Desktop-Based Google Professional-Cloud-Architect Practice Test

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Google Professional-Cloud-Architect Exam Syllabus Topics:

SectionObjectives
Managing implementation- Interacting with Google Cloud programmatically
  • 1. Cloud SDK and gcloud CLI
  • 2. Infrastructure as Code (IaC)
  • 3. APIs and client libraries
- Advising development/operation team(s) to ensure successful deployment
  • 1. Deployment strategies
  • 2. Testing and validation
  • 3. Application development best practices
Managing and provisioning a solution infrastructure- Configuring compute systems
  • 1. Compute Engine configuration
  • 2. Cloud Run and Cloud Functions
  • 3. GKE configuration and management
- Configuring individual storage systems
  • 1. Cloud SQL and Spanner
  • 2. Datastore and Bigtable
  • 3. Cloud Storage configuration
- Configuring network topologies
  • 1. VPC design and peering
  • 2. Hybrid and multi-cloud connectivity
  • 3. Private access and VPN configuration
Designing for security and compliance- Designing for security
  • 1. Data security and encryption
  • 2. Identity and Access Management (IAM)
  • 3. Application security
  • 4. Network security
- Designing for compliance
  • 1. Data residency and sovereignty
  • 2. Regulatory and compliance requirements
  • 3. Audit logging and monitoring
Ensuring solution and operations reliability- Assisting with support
  • 1. Incident response and troubleshooting
  • 2. Capacity planning and scaling
- Monitoring/logging/profiling/alerting solution
  • 1. Error reporting and alerting
  • 2. Cloud Monitoring and Cloud Logging
  • 3. Performance profiling and tracing
- Deployment and release management
  • 1. Release management and versioning
  • 2. Deployment strategies and rollback
- Evaluating quality control measures
Analyzing and optimizing technical and business processes- Analyzing and defining business processes
  • 1. Business KPIs and metrics
  • 2. Cost management and optimization
  • 3. Stakeholder communication
- Analyzing and defining technical processes
  • 1. Software development lifecycle (SDLC)
  • 2. CI/CD pipeline design
  • 3. Incident management and troubleshooting
Designing and planning a cloud solution architecture- Enabling future cloud technology
  • 1. Designing for scalability and elasticity
  • 2. Implementing future-proof architectures
- Creating a migration plan
  • 1. Refactoring and rearchitecting applications
  • 2. Identifying workloads for migration
  • 3. Planning data migration
- Designing network, storage, and compute resources
  • 1. Storage options and data lifecycle management
  • 2. Compute engine selection and configuration
  • 3. Network design including load balancing and firewall rules
  • 4. GKE cluster and workload design
- Designing solution infrastructure that meets business requirements
  • 1. Integration with existing systems
  • 2. Data and storage architecture
  • 3. Migration planning
  • 4. Cost optimization
  • 5. Business use cases and product strategy
  • 6. Networking and compute resources

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q123-Q128):

NEW QUESTION # 123
For this question, refer to the TerramEarth case study.
The TerramEarth development team wants to create an API to meet the company's business requirements. You want the development team to focus their development effort on business value versus creating a custom framework. Which method should they use?

Answer: E

Explanation:
Develop, deploy, protect and monitor your APIs with Google Cloud Endpoints. Using an Open API Specification or one of our API frameworks, Cloud Endpoints gives you the tools you need for every phase of API development.
From scenario:
Business Requirements
Decrease unplanned vehicle downtime to less than 1 week, without increasing the cost of carrying surplus inventory 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.
Reference:
https://cloud.google.com/endpoints/docs/openapi/about-cloud-endpoints?hl=en_US&_ga=2.21787131.-1712523161.1522785064
https://cloud.google.com/endpoints/docs/openapi/architecture-overview
https://cloud.google.com/storage/docs/gsutil/commands/test


NEW QUESTION # 124
A production database virtual machine on Google Compute Engine has an ext4-formatted persistent disk for data files. The database is about to run out of storage space.
How can you remediate the problem with the least amount of downtime?

Answer: E

Explanation:
On Linux instances, connect to your instance and manually resize your partitions and file systems to use the additional disk space that you added.
Extend the file system on the disk or the partition to use the added space. If you grew a partition on your disk, specify the partition. If your disk does not have a partition table, specify only the disk ID.
sudo resize2fs /dev/[DISK_ID][PARTITION_NUMBER]
where [DISK_ID] is the device name and [PARTITION_NUMBER] is the partition number for the device where you are resizing the file system.
References: https://cloud.google.com/compute/docs/disks/add-persistent-disk


NEW QUESTION # 125
You deploy your custom Java application to Google App Engine. It fails to deploy and gives you the following stack trace.
What should you do?

Answer: B


NEW QUESTION # 126
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: A

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.
References: https://cloud.google.com/storage/docs/streaming
https://cloud.google.com/bigquery/docs/loading-data
Topic 1 : Mountkirk Games Case Study 1 6
Topic 2 : TerramEarth Case Study 11
Topic 3 : JencoMart Case Study 6
Topic 4 : Dress4Win case study 12
Topic 5 : Dress4Win Case 2 6
Topic 6 : TerramEarth Case 2 11
Topic 7 : Mountkrik Games Case 2 7
Topic 8 : Mountkrik Games Case 3 7
Topic 9 : Helicopter Racing League Case 6
Topic 10 : EHR Health Care 8
Topic 11 : Cymbal Retail case study 7
Topic 12 : Altostrat Media Case Study 6
Topic 13 : Standalone Questions 254
TOTAL 347
Topic 2, Mountkirk Games Case Study 1
Company Overview
Mountkirk Games makes online, session-based. multiplayer games for the most popular mobile platforms.
Company Background
Mountkirk Games builds all of their games with some server-side integration and has historically used cloud providers to lease physical servers. A few of their games were more popular than expected, and they had problems scaling their application servers, MySQL databases, and analytics tools.
Mountkirk ' s current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting.
Solution Concept
Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the game ' s backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics and take advantage of its autoscaling server environment and integrate with a managed NoSQL database.
Technical Requirements
Requirements for Game Backend Platform
1. Dynamically scale up or down based on game activity.
2. Connect to a managed NoSQL database service.
3. Run customized Linx distro.
Requirements for Game Analytics Platform
1. Dynamically scale up or down based on game activity.
2. Process incoming data on the fly directly from the game servers.
3. Process data that arrives late because of slow mobile networks.
4. Allow SQL queries to access at least 10 TB of historical data.
5. Process files that are regularly uploaded by users ' mobile devices.
6. Use only fully managed services
CEO Statement
Our last successful game did not scale well with our previous cloud provider, resuming in lower user adoption and affecting the game's reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the gams to target users.
CTO Statement
Our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers.
CFO Statement
We are not capturing enough user demographic data usage metrics, and other KPIs. As a result, we do not engage the right users. We are not confident that our marketing is targeting the right users, and we are not selling enough premium Blast-Ups inside the games, which dramatically impacts our revenue.


NEW QUESTION # 127
You company has a Kubernetes application that pulls messages from Pub/Sub and stores them in Firestore.
Because the application is simple, it was deployed as a single pod. The infrastructure team has analyzed Pub/Sub metrics and discovered that the application cannot process the messages in real time. Most of them wait for minutes before being processed. You need to scale the elaboration process that is I/O-intensive.
What should you do?

Answer: A

Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/custom-and-external-metrics#external_metrics


NEW QUESTION # 128
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