GCP Questions for Freshers:

Q1. What is Google Cloud Platform (GCP) and its core services?
Ans: Google Cloud Platform (GCP) is a suite of cloud computing services provided by Google that runs on the same infrastructure that Google uses internally for its end-user products. It offers various cloud services that allow businesses to build, host, and scale applications and services on Google’s infrastructure.

Core Services of GCP:

• Compute Engine: Provides virtual machines (VMs) that run on Google’s infrastructure.
• App Engine: A fully managed serverless application platform for building and deploying applications
• Kubernetes Engine (GKE): Managed Kubernetes service for orchestrating containerized applications.
• Cloud Storage: Object storage service for storing and retrieving any amount of data.
• BigQuery: Serverless, highly scalable, and cost-effective multi-cloud data warehouse.
• Cloud Pub/Sub: Messaging service for building event-driven systems.
• Cloud SQL: Fully managed relational database service.
• Cloud Functions: Serverless functions that respond to events without provisioning or managing servers.

Example Code (Google Cloud Storage):

from google.cloud import storage

# Instantiates a client
storage_client = storage.Client()

# The name for the new bucket
bucket_name = "your-unique-bucket-name"

# Creates the new bucket
bucket = storage_client.create_bucket(bucket_name)

print("Bucket {} created.".format(bucket.name))

Q2. Explain the difference between GCP and traditional hosting.
Ans: Traditional hosting involves owning and maintaining physical servers, while GCP offers virtualized infrastructure, allowing users to rent resources. Here are the main differences:

• Scalability: GCP provides dynamic scalability, allowing resources to scale up or down based on demand, whereas traditional hosting requires manual scaling.
• Pricing: GCP follows a pay-as-you-go model, ensuring you pay only for what you use. Traditional hosting often involves fixed costs, regardless of resource usage.
• Flexibility: GCP offers a wide range of services and tools, allowing flexibility and customization, while traditional hosting might have limitations in terms of available software and configurations.
• Global Reach: GCP has a vast network of data centers worldwide, enabling global reach and low-latency access. Traditional hosting might be limited to specific geographic locations.

Q3. What is Google Compute Engine, and how does it work?
Ans: Google Compute Engine (GCE) is a part of GCP, offering virtual machine instances for compute workloads. Users can create and run virtual machines on Google’s infrastructure.

GCE works by allowing users to choose a pre-defined machine type or create custom machine types based on their requirements. Users can configure the VMs, install software, and run applications. GCE instances can be managed programmatically via the GCP Console, CLI, or API.

Q4. What are Google Cloud Storage buckets and their use cases?
Ans: Google Cloud Storage buckets are containers for storing data objects in GCP. Buckets can store any kind of data, including text files, images, videos, and application binaries. Use cases include:

• Storing backup data
• Hosting static assets for websites
• Distributing large data files or datasets
• Storing user-generated content in applications

Q5. Define Google App Engine and its advantages.
Ans: Google App Engine is a serverless platform for building and hosting applications. Advantages include:

• Automatic Scaling: Scales based on incoming traffic without manual intervention.
• Fully Managed: Google handles underlying infrastructure, allowing developers to focus on code.
• Support for Multiple Languages: Supports various programming languages including Python, Java, Go, and more.
• Integrated Services: Easily integrates with other GCP services for database, storage, and messaging needs.

Q6. What is Google Kubernetes Engine (GKE) and its benefits?
Ans: Google Kubernetes Engine (GKE) is a managed Kubernetes service for orchestrating containerized applications. Benefits include:

• Scalability: Automatically scales containerized applications based on demand.
• Managed Service: Google handles cluster management tasks like upgrading, patching, and monitoring.
• Hybrid and Multi-Cloud: Supports hybrid and multi-cloud deployments, providing flexibility in infrastructure choices.

Q7. Explain the concept of Virtual Private Cloud (VPC) in GCP.
Ans: Virtual Private Cloud (VPC) is a private network hosted on GCP’s infrastructure. It allows users to connect GCP resources securely. Users can define IP address ranges, configure firewall rules, and use VPNs for secure communication within the VPC. VPCs are isolated from each other, providing network security and segmentation.

Q8. What are Google Cloud Functions, and how are they used?
Ans: Google Cloud Functions are serverless functions that execute in response to events. They are used for event-driven applications, where specific functions are triggered by events such as file uploads, database changes, or HTTP requests. Cloud Functions automatically scale based on the number of incoming events.

Q9. Define Cloud Identity and Access Management (IAM) in GCP.
Ans: Cloud Identity and Access Management (IAM) is a service for managing access control to GCP resources. IAM allows administrators to assign specific roles to users or groups, controlling their permissions. IAM ensures secure access to resources while allowing fine-grained control over who can do what in GCP projects.

Q10. What is Google Cloud SQL, and how does it differ from Bigtable?
Ans: Google Cloud SQL is a managed relational database service for MySQL, PostgreSQL, and SQL Server. It offers features like automatic backups and scaling.

Bigtable, on the other hand, is a NoSQL wide-column database service designed for large analytical and operational workloads. It’s ideal for storing large amounts of single-keyed data with low-latency access.

Q11. Explain the role of Stackdriver in GCP monitoring.

Ans: Stackdriver is GCP’s monitoring and logging service. It provides powerful tools for monitoring, logging, and diagnostics. It helps track application performance, set up alerts based on metrics, and gain insights into application behavior. It integrates with various GCP services and supports multi-cloud environments.

Q12. What is Pub/Sub in Google Cloud Platform?
Ans: Pub/Sub is a messaging service for building event-driven systems. It enables communication between independently written applications. Publishers send messages to topics, and subscribers receive messages from topics. It provides reliable, many-to-many, asynchronous messaging.

Q13. How does Google Cloud Load Balancing work?
Ans: Google Cloud Load Balancing distributes incoming network traffic across multiple instances to ensure no single server is overwhelmed. It can balance traffic across multiple regions or global locations. There are different load balancer types, including HTTP(S) Load Balancing and Network Load Balancing, catering to specific use cases.

Q14. Define Google Cloud Pub/Sub and its use cases.
Ans: Google Cloud Pub/Sub is a messaging service that allows applications to send and receive messages between independent components. Use cases include real-time analytics, event-driven applications, and decoupling of microservices architectures.

Q15. Explain the concept of managed services in GCP.
Ans: Managed services in GCP refer to services where Google handles operational aspects like patching, monitoring, and scaling. Users only need to focus on using the service, not managing the underlying infrastructure. Examples include Cloud SQL, App Engine, and Cloud Functions.

Q16. What are the advantages of using Google Cloud Spanner?
Ans: Google Cloud Spanner is a globally distributed, strongly consistent database service. Advantages include:

• Horizontal Scalability: Scales both storage and compute resources dynamically.
• Global Distribution: Provides low-latency access to data from anywhere in the world.
• ACID Transactions: Supports ACID transactions across global locations, ensuring data consistency.

Q17. What is Identity-Aware Proxy (IAP) in GCP?
Ans: Identity-Aware Proxy (IAP) is a security feature that controls access to your cloud applications by verifying user identities. It helps protect against identity theft and phishing attacks by ensuring only authenticated users can access applications and services.

Q18. Explain the role of Cloud Firestore in GCP.
Ans: Cloud Firestore is a NoSQL document database for building real-time applications. It offers seamless synchronization and data access for web, mobile, and server development. Firestore’s real-time updates and offline support make it ideal for applications requiring responsive user interfaces and offline capabilities.

Q19. What is BigQuery, and how is it used for data analysis?
Ans: BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse. It allows users to analyze large datasets in real-time using SQL-like queries. BigQuery integrates with various data visualization tools and machine learning frameworks, enabling advanced data analysis and insights generation.

Q20. How does Google Cloud Storage manage data durability?
Ans: Google Cloud Storage ensures data durability through data replication. When data is uploaded to a bucket, it is automatically replicated across multiple locations within a region. Additionally, users can enable versioning and object lifecycle management policies to further enhance data durability and manage data retention policies.

Q21. What is Cloud Deployment Manager in GCP?
Ans: Cloud Deployment Manager allows users to define their cloud resources and configurations in a template file and deploy them in an automated and repeatable manner. It helps manage complex cloud deployments by providing version control and modular configurations, ensuring consistency across environments.

Q22. Explain Google Cloud CDN and its benefits.
Ans: Google Cloud CDN is a content delivery network that caches content at Google’s globally distributed edge points of presence. It accelerates content delivery, improves website performance, and reduces latency for end-users. Benefits include fast content delivery, DDoS mitigation, and seamless integration with GCP services.

Q23. What are Custom Machine Types in GCP?
Ans: Custom Machine Types allow users to create virtual machines with custom configurations, specifying the exact amount of vCPUs and memory required. This flexibility ensures optimal resource utilization, allowing users to meet specific application requirements without over-provisioning resources.

Q24. Define Google Cloud Armor and its security features.
Ans: Google Cloud Armor is a DDoS and application defense service that provides defense against infrastructure and application-layer DDoS attacks. It offers features like IP blacklisting, geolocation-based access control, and integration with Google Cloud Load Balancing to protect applications from various security threats.

Q25. How does Google Cloud Interconnect enhance network connectivity?
Ans: Google Cloud Interconnect enables users to establish private and direct connections to Google’s network. It enhances network connectivity by providing dedicated, low-latency connections to GCP resources. Interconnect options include Dedicated Interconnect for high-capacity connections and Partner Interconnect for smaller bandwidth requirements.

GCP Questions For Experienced:

Q26. How do you design a highly available architecture in GCP?
Ans: Designing a highly available architecture in GCP involves implementing redundancy, load balancing, and failover mechanisms across multiple regions and zones. Key strategies include:

• Multi-Region Deployment: Deploy resources across multiple regions to ensure availability in case of regional failures.
• Load Balancing: Use Google Cloud Load Balancers to distribute traffic across multiple instances and regions, ensuring no single server is overwhelmed.
• Automated Failover: Implement automated failover mechanisms using services like Google Cloud DNS and Stackdriver Monitoring to detect failures and redirect traffic to healthy instances.
• Data Replication: Use managed services like Cloud SQL, Spanner, and Bigtable, which offer built-in replication and failover capabilities for data durability and availability.

Q27. Explain the use of Google Cloud Endpoints in API development.
Ans: Google Cloud Endpoints is a framework for building, deploying, and managing APIs on GCP. It allows developers to create APIs, generate client libraries, and enable authentication and authorization. Endpoints provide features like API key validation, OAuth 2.0 authentication, and quota management, ensuring secure and scalable API development.

Example Code (Google Cloud Endpoints):

# Define an API endpoint
@endpoints.api(name='myapi', version='v1')
class MyApi(remote.Service):
    @endpoints.method(MyRequest, MyResponse, path='resource', http_method='POST', name='resource.insert')
    def insert_resource(self, request):
        # Process the request and return a response
        # ...
        return MyResponse(message='Resource created successfully!')

# Deploy the API to Google Cloud Endpoints
api_service = endpoints.api_server([MyApi])

Q28. What are the best practices for securing data in Google Cloud Storage?
Ans: Securing data in Google Cloud Storage involves implementing best practices such as:

• Access Control: Use IAM roles and permissions to control access to buckets and objects. Avoid granting overly permissive roles.
• Encryption: Enable server-side encryption to encrypt data at rest. Use customer-supplied encryption keys (CSEK) for additional control over encryption keys.
• Signed URLs and Policy Documents: Use signed URLs and policy documents to grant temporary access to objects, limiting the exposure of sensitive data.
• Logging and Monitoring: Enable Cloud Storage access logs and monitor bucket activity using Stackdriver Logging and Monitoring to detect and respond to suspicious behavior.

Q29. Describe the process of setting up a VPN in Google Cloud Platform.
Ans: Setting up a VPN in GCP involves the following steps:

1. Create a Cloud VPN Gateway: Create a Cloud VPN gateway specifying the public IP address and other configuration details.
2. Configure the Local VPN Device: Configure the on-premises VPN device with the necessary settings, including the public IP of the Cloud VPN gateway, shared secret, and encryption settings.
3. Configure Traffic Selection: Define the traffic selectors on both ends to specify the IP ranges allowed to traverse the VPN tunnel.
4. Establish VPN Connection: Once configurations are in place, initiate the VPN connection from the on-premises device. GCP will establish the connection automatically.
5. Monitor and Troubleshoot: Monitor the VPN connection status using the GCP Console or Cloud SDK. Use Stackdriver Logging and Monitoring to troubleshoot connectivity issues if necessary.

Q30. How do you optimize costs in GCP architecture? Provide examples.
Ans: Optimizing costs in GCP involves several strategies:

• Rightsize Resources: Choose appropriate machine types and configurations based on workload requirements. Utilize custom machine types to match resources precisely.
• Automate Scaling: Implement auto-scaling for instances and managed services. Scale resources based on demand to avoid over-provisioning.
• Use Managed Services: Leverage managed services like Cloud Functions, App Engine, and Cloud SQL. Google handles underlying infrastructure, optimizing resource utilization.
• Monitor and Analyze Usage: Use tools like Billing Reports and Cost Explorer to analyze usage patterns. Identify underutilized resources and consider downsizing or deleting them.
• Data Lifecycle Management: Implement data lifecycle policies for Cloud Storage and BigQuery. Automatically archive or delete data that is no longer needed to reduce storage costs.

Example (Rightsize Resources):

# Example of creating a custom machine type in GCP
machine_type_config = {
    "guestCpus": 4,
    "memoryMb": 16384
}

compute.instances().insert(
    project=project,
    zone=zone,
    body={
        "machineType": f"zones/{zone}/machineTypes/custom",
        "customMachineType": machine_type_config,
        # ...other instance configuration
    }
).execute()

Q31. Explain the differences between Cloud Functions and Cloud Run.
Ans:

• Cloud Functions: Cloud Functions are serverless functions that respond to events without the need for server provisioning. They are event-driven, automatically scaling with the number of incoming events. They’re suitable for short-lived, stateless functions.
• Cloud Run: Cloud Run allows you to run containers on a fully managed environment. It is ideal for applications packaged as containers. Cloud Run automatically scales based on incoming requests. It provides more flexibility as you can run any language or library that is packaged in a container.

Q32. What is Google Cloud Composer, and how does it simplify workflow automation?
Ans: Google Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow. It allows you to author, schedule, and monitor workflows. Composer simplifies workflow automation by providing a user-friendly interface for designing complex workflows, managing dependencies, and handling retries and failures. It integrates with other GCP services, enabling seamless automation of data pipelines and ETL processes.

Q33. Describe the integration options between on-premises data centers and GCP.
Ans: Integration between on-premises data centers and GCP can be achieved through various methods:

• Cloud VPN: Establish secure IPsec VPN connections between on-premises networks and GCP VPCs, allowing private communication over the public internet.
• Cloud Interconnect: Use Dedicated Interconnect or Partner Interconnect to establish private, low-latency connections between on-premises networks and GCP. This method offers higher bandwidth and reliability compared to VPN connections.
• Hybrid Connectivity: Implement hybrid cloud architectures using Anthos, allowing applications to run on-premises and in GCP simultaneously. Anthos provides a unified platform for managing both on-premises and cloud environments.

Q34. How does Google Cloud Dataflow simplify data processing and analysis?
Ans: Google Cloud Dataflow is a fully managed stream and batch processing service for real-time data analysis and transformation. It simplifies data processing and analysis by providing:

• Scalability: Dataflow automatically scales to handle large datasets, ensuring high throughput and low latency for data processing tasks.
• Serverless Operation: Developers can focus on writing data processing logic without managing infrastructure. Dataflow handles resource provisioning, scaling, and monitoring.
• Unified Batch and Stream Processing: Dataflow supports both batch and stream processing within the same programming model, enabling seamless transition between different data processing paradigms.

Q35. What is Google Cloud Memorystore, and how is it used for caching?
Ans: Google Cloud Memorystore is a fully managed in-memory data store service built on Redis. It is used for caching by storing frequently accessed data in memory, reducing the need to fetch data from slower storage systems like databases. Memorystore provides low-latency access to cached data, improving application performance and responsiveness.

Example (Using Memorystore for Caching):

# Example code to use Memorystore for caching in Python using the redis-py library
import redis

# Connect to the Memorystore instance
client = redis.Redis(host='your-memorystore-instance-hostname', port=6379, db=0)

# Check if the data is already in the cache
cached_data = client.get('cached_data_key')

if cached_data is None:
    # If data is not in cache, fetch it from the database
    data = fetch_data_from_database()

    # Store the fetched data in the cache with an expiration time
    client.setex('cached_data_key', 3600, data)
else:
    # If data is in cache, use the cached data
    data = cached_data

Q36. Explain the concept of Google Cloud Dataproc and its applications.
Ans: Google Cloud Dataproc is a fast, easy-to-use, fully managed cloud service for running Apache Spark and Apache Hadoop clusters. It simplifies the process of data processing, transformation, and analysis. Applications include:

• Big Data Processing: Dataproc efficiently processes large volumes of data using Apache Spark and Hadoop, enabling complex data analytics and machine learning tasks.
• Data ETL: Dataproc can be used for data extraction, transformation, and loading (ETL) tasks, enabling businesses to clean and transform raw data into usable formats for analysis.
• Machine Learning: Dataproc integrates with popular machine learning frameworks like TensorFlow, enabling the training of machine learning models at scale using distributed computing resources.

Q37. What is the significance of Google Cloud Audit Logging in security?
Ans: Google Cloud Audit Logging records all administrative and data plane actions in GCP, providing a comprehensive audit trail for security and compliance purposes. It is significant in security because it allows organizations to:

• Monitor Activity: Track who did what, where, and when, ensuring accountability and transparency in actions performed on GCP resources.
• Detect Anomalies: Analyze audit logs to detect suspicious activities, unauthorized access, or potential security threats, enabling timely response and mitigation.
• Compliance: Meet regulatory and compliance requirements by retaining audit logs and demonstrating adherence to security policies and standards.

Q38. How do you implement identity management using Cloud Identity Platform?
Ans: Cloud Identity Platform is an identity-as-a-service (IDaaS) solution that provides secure and seamless access to applications. It allows organizations to implement identity management by:

• User Authentication: Enable user authentication using various methods, including email/password, multi-factor authentication (MFA), and social identity providers.
• User Management: Manage user identities, roles, and permissions. Cloud Identity Platform integrates with existing identity sources like LDAP or Active Directory for seamless user management.
• Single Sign-On (SSO): Implement single sign-on across multiple applications, allowing users to log in once and access multiple services without re-entering credentials.

Q39. Describe the use case of Google Cloud Machine Learning Engine.
Ans: Google Cloud Machine Learning Engine allows developers to build, train, and deploy machine learning models at scale. Use cases include:

• Predictive Analytics: Build predictive models for sales forecasting, customer churn prediction, and demand forecasting.
• Image Recognition: Develop models for image recognition, object detection, and image classification in applications like healthcare, retail, and autonomous vehicles.
• Natural Language Processing (NLP): Create NLP models for sentiment analysis, chatbots, and language translation, enhancing customer support and communication.

Q40. How does Google Cloud Armor protect against DDoS attacks?
Ans: Google Cloud Armor protects against Distributed Denial of Service (DDoS) attacks by providing the following features:

• Global DDoS Defense: Google’s global network infrastructure detects and mitigates DDoS attacks at the edge of the network, preventing malicious traffic from reaching the targeted resources.
• WAF Rules: Implement Web Application Firewall (WAF) rules to filter and block malicious traffic based on predefined patterns or custom rules, ensuring the security of web applications.
• Rate Limiting: Configure rate-limiting policies to limit the number of requests from specific IP addresses, preventing the overwhelming of resources during DDoS attacks.

Q41. What is the role of Cloud Scheduler in GCP?
Ans: Cloud Scheduler is a fully managed cron job service in GCP that allows users to schedule recurring tasks. It automates the execution of tasks, enabling users to trigger HTTP/S endpoints, Pub/Sub topics, or App Engine applications at specified intervals. Cloud Scheduler is useful for tasks like periodic data backups, report generation, and system maintenance.

Example (Using Cloud Scheduler to Trigger Pub/Sub Topic):

# Example code to create a Cloud Scheduler job to trigger a Pub/Sub topic
gcloud scheduler jobs create pubsub my-scheduled-job \
  --schedule="0 1 * * *" \
  --topic=my-topic \
  --message-body="Hello, Pub/Sub!"

Q42. Explain the process of setting up load balancing for multi-region deployments.
Ans: Setting up load balancing for multi-region deployments in GCP involves configuring Global Load Balancing. Here are the steps:

1. Create Backend Services: Create backend services for each region where your application is deployed. Specify the instances or instance groups as the backends for these services.
2. Create a URL Map: Create a URL map that defines the routing rules for incoming requests. Specify the backend services and path matchers to route requests to the appropriate backends based on the URL path.
3. Create a Global Forwarding Rule: Create a global forwarding rule that references the URL map. This rule distributes incoming traffic across the backend services defined in the URL map.
4. Configure DNS: Update your domain’s DNS settings to point to the IP address of the global forwarding rule. Google’s global network infrastructure will route traffic to the nearest healthy backend service based on the user’s location.

Q43. What are the advantages of using Google Kubernetes Engine Autopilot mode?
Ans: Google Kubernetes Engine (GKE) Autopilot mode is a fully managed Kubernetes service that simplifies cluster management. Advantages include:

• Automated Operations: Autopilot mode handles cluster operations, including upgrades, scaling, and maintenance, ensuring high availability without manual intervention.
• Resource Efficiency: Autopilot automatically optimizes resource allocation, eliminating over-provisioning and optimizing costs.
• Security: Autopilot implements security best practices, ensuring the cluster is always up to date with the latest security patches and configurations.

Q44. How does Google Cloud CDN optimize content delivery for global users?
Ans: Google Cloud CDN optimizes content delivery for global users by caching content at Google’s globally distributed edge points of presence (PoPs). When a user requests content, CDN serves it from the nearest PoP, reducing latency and improving response times. CDN also leverages Google’s high-speed global network, ensuring fast and reliable content delivery to users worldwide.

Q45. Explain the use case of Google Cloud IoT Core in IoT applications.
Ans: Google Cloud IoT Core is a fully managed service for securely connecting and managing IoT devices. Use cases include:

• Device Management: IoT Core allows organizations to manage a large number of IoT devices, including firmware updates, configuration changes, and monitoring device health.
• Data Ingestion: IoT Core securely ingests and processes IoT data from devices, enabling real-time analytics, monitoring, and decision-making.
• Integration with Big Data Services: IoT Core integrates seamlessly with BigQuery, Pub/Sub, and Dataflow, allowing organizations to analyze and derive insights from IoT data at scale.

Q46. What is Google Cloud Armor security policy and its components?
Ans: Google Cloud Armor security policy defines rules to protect applications and services from attacks. Components include:

• Match Conditions: Define conditions based on IP addresses, HTTP methods, or request headers to identify malicious traffic.
• Action Rules: Specify actions to take when a request matches certain conditions, such as allowing, denying, or redirecting the request.
• Security Policies: Group match conditions and action rules into security policies. Multiple security policies can be applied to different backend services, allowing granular control over security settings.

Q47. Describe the process of migrating a traditional database to Google Cloud SQL.
Ans: Migrating a traditional database to Google Cloud SQL involves the following steps:

1. Assessment: Assess the on-premises database to understand schema, data, dependencies, and performance requirements.
2. Database Dump: Export the data from the on-premises database as a dump file, compatible with the Cloud SQL database engine (MySQL, PostgreSQL, SQL Server).
3. Create Cloud SQL Instance: Create a Cloud SQL instance of the desired database engine. Configure settings such as storage capacity, machine type, and replication.
4. Import Data: Import the data dump file into the Cloud SQL instance. This can be done using the Cloud SQL import functionality or other data migration tools.
5. Schema and Application Adjustment: Modify the application code and queries if necessary to match any differences between the on-premises database and Cloud SQL. Update connection strings and authentication methods as needed.
6. Testing and Validation: Test the application thoroughly to ensure data integrity, performance, and functionality. Validate that the migration was successful and resolve any issues.

Q48. How does Google Cloud Storage Transfer Service simplify data transfers?
Ans: Google Cloud Storage Transfer Service simplifies data transfers by providing an automated, serverless solution for transferring large volumes of data from various sources to Cloud Storage. It simplifies the process by allowing users to:

• Schedule Transfers: Schedule one-time or recurring transfers to move data at specific intervals, ensuring data consistency.
• Source Flexibility: Transfer data from various sources, including other cloud providers, on-premises storage systems, or other Google Cloud Storage buckets.
• Managed Operation: Transfer Service handles all aspects of the transfer, including network configuration, error handling, and data verification, reducing manual intervention and ensuring reliability.

Q49. What is Cloud Security Scanner in GCP, and how does it work?
Ans: Cloud Security Scanner is a web security scanner for detecting security vulnerabilities in web applications hosted on GCP. It works by:

• Automated Scanning: Automatically scans web applications for common vulnerabilities like cross-site scripting (XSS) and SQL injection, identifying potential security issues.
• Black-Box Testing: Performs black-box testing, analyzing the application from the outside without needing access to the application’s source code.
• Detailed Reports: Provides detailed reports with identified vulnerabilities, including recommendations for mitigation. It also offers guidance on fixing detected issues.

Q50. Explain the role of Google Cloud Natural Language API in text analysis.
Ans: Google Cloud Natural Language API analyzes and understands text using machine learning. Its role includes:

• Entity Recognition: Identifies entities such as people, organizations, and locations mentioned in the text.
• Sentiment Analysis: Determines the sentiment of the text, whether it’s positive, negative, or neutral.
• Syntax Analysis: Parses the syntax and structure of sentences, extracting linguistic information like parts of speech and grammatical relationships.
• Content Classification: Classifies content into predefined categories or labels based on the context of the text.
• Entity Sentiment Analysis: Combines entity recognition and sentiment analysis to determine the sentiment expressed about specific entities in the text.

Click here for more GCP related topics and interview questions and answers.

To know more about GCP, please visit GCP official site.