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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified Professional Cloud Architect Exam |
| Exam Number: | Professional Cloud Architect |
| Exam Format: | Case study-based questions, Multiple select, Multiple choice |
| Exam Duration: | 120 minutes |
| Related Certifications: | Google Cloud Professional Cloud Network Engineer Google Cloud Professional Cloud Security Engineer Google Cloud Professional Data Engineer Google Cloud Associate Cloud Engineer Google Cloud Digital Leader Google Cloud Professional Cloud Developer |
| Available Languages: | French, Spanish, Japanese, English, Portuguese |
| Passing Score: | Not publicly disclosed (scaled score; commonly reported passing threshold ~70%) |
| Real Exam Qty: | Approximately 50–60 questions |
| Certificate Validity Period: | 2 years |
| Exam Price: | $200 USD |
| Recommended Training: | Google Cloud Skills Boost - Cloud Architect Learning Path Coursera Google Cloud Professional Cloud Architect Training |
| Exam Registration: | Google Cloud Certification Portal Professional Cloud Architect Exam Page |
| Sample Questions: | Google Professional-Cloud-Architect Sample Questions |
| Exam Way: | Online proctored or onsite test center exam |
| Pre Condition: | No formal prerequisites required, but Google recommends prior experience with Google Cloud and cloud architecture design. |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/cloud-architect |
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NEW QUESTION # 107
You want to enable your running Google Container Engine cluster to scale as demand for your application changes.
What should you do?
Answer: A
Explanation:
and redeploy your application.
Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-autoscaler Cluster autoscaling
--enable-autoscaling
Enables autoscaling for a node pool.
Enables autoscaling in the node pool specified by --node-pool or the default node pool if --node-pool is not provided.
Where:
--max-nodes=MAX_NODES
Maximum number of nodes in the node pool.
Maximum number of nodes to which the node pool specified by --node-pool (or default node pool if unspecified) can scale.
Incorrect Answers:
C, D: Warning: Do not use Alpha Clusters or alpha features for production workloads.
Note: You can experiment with Kubernetes alpha features by creating an alpha cluster. Alpha clusters are short-lived clusters that run stable Kubernetes releases with all Kubernetes APIs and features enabled. Alpha clusters are designed for advanced users and early adopters to experiment with workloads that take advantage of new features before those features are production-ready. You can use Alpha clusters just like normal Kubernetes Engine clusters.
NEW QUESTION # 108
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 # 109
You have an App Engine application that needs to be updated. You want to test the update with production
traffic before replacing the current application version.
What should you do?
Answer: A
NEW QUESTION # 110
Your company wants to try out the cloud with low risk. They want to archive approximately
100 TB of their log data to the cloud and test the analytics features available to them there, while also retaining that data as a long-term disaster recovery backup. Which two steps should they take? Choose 2 answers
Answer: B,D
NEW QUESTION # 111
For this question, refer to the Dress4Win case study. Considering the given business requirements, how would you automate the deployment of web and transactional data layers?
Answer: A
Explanation:
Topic 6, TerramEarth Case 2
Company Overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries. About 80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in
100 countries. Their mission is to build products that make their customers more productive.
Solution Concept
There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second. Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced. The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules.
Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second with 22 hours of operation per day, TerramEarth collects a total of about 9 TB/day from these connected vehicles.
Existing Technical Environment
TerramEarth's existing architecture is composed of Linux and Windows-based systems that reside in a single
U.S. west coast based data center. These systems gzip CSV files from the field and upload via FTP, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old.
With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts.
Business Requirements
Decrease unplanned vehicle downtime to less than 1 week.
Support the dealer network with more data on how their customers use their equipment to better position new products and services Have the ability to partner with different companies - especially with seed and fertilizer suppliers in the fast-growing agricultural business - to create compelling joint offerings for their customers.
Technical Requirements
Expand beyond a single datacenter to decrease latency to the American Midwest and east coast.
Create a backup strategy.
Increase security of data transfer from equipment to the datacenter.
Improve data in the data warehouse.
Use customer and equipment data to anticipate customer needs.
Application 1: Data ingest
A custom Python application reads uploaded datafiles from a single server, writes to the data warehouse.
Compute:
Windows Server 2008 R2
- 16 CPUs
- 128 GB of RAM
- 10 TB local HDD storage
Application 2: Reporting
An off the shelf application that business analysts use to run a daily report to see what equipment needs repair.
Only 2 analysts of a team of 10 (5 west coast, 5 east coast) can connect to the reporting application at a time.
Compute:
Off the shelf application. License tied to number of physical CPUs
- Windows Server 2008 R2
- 16 CPUs
- 32 GB of RAM
- 500 GB HDD
Data warehouse:
A single PostgreSQL server
- RedHat Linux
- 64 CPUs
- 128 GB of RAM
- 4x 6TB HDD in RAID 0
Executive Statement
Our competitive advantage has always been in the manufacturing process, with our ability to build better vehicles for lower cost than our competitors. However, new products with different approaches are constantly being developed, and I'm concerned that we lack the skills to undergo the next wave of transformations in our industry. My goals are to build our skills while addressing immediate market needs through incremental innovations.
NEW QUESTION # 112
......
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