Free Google Cloud Digital Leader Cloud-Digital-Leader Exam Questions

Anthos :

Anthos unifies the management of infrastructure and applications across on-premises, edge, and in multiple public clouds with a Google Cloud-backed control plane for consistent operation at scale.

- Build, deploy, and optimize apps on GKE and VMs anywhere---simply, flexibly, and securely.

- Consistent development and operations experience for hybrid and multi-cloud environments.

Key features:

1. Enterprise-grade container orchestration and management service

2. Automate policy and security at scale

3. Fully managed service mesh with built-in visibility

4. Modernizing your security for hybrid and multi-cloud deployments

BigQuery is a fully managed, serverless data warehouse that is designed for large-scale data analysis. It is optimized for handling petabytes of data and provides fast query performance. It is scalable and supports advanced analytical queries, making it ideal for organizations that need to analyze massive amounts of historical data efficiently.

Option A: BigQuery is correct because it is specifically designed for large-scale, advanced data analytics and is fully managed by Google Cloud.

Google Cloud: BigQuery Product Overview

Google Cloud BigQuery: Advanced Data Analysis Capabilities

https://cloud.google.com/anthos

Moving to Google Cloud is not an all-or-nothing option. Certain workloads can continue to remain on-premise while the predominant chunk moves to Google Cloud

Streaming analytics is the real-time processing of data as it is ingested. The defining function of streaming analytics is to process data records continuously, as opposed to batch processing, which handles data in large chunks at intervals. Streaming analytics allows for immediate insights and actions based on live data.

Option B: Processing data records continuously is correct because it describes the fundamental nature of streaming analytics: it analyzes data streams as they come in, enabling real-time analysis, monitoring, and decision-making.

Google Cloud Documentation on Dataflow: Real-Time Stream Processing

Google Cloud Platform's Big Data Solutions: Stream Analytics

https://cloud.google.com/sql/docs/postgres/using-query-insights

https://cloud.google.com/blog/products/devops-sre/sre-fundamentals-slis-slas-and-slos

The above is a real world example of a company named Planet, where they sent around 80+ satellites to take pictures of earth every day, 24 hours. They run around 40,000 preemptible VMs concurrently.

Preemptible instances function like normal instances but have the following limitations:

Compute Engine might stop preemptible instances at any time due to system events. The probability that Compute Engine will stop a preemptible instance for a system event is generally low, but might vary from day to day and from zone to zone depending on current conditions.

Compute Engine always stops preemptible instances after they run for 24 hours. Certain actions reset this 24-hour counter.

Preemptible instances are finite Compute Engine resources, so they might not always be available.

Preemptible instances can't live migrate to a regular VM instance, or be set to automatically restart when there is a maintenance event.

Due to the above limitations, preemptible instances are not covered by any Service Level Agreement (and, for clarity, are excluded from the Compute Engine SLA).

The Google Cloud Free Tier credits for Compute Engine do not apply to preemptible instances.

Reference link-https://cloud.google.com/compute/docs/instances/preemptible

The correct answer is C. GKE Autopilot. Here's why:

Context of the Questio n : The organization is using Kubernetes and wants to minimize cluster management so their teams can focus more on developing applications.

Google Cloud Product Relevance:

GKE Autopilot is a fully managed mode of operation for Google Kubernetes Engine (GKE) that abstracts much of the cluster management, such as provisioning and managing the control plane, nodes, and infrastructure. It automates operational tasks and optimizes resource management, enabling developers to focus on writing and deploying code rather than managing Kubernetes clusters.

GKE Autopilot is ideal for organizations that want the benefits of Kubernetes without the overhead of managing cluster infrastructure.

Why Not Other Options:

A . Cloud Run: While Cloud Run is a fully managed service for running containers, it is not specifically a Kubernetes service and does not offer the same Kubernetes-native experience as GKE Autopilot.

B . Compute Engine: Compute Engine requires manual management of VMs and is not specific to containerized or Kubernetes applications.

D . GKE Standard: GKE Standard provides more control over cluster configuration but requires more management effort compared to GKE Autopilot.

Google Cloud Digital Leader Reference:

For more details on GKE Autopilot, refer to the GKE Autopilot documentation.

Collect all weather data evenly across all cities. Mainly because it seems that the emphasis for data collection for ML is to make sure there are no holes in your data collection.

The pricing calculator can be used to give clear estimates of resource usage.

->Running test loads is as closely indicative as using the pricing calculator.

->There are no cloud cost references published, either by Google or CNCF. Even if some companies have published such info. It might not apply to you.

Reference link:-https://cloud.google.com/products/calculator