Cloud Digital Leader Google Cloud products, services, and solutions Practice Test 3 — 15 Questions

Question 1

A data analyst at a media company needs to run complex SQL queries on petabytes of user engagement data to produce weekly reports. The dataset is stored in Google Cloud. Which Google Cloud product is purpose-built for this type of large-scale analytical SQL workload?

Accepted Answer

BigQuery, Google Cloud's serverless data warehouse for petabyte-scale analytical SQL. BigQuery is Google Cloud's serverless, highly scalable data warehouse specifically designed for petabyte-scale analytical SQL queries. It separates compute from storage and uses a columnar storage format and a distributed query engine to execute complex SQL on massive datasets without provisioning infrastructure, making it the ideal choice for the described workload.

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Cloud SQL, Google Cloud's managed relational database service

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Cloud Bigtable, Google's NoSQL wide-column database

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Firestore, Google Cloud's serverless NoSQL document database

Question 2

A software team deploys microservices using containers and wants Google Cloud to automatically manage the scaling, self-healing, and rollout of their containerized applications. They don't want to provision or manage underlying virtual machines. Which Google Cloud service best meets this need?

Accepted Answer

Google Kubernetes Engine (GKE), which manages containerized applications with automatic scaling, self-healing, and rolling updates. Google Kubernetes Engine (GKE) is the correct choice because it provides a managed Kubernetes environment that automates scaling, self-healing (e.g., automatic pod restarts and node repair), and rollout management (e.g., rolling updates and rollbacks) for containerized applications. This meets the requirement of not provisioning or managing underlying virtual machines, as GKE abstracts the node infrastructure.

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Compute Engine, Google Cloud's virtual machine service

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Cloud Storage, for storing container images and serving them to instances

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Cloud CDN, which distributes containerized applications globally

Question 3

A marketing team wants to create interactive dashboards and reports for business stakeholders using data stored in BigQuery, without writing code. Which Google Cloud product is most appropriate for this self-service business intelligence requirement?

Accepted Answer

Looker Studio, Google's self-service data visualization tool with native BigQuery connectivity and a no-code interface. Looker Studio (formerly Google Data Studio) is the correct choice because it is Google Cloud's self-service business intelligence tool that provides a no-code, drag-and-drop interface for creating interactive dashboards and reports. It has native, built-in connectivity to BigQuery, allowing the marketing team to visualize data without writing any code or SQL queries.

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Cloud Dataflow, Google Cloud's stream and batch data processing service

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BigQuery itself, where the marketing team writes SQL queries to generate reports

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Vertex AI, Google Cloud's unified machine learning platform

Question 4

A logistics company needs to send millions of shipment status updates per day from IoT tracking devices to backend systems for processing and storage. The solution must decouple the tracking devices from the backend and handle traffic spikes without losing messages. Which Google Cloud product best fits this asynchronous messaging requirement?

Accepted Answer

Cloud Pub/Sub, Google's fully managed messaging service that decouples producers from consumers and handles massive message volumes reliably. Cloud Pub/Sub is the correct choice because it is a fully managed, asynchronous messaging service designed to decouple producers (IoT devices) from consumers (backend systems). It can handle millions of messages per second, provides at-least-once delivery, and buffers messages during traffic spikes, ensuring no data loss without requiring the backend to be always available.

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Cloud SQL, to store each tracking update as a row in a relational database as it arrives

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Cloud Storage, to have each device upload a file containing its status update

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Cloud Load Balancing, to distribute incoming tracking requests evenly across backend servers

Question 5

A healthcare provider wants to use AI to analyze unstructured medical records — scanned documents with handwritten notes and printed text — to extract diagnosis codes for billing. Which combination of Google Cloud AI products most directly addresses this document understanding use case?

Accepted Answer

Document AI and Vision API, which together handle OCR, layout understanding, and information extraction from scanned documents with handwritten and printed text. Option B is correct because Document AI is purpose-built for extracting structured information (like diagnosis codes) from unstructured documents, including both handwritten and printed text, using OCR and layout understanding. The Vision API complements this by providing advanced OCR capabilities for scanned images, together forming a direct solution for the healthcare provider's document understanding use case.

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BigQuery ML and Looker Studio, to analyze and visualize the extracted diagnosis codes

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Vertex AI Pipelines and Cloud Dataflow, to orchestrate machine learning training jobs on document data

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Cloud Translation API and Natural Language API, to translate and analyze the text content of medical records

Question 6

A retail company needs to process financial transactions requiring strict ACID compliance, serve global customers with consistent low-latency reads and writes, and scale horizontally without downtime for maintenance. Which Google Cloud database service is uniquely designed to meet all three requirements simultaneously?

Accepted Answer

Cloud Spanner, Google's globally distributed relational database with ACID transactions and horizontal scalability. Cloud Spanner is the only Google Cloud database service that simultaneously provides ACID compliance across global transactions, consistent low-latency reads and writes via TrueTime and synchronous replication, and horizontal scaling without downtime through automatic sharding and resharding. It uniquely combines relational database semantics with global distribution, making it the correct choice for the given requirements.

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Cloud SQL with read replicas in multiple regions

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Firestore in Datastore mode with multi-region replication

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BigQuery with streaming inserts for real-time transaction processing

Question 7

A software team wants to host their container images securely within Google Cloud and integrate with Cloud Build for CI/CD pipelines and GKE for deployments. Which Google Cloud product serves as the managed repository for storing and managing container images?

Accepted Answer

Artifact Registry, Google Cloud's managed repository for container images and build artifacts with native CI/CD integration. Artifact Registry is Google Cloud's fully managed, next-generation repository for storing, managing, and securing container images and build artifacts. It natively integrates with Cloud Build for CI/CD pipelines and GKE for deployments, providing features like vulnerability scanning, access control via IAM, and support for multiple formats (Docker, Maven, npm, etc.). This makes it the correct choice for the team's requirements.

Answer

Cloud Storage, by storing container images as objects in a bucket

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Container Registry (gcr.io), the legacy Google container image service

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Cloud Source Repositories, for storing source code and container build files

Question 8

An enterprise wants to give its business intelligence team a governed analytics platform where data models are defined centrally with version-controlled semantic definitions, and all reports are guaranteed to use the same business metric definitions. Which Google Cloud product is designed for this governed BI use case?

Accepted Answer

Looker, with its LookML semantic layer for centrally governed, version-controlled metric definitions used consistently across all reports. Looker is the correct choice because it provides a governed analytics platform with its LookML semantic layer. LookML allows data models to be defined centrally with version control, ensuring that all reports and dashboards use the same business metric definitions consistently. This directly addresses the enterprise's need for a governed BI solution where semantic definitions are managed and versioned.

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Looker Studio, Google's free self-service data visualization tool

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BigQuery, where analysts write shared SQL queries stored in the project

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Vertex AI, which provides a model registry for governing ML model definitions

Question 9

A company wants to expose its internal backend services to external partners through a managed API layer that handles authentication, rate limiting, traffic management, and analytics — without modifying the underlying services. Which Google Cloud product is designed for this API management use case?

Accepted Answer

Apigee, Google Cloud's full-featured enterprise API management platform for authentication, rate limiting, analytics, and developer portal without modifying backend services. Apigee is Google Cloud's full-featured enterprise API management platform designed to expose backend services to external partners without requiring modifications to those services. It provides built-in authentication, rate limiting, traffic management, and analytics, along with a developer portal for partner onboarding. This makes it the correct choice for the described use case.

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Cloud Load Balancing, which distributes API requests across backend instances

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Cloud Endpoints, a lighter-weight API gateway for Google Cloud-hosted APIs

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Cloud Armor, Google Cloud's web application firewall and DDoS protection service

Question 10

A media company stores video files, images, and static website assets that must be served globally with low latency. They want an object storage solution that is highly durable, accessible via standard HTTPS, and can be configured to make specific assets publicly accessible. Which Google Cloud storage product is most appropriate?

Accepted Answer

Cloud Storage, Google Cloud's object storage for unstructured data accessible via HTTPS with configurable public access. Cloud Storage is Google Cloud's object storage service designed for unstructured data such as video files, images, and static website assets. It provides global accessibility via standard HTTPS, offers 99.999999999% (11 9's) durability, and supports granular access control through IAM and ACLs, allowing specific assets to be made publicly accessible. This directly matches all the requirements in the question.

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Filestore, Google Cloud's managed NFS file storage service

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Persistent Disk, Google Cloud's block storage for virtual machine instances

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Cloud SQL, Google Cloud's managed relational database service

Question 11

A startup needs to build a mobile application backend. They need user authentication, a real-time database that syncs to mobile clients automatically, and serverless functions for business logic — all managed with minimal backend engineering. Which Google Cloud platform provides this integrated mobile/web backend solution?

Accepted Answer

Firebase, which provides integrated authentication, auto-syncing Firestore database, serverless functions, and hosting — designed for mobile and web backends with minimal engineering overhead. Firebase is the correct answer because it is a Google Cloud platform specifically designed to provide an integrated mobile/web backend with minimal engineering overhead. It bundles user authentication, a real-time NoSQL database (Firestore) that automatically syncs data to clients, and serverless Cloud Functions for business logic, all managed without requiring backend infrastructure provisioning.

Answer

Google Kubernetes Engine (GKE), for containerized backend microservices

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BigQuery, for storing and analyzing mobile application user data

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Cloud Spanner, for globally consistent transactional data storage in the mobile backend

Question 12

A data engineering team needs to build a pipeline that reads event data from Pub/Sub in real time, applies transformations and aggregations, and writes results to BigQuery — all without managing any infrastructure. Which Google Cloud product is designed for this serverless stream and batch data processing use case?

Accepted Answer

Cloud Dataflow, Google Cloud's serverless stream and batch data processing service built on Apache Beam. Cloud Dataflow is Google Cloud's fully managed, serverless service for both stream and batch data processing, built on Apache Beam. It directly reads from Pub/Sub, applies transformations and aggregations using the Beam SDK, and writes the results to BigQuery without requiring any infrastructure management, making it the correct choice for this use case.

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Cloud Composer, Google Cloud's managed Apache Airflow service for workflow orchestration

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Cloud Dataproc, Google Cloud's managed Spark and Hadoop service

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BigQuery directly, using streaming inserts to load Pub/Sub data in real time

Question 13

A company wants to create a customer-facing conversational AI assistant that understands natural language and can answer questions about its products, integrated into their website and mobile app. Which Google Cloud AI product is the most appropriate starting point?

Accepted Answer

Dialogflow CX, Google Cloud's managed conversational AI platform for building natural language understanding chatbots integrated into web and mobile apps. Dialogflow CX is the correct choice because it is Google Cloud's managed conversational AI platform specifically designed for building natural language understanding (NLU) chatbots that can be integrated into websites and mobile apps. It provides advanced state management, flow-based conversation design, and seamless integration with web and mobile channels, making it the most appropriate starting point for a customer-facing conversational AI assistant.

Answer

BigQuery ML, for building machine learning models on customer data to predict product recommendations

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Cloud Vision API, for analyzing images to understand customer product photos

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Cloud Natural Language API, for analyzing the sentiment of customer product reviews

Question 14

A company needs to run containerized workloads but wants to avoid managing Kubernetes cluster infrastructure (node pools, cluster upgrades, node autoscaling) entirely. They want to simply deploy container images and have Google Cloud manage all underlying infrastructure automatically. Which Google Cloud product best fits this fully managed container execution requirement?

Accepted Answer

Cloud Run, which executes container images on fully managed serverless infrastructure with automatic scaling and no cluster or node management. Cloud Run is the correct choice because it provides a fully managed serverless platform that executes container images without any cluster or node management. It automatically handles scaling, infrastructure provisioning, and maintenance, aligning perfectly with the requirement to avoid managing Kubernetes cluster infrastructure entirely.

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Google Kubernetes Engine (GKE) Standard mode, where the team manages node pools and cluster configuration

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Compute Engine with a startup script that pulls and runs the container image from Artifact Registry

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App Engine Flexible, which runs containers on managed virtual machines

Question 15

A DevOps team wants to automate their software build, test, and deployment process on Google Cloud. They need a service that triggers automatically when code is pushed to a repository, builds container images, runs tests, and deploys to production. Which Google Cloud product orchestrates this CI/CD pipeline?

Accepted Answer

Cloud Build, Google Cloud's managed CI/CD service that triggers on code pushes, builds images, runs tests, and deploys automatically. Cloud Build is Google Cloud's managed CI/CD service that directly supports the described workflow: it can be triggered automatically by code pushes to a repository (e.g., Cloud Source Repositories, GitHub, Bitbucket), then execute a series of steps defined in a build configuration file (cloudbuild.yaml) to build container images, run tests, and deploy to production environments such as Google Kubernetes Engine, Cloud Run, or Compute Engine. This makes it the correct choice for orchestrating the entire CI/CD pipeline.

Answer

Cloud Composer, Google Cloud's managed Apache Airflow service

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Cloud Scheduler, which triggers periodic jobs on a cron schedule

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Eventarc, which routes events from Google Cloud services to Cloud Run functions