Cloud Digital Leader Google Cloud products, services, and solutions — Timed 10 Questions

Question 1

A data analytics team needs to analyze petabytes of structured data using SQL queries without managing any database infrastructure. Query results must return within seconds for most queries. Which Google Cloud service is designed for this use case?

Accepted Answer

BigQuery. BigQuery is a serverless, highly scalable data warehouse designed for analyzing petabytes of data using SQL without any infrastructure management. Its columnar storage and distributed query engine enable sub-second query performance on large datasets, making it ideal for this use case.

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Cloud SQL

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Cloud Bigtable

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Cloud Spanner

Question 2

A developer wants to deploy a containerized web application without managing servers, clusters, or Kubernetes configuration. The application should automatically scale to zero when not in use and handle bursts of traffic. Which Google Cloud service is the best fit?

Accepted Answer

Cloud Run. Cloud Run is the best fit because it is a fully managed serverless container platform that automatically scales to zero when idle and scales up to handle traffic bursts, without requiring any server, cluster, or Kubernetes configuration. The developer simply deploys a container image, and Cloud Run handles all infrastructure management, including scaling and load balancing.

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Google Kubernetes Engine (GKE)

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Compute Engine

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App Engine Standard

Question 3

A retail company wants to build a recommendation engine that suggests products to customers based on their browsing history. The team has ML expertise but wants to use Google's pre-built ML infrastructure to train and deploy models at scale without managing compute resources. Which Google Cloud service should they use?

Accepted Answer

Vertex AI. Vertex AI is the correct choice because it provides a fully managed, unified ML platform that handles the entire ML workflow—from data preparation and training to deployment and monitoring—without requiring the team to manage underlying compute infrastructure. It integrates with Google Cloud's pre-built ML infrastructure, including distributed training, AutoML, and custom model serving, making it ideal for building and scaling a recommendation engine.

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BigQuery ML

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Cloud AI Platform Notebooks (now Vertex AI Workbench)

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Cloud Dataflow

Question 4

A company needs to store large volumes of unstructured data (images, videos, backups, documents) with high durability and global accessibility. Which Google Cloud service is designed for object storage at any scale?

Accepted Answer

Cloud Storage. Cloud Storage is Google Cloud's fully managed, scalable object storage service designed for unstructured data such as images, videos, backups, and documents. It offers high durability (99.999999999% annual durability) and global accessibility via a unified namespace, making it the correct choice for storing large volumes of unstructured data at any scale.

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Persistent Disk

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Cloud Filestore

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Cloud Spanner

Question 5

A business intelligence team wants to create interactive dashboards and reports from their BigQuery data without writing code. They need to share reports with stakeholders who don't have GCP accounts. Which Google Cloud tool is most appropriate?

Accepted Answer

Looker Studio (formerly Data Studio). Looker Studio (formerly Data Studio) is the correct choice because it is a no-code, drag-and-drop business intelligence tool that connects directly to BigQuery, enabling the creation of interactive dashboards and reports. It also supports sharing reports via public links or embedded views, allowing stakeholders without GCP accounts to access them without needing IAM permissions.

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Vertex AI Workbench

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Cloud Dataprep by Trifacta

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BigQuery Studio

Question 6

A company wants to build an application that can understand and respond to natural language queries from customers (e.g., a customer support chatbot). Which Google Cloud capability should they use?

Accepted Answer

Dialogflow CX or Vertex AI Conversation. Dialogflow CX and Vertex AI Conversation are Google Cloud's purpose-built services for building conversational interfaces, including chatbots that understand natural language. They leverage natural language understanding (NLU) models to parse user intents and entities, enabling the application to respond appropriately to customer queries. This makes them the correct choice for a customer support chatbot.

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Cloud Vision API

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BigQuery ML

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Cloud Translation API

Question 7

A company needs to send messages between different microservices in a decoupled way. When one service publishes an event, multiple downstream services should receive and process it independently. Which Google Cloud service enables this publish-subscribe messaging pattern?

Accepted Answer

Cloud Pub/Sub. Cloud Pub/Sub is the correct choice because it is Google Cloud's fully managed, asynchronous messaging service designed specifically for the publish-subscribe pattern. It allows a publisher service to emit events to a topic, and multiple subscriber services can independently pull or push those messages from that topic, ensuring decoupled and reliable communication.

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Cloud Tasks

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Cloud Scheduler

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Eventarc

Question 8

A global fintech company needs a database that can handle financial transactions across 50+ countries with consistent, ACID-compliant operations, SQL queries, and automatic global replication with no downtime for maintenance. Which Google Cloud database service meets all these requirements?

Accepted Answer

Cloud Spanner. Cloud Spanner is the only Google Cloud database that provides ACID-compliant transactions, full SQL support, and automatic synchronous global replication with no downtime for maintenance. It is designed for horizontally scalable, globally distributed applications that require strong consistency across regions, making it the ideal choice for a fintech company operating in 50+ countries.

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Cloud SQL (PostgreSQL)

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Cloud Bigtable

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Firestore

Question 9

A team needs to process and analyze streaming data in real-time as it arrives from IoT sensors. The pipeline must apply transformations, filter events, and write results to BigQuery. Which Google Cloud service is designed for this stream processing use case?

Accepted Answer

Cloud Dataflow. Cloud Dataflow is the correct choice because it is a fully managed, serverless service designed specifically for stream and batch data processing. It uses Apache Beam as its programming model, enabling you to apply transformations, filter events, and write results to BigQuery in real-time, exactly matching the described pipeline requirements.

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Cloud Dataproc

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Cloud Composer

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BigQuery Streaming Insert

Question 10

A company wants to use pre-trained Google AI models to add vision capabilities to their application — specifically to detect objects in images and extract text from scanned documents — without training their own models. Which Google Cloud APIs provide these capabilities?

Accepted Answer

Cloud Vision API for object detection and OCR; Cloud Document AI for structured document extraction.. Option A is correct because Cloud Vision API provides pre-trained models for object detection and OCR (Optical Character Recognition) to extract text from images, while Cloud Document AI specializes in extracting structured data (e.g., fields, tables) from scanned documents. Both services require no custom training, aligning with the company's requirement to use pre-trained Google AI models.

Answer

BigQuery ML for both use cases — it trains vision models on image data stored in BigQuery.

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Vertex AI AutoML Vision — train a custom model on your own images.

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Cloud Natural Language API for text extraction from images.