Google Associate-Cloud-Engineer Exam With Confidence Using Practice Dumps

Google Cloud Coldline is a new cold-tier storage for archival data with access frequency of less than once per year. Unlike other cold storage options, Nearline has no delays prior to data access, so now it is the leading solution among competitors.

The Real description is about Coldline storage Class:

Coldline Storage

Coldline Storage is a very-low-cost, highly durable storage service for storing infrequently accessed data. Coldline Storage is a better choice than Standard Storage or Nearline Storage in scenarios where slightly lower availability, a 90-day minimum storage duration, and higher costs for data access are acceptable trade-offs for lowered at-rest storage costs.

Coldline Storage is ideal for data you plan to read or modify at most once a quarter. Note, however, that for data being kept entirely for backup or archiving purposes, Archive Storage is more cost-effective, as it offers the lowest storage costs.

Comprehensive and Detailed In Depth Explanation:

The goal is to proactively identify stuck Dataflow jobs with minimal management effort. Let's analyze each option:

A. Error Reporting for slowdowns: Error Reporting primarily focuses on capturing and aggregating exceptions and errors (stack traces). While a stuck job might eventually throw an error, it might also just become unresponsive without generating explicit errors. Relying solely on Error Reporting might not provide timely detection of stuck jobs. Identifying stack traces that indicate slowdowns can also be complex and require significant manual configuration and analysis.

B. Personalized Service Health dashboard: The Personalized Service Health dashboard provides information about Google Cloud service incidents that might be affecting your resources. While it can alert you to broader Dataflow service outages, it won't specifically identify individual stuck jobs due to application-level errors or logic within your Dataflow pipeline.

C. Pub/Sub messages for delays, backup job, and Cloud Tasks alerts: This approach involves significant custom implementation and management. You would need to instrument your Dataflow jobs to detect delays, send messages to Pub/Sub, manage a backup job, and configure Cloud Tasks for alerting. This adds considerable operational overhead and complexity.

D. Cloud Monitoring alerts on data freshness metric: Dataflow provides built-in metrics, including "data freshness" (or similar metrics like "system lag" or "processing time"), which indicate how far behind the pipeline is in processing data. If a job gets stuck, the data freshness will deteriorate beyond an acceptable threshold. Cloud Monitoring allows you to easily set up alerts based on these built-in metrics. This requires minimal custom coding and leverages the platform's existing monitoring capabilities, aligning with the "minimal management effort" requirement.

Therefore, setting up Cloud Monitoring alerts on relevant Dataflow metrics like data freshness is the most efficient and recommended way to detect stuck Dataflow jobs with minimal management overhead.

Google Cloud Documentation References:

Monitoring Dataflow Pipelines: - This document details the various metrics available for monitoring Dataflow jobs in Cloud Monitoring, including metrics related to processing time, system lag, and data freshness.

Creating Alerts in Cloud Monitoring: - Explains how to set up alerts based on metrics collected by Cloud Monitoring.

Dataflow Metrics: - Provides a comprehensive list of Dataflow metrics that can be used for monitoring and alerting.

Comprehensive and Detailed In Depth Explanation:

Let's evaluate each database option against the requirements: automatic scaling, multi-region support, and low latency for a growing social media platform:

A. Bigtable: Bigtable is a highly scalable NoSQL database designed for large analytical and operational workloads with low latency. It offers excellent horizontal scalability and can be deployed across multiple regions for high availability and lower latency for a global user base. However, it's a NoSQL database and might require significant changes to your existing PostgreSQL data model and application code.

B. BigQuery: BigQuery is a fully managed, serverless data warehouse optimized for analytical queries on large datasets. It's not designed for low-latency transactional workloads that a social media platformwould require for real-time user interactions. While it's globally available, its primary use case is not operational database needs.

C. Spanner: Spanner is a globally distributed, horizontally scalable relational database service with strong consistency. It offers automatic scaling, built-in multi-region and multi-continental configurations for high availability and low latency across a global user base, and supports standard SQL (with some extensions). This makes it a strong candidate for a rapidly growing platform needing scalability, global presence, and low latency. While it's not directly PostgreSQL, it offers a relational model and tools to aid migration.

D. Cloud SQL for PostgreSQL: Cloud SQL offers managed PostgreSQL instances with automatic scaling capabilities. It supports high availability within a region and cross-region read replicas for disaster recovery and read scaling. However, its multi-region capabilities for write operations and automatic scaling across regions are more limited compared to Spanner. For a rapidly growing platform with a primarily North American user base but future global expansion in mind and a need for low latency, Spanner's architecture is better suited for true multi-region write capabilities and consistent low latency globally.

Considering the requirements for automatic scaling, multi-region support for both reads and writes with low latency for a growing user base, Spanner is the most appropriate choice.

Google Cloud Documentation References:

Cloud Spanner Overview: - This document highlights Spanner 's global scalability, strong consistency, and multi-region capabilities.

Cloud Bigtable Overview: - While scalable and low-latency, it 's a NoSQL database, which might require significant application changes.

BigQuery Overview: - Focuses on analytics, not low-latency transactional workloads.

Cloud SQL for PostgreSQL Overview: - While it offers scaling and regional HA, its multi-region write capabilities are not as robust as Spanner 's.

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