Google Cloud Platform (GCP) Interview Question Flashcards
Can you explain the difference between Google Cloud Storage and Google Cloud SQL?
Google Cloud Storage is an object storage service for storing and retrieving any data at any time. It is ideal for unstructured data like media files, backups, etc. On the other hand, Google Cloud SQL is a fully-managed relational database service for MySQL, PostgreSQL, and SQL Server. It is ideal for structured data and supports transactions, complex joins, and other SQL features.
What is Google Cloud Pub/Sub, and how does it work?
Google Cloud Pub/Sub is a messaging service created for sending and receiving messages between independent applications. It works on the principle of the publisher-subscriber model. Publishers create and send messages to “topics”. Subscribers then receive those messages through a subscription to those topics.
AWS Kinesis, Amazon MQ
What is Google Cloud Dataflow, and what are its benefits?
Google Cloud Dataflow is an independent and fully managed service for implementing Apache Beam pipelines within the Google Cloud Platform. It provides a simplified, serverless approach for batch and real-time data processing. Its benefits include automatic resource management, dynamic work rebalancing, and creating pipelines using Java or Python SDKs.
How do Google Cloud’s networking products ensure secure and reliable connectivity?
Google Cloud’s networking products are designed to provide secure, high-performance, and reliable connectivity. Here’s how they achieve this:
Google Cloud VPC (Virtual Private Cloud): VPC provides a private network with IP allocation, routing, and network firewall policies to ensure secure connectivity within your cloud environment. It supports both IPv4 and IPv6 for global reach and scalability.
Cloud Load Balancing: This service automatically distributes traffic across servers to secure high availability and reliability. It also provides cross-region load balancing, allowing your application to stay resilient even if an entire region goes down.
Cloud Armor: This service works with Cloud Load Balancing to defend against DDoS attacks, thus ensuring secure connectivity. AWS WAF
Cloud CDN (Content Delivery Network): By caching content close to the users, Cloud CDN ensures fast, reliable content delivery to users worldwide. AWS CloudFront
Cloud Interconnect and Cloud VPN: These services provide secure, high-performance connectivity between your cloud resources and your on-premises, hosted, or other cloud environments. Cloud Interconnect -> Direct Connect, Cloud VPN -> Site to Site connection
Cloud DNS: This scalable, reliable DNS service ensures your application is easily accessible from anywhere in the world. AWS Route53
Private Google Access: This service allows VM instances with internal IP addresses to reach Google APIs and services securely without needing a public IP. AWS VPC PrivateLink
Can you explain the concept of Google Cloud Functions?
Google Cloud Functions is a powerful tool that enables developers to build and connect cloud services in a serverless execution environment. This means you can create simple, single-purpose functions triggered by events emitted from your cloud infrastructure and services. Through Cloud Functions, you can easily automate your cloud workflows and build powerful applications with minimal overhead. Simply attach your function to an event, and it will execute automatically when that event is fired, making it a highly efficient and effective solution for modern cloud development.
How does Google Cloud AutoML benefit businesses?
Google Cloud AutoML is a powerful tool that enables individuals without machine learning expertise to access the benefits of machine learning. This platform allows businesses to create customized machine learning models with minimal effort, which can be tailored to suit their specific needs. With Google Cloud AutoML, businesses can leverage the advantages of machine learning to improve their operations without having to invest significant amounts of time or resources.
Can you describe how Google Cloud Bigtable works?
Google Cloud Bigtable is a scalable, fully managed NoSQL database service. It is designed to collect and retain data from 1TB to hundreds of PB. It offers low latency and high throughput, making it suitable for big data and real-time applications. It integrates seamlessly with popular big data tools like Hadoop and supports the Apache HBase API and the Google Cloud Bigtable API.
Can you explain the difference between BigQuery and Bigtable?
BigQuery is a fully managed, serverless data warehouse that enables super-fast SQL queries using the processing power of Google’s infrastructure. It’s designed for analyzing large datasets. Bigtable, on the other hand, is a NoSQL big data database service designed for low-latency, large-scale applications and operational analytics.
How would you design a data pipeline in Google Cloud Platform?
Designing a data pipeline in Google Cloud Platform (GCP) involves several steps and services. Here’s a basic outline:
Data ingestion: The first step is to ingest data from different sources. This could be from on-premises databases, third-party applications, or other cloud platforms. GCP provides several services for data ingestion, like Cloud Pub/Sub for real-time messaging, Cloud Storage for unstructured data, and Cloud SQL for structured data.
Data processing: Once the data is ingested, it needs to be processed. This could involve cleaning the data, transforming it into a suitable format, or running computations. You can use Cloud Dataflow, a fully-managed service for stream and batch processing, or Cloud Dataproc, a managed Hadoop and Spark service for big data processing.
Data storage: After processing, the data usually needs to be stored for further analysis. Depending on your needs, you can use BigQuery, a fully-managed and highly scalable data warehouse, Cloud Bigtable for NoSQL workloads, or Cloud Spanner for relational database needs.
Data analysis and visualization: The processed data can be analyzed and visualized using tools like Google Data Studio or Looker. You can also use BigQuery ML to create and execute machine learning models on your data.
Designing a data pipeline in GCP should also involve security, reliability, and cost considerations. You should use Cloud IAM for access control, ensure your data is backed up for reliability, and monitor your usage to control costs.
What is Google Cloud Dataflow? What are its benefits?
Google Cloud Dataflow is a fully managed service used for Apache Beam pipeline execution within the Google Cloud Platform. It provides a simplified, serverless approach for batch and real-time data processing. Its benefits include automatic resource management, dynamic work rebalancing, and creating pipelines using Java or Python SDKs.
How would you handle large datasets in Google Cloud Platform?
Google Cloud Platform offers several services to handle large datasets. You can use Cloud Storage to store large amounts of data, BigQuery to analyze large datasets, and Bigtable to handle large-scale operational analytics. For processing large datasets, you can use Cloud Dataflow or Cloud Dataproc.
What is Google Cloud Dataproc, and how does it work?
Google Cloud Dataproc is a managed service that runs Apache Hadoop and Spark jobs. It simplifies the creation, configuration, and management of Hadoop clusters, reducing the time required to start a job. It also supports the most common Hadoop ecosystem tools, allowing you to use existing skills and code.
How does Google Cloud Datalab help in data exploration and visualization?
Google Cloud Datalab is a tool for exploring, transforming, analyzing, and visualizing data on the Google Cloud Platform. It provides a Jupyter notebook-based environment with support for
1. multiple programming languages,
2. built-in machine learning APIs,
3. and easy integration with BigQuery.
How does Google Cloud AutoML benefit businesses?
Google Cloud AutoML offers several benefits to businesses:
- Simplified machine learning: AutoML allows businesses to leverage machine learning models without requiring machine learning or coding expertise.
- Custom models: Businesses can create machine learning models based on their needs. This can help in improving the accuracy of predictions.
- Scalability: AutoML is built on Google Cloud, which means it can easily scale as the business grows. It can handle large datasets and high demand without any additional infrastructure investment.
- Integration: AutoML can be easily integrated with other Google Cloud services, helping provide seamless workflow.
- Speed: AutoML significantly reduces the time it takes to create and deploy machine learning models. This can help make data-driven decisions quickly.
- Cost-effective: With AutoML, businesses only pay for what they use. This can make machine learning more affordable, especially for small and medium-sized businesses.
