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Exam Topics

The certification exam covers six domains. Each of them has a list of subtopics that the applicants should understand before taking the test. To develop competence in the content, it is recommended that you spend some time going over the exam guide and taking some practice tests. Below are the details of the subjects covered in the Google Professional Cloud Architect exam:

Designing & Planning Cloud Solution Architectures

Design a solution infrastructure that fulfills the business prerequisites: this covers skills in cost optimization; product strategy and business use cases; integration with the external systems; supporting application designs; build-buy-or-modify; design decision trade-off; data movement; observability and compliance; success measurements;Design a solution infrastructure that fulfills the technical prerequisites: the candidates must demonstrate their understanding of Cloud resources elasticity; failover design and high availability; latency and performance; scalability to fulfill growth needs;Create a migration plan: the students should be able to integrate the solution with current systems; migrate data and systems to support solutions; license mapping; test and proof concepts; network planning; dependency management strategy;Envision future solutions enhancements: the individuals should understand the technology and Cloud improvements; evangelism and advocacy; business requirements evolution.Design network, compute, and storage resources: this covers one’s competence in selecting data processing technologies, relevant storage types, and compute resources; integration with multi-Cloud/on-premises environments; Cloud-native networking; mapping compute requirements for platform products;Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q176-Q181):

NEW QUESTION # 176
During a high traffic portion of the day, one of your relational databases crashes, but the replica is never promoted to a master. You want to avoid this in the future. What should you do?

A. Implement routinely scheduled failovers of your databases.B. Create snapshots of your database more regularly.C. Use a different database.D. Choose larger instances for your database.

Answer: B

Explanation:
Explanation
Take regular snapshots of your database system.
If your database system lives on a Compute Engine persistent disk, you can take snapshots of your system each time you upgrade. If your database system goes down or you need to roll back to a previous version, you can simply create a new persistent disk from your desired snapshot and make that disk the boot disk for a new Compute Engine instance. Note that, to avoid data corruption, this approach requires you to freeze the database system's disk while taking a snapshot.
Reference: https://cloud.google.com/solutions/disaster-recovery-cookbook

NEW QUESTION # 177
For this question refer to the TerramEarth case study.
Which of TerramEarth's legacy enterprise processes will experience significant change as a result of increased Google Cloud Platform adoption.

A. Capacity planning, TCO calculations, opex/capex allocationB. Data Center expansion, TCO calculations, utilization measurementC. Capacity planning, utilization measurement, data center expansionD. Opex/capex allocation, LAN changes, capacity planning

Answer: A

Explanation:
Capacity planning, TCO calculations, opex/capex allocation From the case study, it can conclude that Management (CXO) all concern rapid provision of resources (infrastructure) for growing as well as cost management, such as Cost optimization in Infrastructure, trade up front capital expenditures (Capex) for ongoing operating expenditures (Opex), and Total cost of ownership (TCO)

NEW QUESTION # 178
Your company captures all web traffic data in Google Analytics 260 and stores it in BigQuery. Each country has its own dataset. Each dataset has multiple tables. You want analysts from each country to be able to see and query only the data for their respective countries.
How should you configure the access rights?

A. Create a group per country. Add analysts to their respective country-groups. Create a single group
'all_analysts', and add all country-groups as members. Grant the 'all-analysis' group the IAM role of BigQuery dataViewer. Share the appropriate dataset with view access with each respective analyst country-group.B. Create a group per country. Add analysts to their respective country-groups. Create a single group
'all_analysts', and add all country-groups as members. Grant the 'all-analysis' group the IAM role of BigQuery jobUser. Share the appropriate dataset with view access with each respective analyst country-group.C. Create a group per country. Add analysts to their respective country-groups. Create a single group
'all_analysts', and add all country-groups as members. Grant the 'all-analysis' group the IAM role of BigQuery dataViewer. Share the appropriate table with view access with each respective analyst countrygroup.D. Create a group per country. Add analysts to their respective country-groups. Create a single group
'all_analysts', and add all country-groups as members. Grant the 'all-analysis' group the IAM role of BigQuery jobUser. Share the appropriate tables with view access with each respective analyst countrygroup.

Answer: B

NEW QUESTION # 179
TerramEarth's 20 million vehicles are scattered around the world. Based on the vehicle's location, its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US, Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after
100 K miles. You want to run this job on all the data.
What is the most cost-effective way to run this job?

A. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a region bucket and use a Cloud Dataproc cluster to finish the jobB. Move all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the jobC. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a multi-region bucket and use a Dataproc cluster to finish the jobD. Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job

Answer: A

NEW QUESTION # 180
One of the developers on your team deployed their application in Google Container Engine with the Dockerfile below. They report that their application deployments are taking too long.

You want to optimize this Dockerfile for faster deployment times without adversely affecting the app's functionality.
Which two actions should you take? Choose 2 answers.

A. Use larger machine types for your Google Container Engine node pools.B. Copy the source after the package dependencies (Python and pip) are installed.C. Remove Python after running pip.D. Remove dependencies from requirements.txt.E. Use a slimmed-down base image like Alpine linux.

Answer: B,E

Explanation:
Explanation
The speed of deployment can be changed by limiting the size of the uploaded app, limiting the complexity of the build necessary in the Dockerfile, if present, and by ensuring a fast and reliable internet connection.
Note: Alpine Linux is built around musl libc and busybox. This makes it smaller and more resource efficient than traditional GNU/Linux distributions. A container requires no more than 8 MB and a minimal installation to disk requires around 130 MB of storage. Not only do you get a fully-fledged Linux environment but a large selection of packages from the repository.
References: https://groups.google.com/forum/#!topic/google-appengine/hZMEkmmObDU
https://www.alpinelinux.org/about/

NEW QUESTION # 181
......

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