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| Section | Objectives |
|---|---|
| Design for security and compliance | - Implement identity and access management (IAM) - Configure data protection and encryption |
| Analyze and optimize technical and business processes | - Optimize cost and resource usage - Monitor, log, and optimize system performance |
| Manage and provision the solution infrastructure | - Automate infrastructure deployment - Provision compute, storage, and network resources |
| Manage implementations of cloud architecture | - Ensure solution reliability and operational excellence - Manage deployment and production systems |
| Design and plan a cloud solution architecture | - Design Google Cloud solutions based on business requirements - Design for reliability, scalability, and availability - Plan security and compliance requirements |
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NEW QUESTION # 202
Your company plans to migrate a multi-petabyte data set to the cloud. The data set must be available 24hrs a day. Your business analysts have experience only with using a SQL interface. How should you store the data to optimize it for ease of analysis?
Answer: C
Explanation:
Google Big Query is for multi peta byte storage , HA(High availability) which means 24 hours, SQL interface .
https://medium.com/google-cloud/the-12-components-of-google-bigquery-c2b49829a7c7
https://cloud.google.com/solutions/bigquery-data-warehouse
https://cloud.google.com/bigquery/
BigQuery is Google's serverless, highly scalable, low cost enterprise data warehouse designed to make all your data analysts productive. Because there is no infrastructure to manage, you can focus on analyzing data to find meaningful insights using familiar SQL and you don't need a database administrator.
BigQuery enables you to analyze all your data by creating a logical data warehouse over managed, columnar storage as well as data from object storage, and spreadsheets.
References:
https://cloud.google.com/bigquery/
NEW QUESTION # 203
For this question, refer to the TerramEarth case study
You analyzed TerramEarth's business requirement to reduce downtime, and found that they can achieve a majority of time saving by reducing customers' wait time for parts You decided to focus on reduction of the 3 weeks aggregate reporting time Which modifications to the company's processes should you recommend?
Answer: C
Explanation:
The Avro binary format is the preferred format for loading compressed data. Avro data is faster to load because the data can be read in parallel, even when the data blocks are compressed.
Cloud Storage supports streaming transfers with the gsutil tool or boto library, based on HTTP chunked transfer encoding. Streaming data lets you stream data to and from your Cloud Storage account as soon as it becomes available without requiring that the data be first saved to a separate file. Streaming transfers are useful if you have a process that generates data and you do not want to buffer it locally before uploading it, or if you want to send the result from a computational pipeline directly into Cloud Storage.
Reference:
https://cloud.google.com/bigquery/docs/loading-data
NEW QUESTION # 204
You are creating an App Engine application that uses Cloud Datastore as its persistence layer.
You need to retrieve several root entities for which you have the identifiers. You want to minimize the overhead in operations performed by Cloud Datastore. What should you do?
Answer: A
Explanation:
Create the Key object for each Entity and run a batch get operation
https://cloud.google.com/datastore/docs/best-practices
Use batch operations for your reads, writes, and deletes instead of single operations. Batch operations are more efficient because they perform multiple operations with the same overhead as a single operation.
Firestore in Datastore mode supports batch versions of the operations which allow it to operate on multiple objects in a single Datastore mode call.
Such batch calls are faster than making separate calls for each individual entity because they incur the overhead for only one service call. If multiple entity groups are involved, the work for all the groups is performed in parallel on the server side.
NEW QUESTION # 205
Your company has an application deployed on Anthos clusters (formerly Anthos GKE) that is running multiple microservices. The cluster has both Anthos Service Mesh and Anthos Config Management configured. End users inform you that the application is responding very slowly. You want to identify the microservice that is causing the delay. What should you do?
Answer: D
Explanation:
The Anthos Service Mesh pages in the Google Cloud Console provide both summary and in-depth metrics, charts, and graphs that enable you to observe service behavior. You can monitor the overall health of your services, or drill down on a specific service to set a service level objective (SLO) or troubleshoot an issue.
https://cloud.google.com/service-mesh/docs/observability/explore-dashboard
https://cloud.google.com/anthos/service-mesh
NEW QUESTION # 206
For this question, refer to the JencoMart case study.
The migration of JencoMart's application to Google Cloud Platform (GCP) is progressing too slowly. The infrastructure is shown in the diagram. You want to maximize throughput. What are three potential bottlenecks? (Choose 3 answers.)
Answer: D,E,F
NEW QUESTION # 207
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