Cloud does not offer a native SMTP service for sending transactional emails. The standard approach is to integrate a third-party transactional email service (e.g., SendGrid, Mailgun, Postmark) via API from serverless compute services like Cloud Run or Cloud Functions. These services handle deliverability, reputation, and compliance with email standards (SPF, DKIM, DMARC) that are critical for transactional email.

Answer

Using Cloud Storage to store email templates and delivering them directly to customers' inboxes

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Running a self-managed SMTP server on a Compute Engine VM and configuring MX records to deliver email

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Using Gmail directly by authenticating the application with a corporate Gmail account and sending through Gmail SMTP

Question 5

A healthcare company needs to run a large batch processing job that analyzes patient records using Apache Spark, transforming data from Cloud Storage and writing results to BigQuery. The job runs once daily and requires a large cluster that should exist only during the job. Which Google Cloud product best handles this ephemeral large-batch Spark workload?

Accepted Answer

Cloud Dataproc, which runs managed Apache Spark clusters that can be created for the job and deleted on completion — paying only during the processing window. Cloud Dataproc is the correct choice because it provides managed Apache Spark clusters that can be created on demand for the batch job and automatically deleted upon completion, ensuring you only pay for the processing time. This ephemeral cluster model perfectly matches the requirement of a large cluster that exists only during the daily job, without manual infrastructure management.

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Cloud Dataflow, for running the Apache Spark code as a streaming pipeline

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Compute Engine VMs, by manually installing Apache Spark on a cluster of VMs each day before the job

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BigQuery, by running the Spark transformation directly within BigQuery's execution engine

Question 6

A company's DevOps team wants to orchestrate a complex workflow that involves calling multiple Google Cloud APIs in sequence — first running a Cloud Build job, then checking the results, then either deploying to Cloud Run or sending a notification. Which Google Cloud product is designed for orchestrating multi-step workflow logic?

Accepted Answer

Google Cloud Workflows, which orchestrates multi-step processes by calling APIs in sequence with conditional logic, error handling, and state management. Google Cloud Workflows is the correct choice because it is a fully managed orchestration platform specifically designed to define multi-step workflows that call Google Cloud APIs and external services in sequence. It supports conditional logic (e.g., if-then-else), error handling (e.g., retries with exponential backoff), and state management, making it ideal for the described scenario of running a Cloud Build job, checking results, and conditionally deploying to Cloud Run or sending a notification.

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Cloud Scheduler, which triggers a series of jobs at specified cron intervals

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Cloud Pub/Sub, by publishing messages between pipeline stages to trigger each subsequent step

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Cloud Run, by writing the orchestration logic as a container application that calls other services sequentially

Question 7

A financial services company needs a managed data warehouse that can ingest streaming transaction data in real time AND support complex SQL analytics across years of historical data — all without managing any infrastructure. Which Google Cloud product meets both streaming ingest and analytical query requirements in a single serverless service?

Accepted Answer

BigQuery, which supports real-time streaming ingest via its Storage Write API and large-scale analytical SQL queries across petabytes of data in a single fully managed, serverless service. BigQuery is a fully managed, serverless data warehouse that supports real-time streaming ingest via the Storage Write API and enables complex SQL analytics across petabytes of historical data. This single service meets both requirements without any infrastructure management, unlike the other options that require separate services or manual orchestration.

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Cloud Bigtable for streaming ingest and BigQuery for historical analytics — two separate services

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Cloud SQL with read replicas — one instance for streaming writes, read replicas for analytical queries

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Cloud Dataflow running continuously to process the stream and load to Persistent Disk for SQL queries

Question 8

A startup is building an application that sends daily promotional push notifications to millions of mobile users on both iOS and Android devices. Which Google Cloud or Google service most directly provides the infrastructure for sending these mobile push notifications?

Accepted Answer

Firebase Cloud Messaging (FCM), which delivers push notifications to iOS and Android devices through Google's mobile notification infrastructure. Firebase Cloud Messaging (FCM) is Google's dedicated, scalable infrastructure for delivering push notifications to both iOS and Android devices. It handles device registration, message routing, and platform-specific delivery through Apple Push Notification service (APNs) for iOS and Google's own push service for Android, making it the most direct and appropriate service for this use case.

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Cloud Pub/Sub, which delivers messages to subscribed mobile application instances

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Cloud Storage, by writing notification content to buckets that mobile applications poll for new messages

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Cloud Run, by exposing an API that mobile applications call to retrieve their pending notifications

Question 9

A company wants to connect its on-premises data center to Google Cloud with a reliable, lower-latency connection that doesn't traverse the public internet, but doesn't need the bandwidth of a full Dedicated Interconnect. Which Google Cloud connectivity product is most appropriate?

Accepted Answer

Partner Interconnect, which provides private connectivity through a service provider partner's network — supporting lower bandwidth tiers without requiring a direct physical fiber connection. Partner Interconnect is the correct choice because it provides private connectivity between an on-premises data center and Google Cloud via a service provider partner's network, offering lower bandwidth tiers (e.g., 50 Mbps to 10 Gbps) without requiring a direct physical fiber connection. This meets the requirements for a reliable, lower-latency connection that avoids the public internet, while Dedicated Interconnect would be overkill for bandwidth needs below 10 Gbps.

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Cloud VPN, which creates an encrypted IPsec tunnel over the public internet

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Dedicated Interconnect, which requires provisioning a 10 Gbps or 100 Gbps dedicated physical fiber connection

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Cloud CDN, which caches content at edge locations close to the on-premises data center

Question 10

A startup's website becomes unexpectedly popular and traffic spikes 50x within minutes. The application is hosted on Google Cloud. Which Google Cloud product automatically increases the number of application instances in response to this traffic spike without manual intervention?

Accepted Answer

Managed Instance Groups with autoscaling (for VMs) or Cloud Run (for containers), which automatically provision additional instances based on traffic load without manual intervention. Managed Instance Groups (MIGs) with autoscaling and Cloud Run both automatically adjust the number of running instances or container replicas based on real-time metrics like CPU utilization, request rate, or latency. When traffic spikes 50x, the autoscaler detects the increased load and provisions new VMs or container instances without any manual intervention, ensuring the application remains responsive. This is the only option that provides automatic, infrastructure-level scaling in response to load.

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Cloud Monitoring, which detects the traffic spike and sends an alert to the operations team to manually scale up

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Cloud Load Balancing, which distributes traffic evenly across existing instances to handle the spike

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Cloud Billing, which automatically increases the spending limit when traffic spikes occur

Question 11

A company runs a mission-critical PostgreSQL database on Google Cloud that must support automatic failover to a standby instance within 60 seconds if the primary instance fails, with minimal data loss. Which Cloud SQL configuration satisfies this high availability requirement?

Accepted Answer

Cloud SQL High Availability configuration with a synchronously replicated standby instance that automatically promotes to primary within approximately 60 seconds of primary failure. Cloud SQL's High Availability (HA) configuration uses a synchronous replication mechanism between the primary and standby instances. This ensures that transactions are committed on both instances before being acknowledged, meeting the requirement for minimal data loss. In the event of a primary failure, the standby is automatically promoted to primary within approximately 60 seconds, satisfying the failover time requirement.

Answer

Cloud SQL with automated daily backups, restoring from backup if the primary fails

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Cloud SQL read replicas in another region, manually promoted if the primary fails

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Running a self-managed PostgreSQL cluster on Compute Engine VMs with a custom pacemaker/corosync HA setup

Question 12

A company wants to migrate its existing on-premises virtual machines (VMware VMs) to Google Cloud with minimal changes to the operating system and applications. Which Google Cloud product is specifically designed for migrating on-premises VMs to Google Cloud with minimal modification?

Accepted Answer

Migrate to Virtual Machines (formerly Velostrata), which migrates on-premises VMware VMs to Compute Engine with minimal modification and downtime. Migrate to Virtual Machines (formerly Velostrata) is the correct choice because it is specifically designed to migrate on-premises VMware VMs to Compute Engine with minimal modification to the OS and applications. It uses a streaming migration approach that moves the VM's disk state incrementally while keeping the VM running, resulting in minimal downtime and no need to re-architect the workloads.

Answer

Cloud Dataflow, by streaming data from on-premises VMs to Google Cloud storage

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Anthos, by registering on-premises Kubernetes clusters with Google Cloud's management plane

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Cloud Storage Transfer Service, by copying VM disk images from on-premises storage to Cloud Storage

Question 13

Drag and drop the steps to deploy a containerized application to Google Kubernetes Engine (GKE) into the correct order.

Accepted Answer

Build the Docker container image, push it to Container Registry, create a GKE cluster, apply a Deployment manifest using kubectl, then expose the Deployment as a Service.. The correct sequence starts with containerizing the app, creating a cluster, defining the deployment, applying it, and exposing it as a service.

Answer

Create a GKE cluster, build the Docker container image, push it to Container Registry, apply a Deployment manifest, then expose as a Service.

Answer

Build the Docker container image, create a GKE cluster, apply a Deployment manifest, push the image to Container Registry, then expose as a Service.

Answer

Build the Docker container image, push it to Container Registry, create a GKE cluster, expose as a Service, then apply a Deployment manifest.

Question 14

Drag and drop the steps to set up a Cloud CDN for a backend bucket in the correct order.

Accepted Answer

Create and prepare the bucket, then set up a load balancer, then enable Cloud CDN, then test.. First create and prepare the bucket, then set up a load balancer, enable CDN, and test.

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Create and prepare the bucket, then enable Cloud CDN, then set up a load balancer, then test.

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Set up a load balancer, then create and prepare the bucket, then enable Cloud CDN, then test.

Answer

Create and prepare the bucket, then set up a load balancer, then test, then enable Cloud CDN.

Question 15

Match each Google Cloud AI/ML term to its definition.

Accepted Answer

Vertex AI: A unified platform for AI/ML model development and deployment.. Vertex AI is the unified platform; AutoML automates model training; Cloud TPUs accelerate computation. Common confusions include swapping these definitions.

Answer

Vertex AI: A suite of tools to train high-quality models with minimal ML expertise.

Answer

Cloud TPU: A unified platform for AI/ML model development and deployment.