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Google Professional-Cloud-Architect Exam Syllabus Topics:

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

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q59-Q64):

NEW QUESTION # 59
For this question, refer to the Mountkirk Games case study. You need to analyze and define the technical architecture for the database workloads for your company, Mountkirk Games. Considering the business and technical requirements, what should you do?

A. Use Cloud SQL for time series data, and use Cloud Bigtable for historical data queries.
B. Use Cloud SQL to replace MySQL, and use Cloud Spanner for historical data queries.
C. Use Cloud Bigtable for time series data, use Cloud Spanner for transactional data, and use BigQuery for historical data queries.
D. Use Cloud Bigtable to replace MySQL, and use BigQuery for historical data queries.

Answer: C

Explanation:
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series

NEW QUESTION # 60
You are using Cloud SQL as the database backend for a large CRM deployment. You want to scale as usage increases and ensure that you don't run out of storage, maintain 75% CPU usage cores, and keep replication lag below 60 seconds. What are the correct steps to meet your requirements?

A. 1. Enable automatic storage increase for the instance.
2. Create a Stackdriver alert when CPU usage exceeds 75%, and change the instance type to reduce CPU usage.
3. Create a Stackdriver alert for replication lag, and shard the database to reduce replication time.
B. 1. Enable automatic storage increase for the instance.
2. Change the instance type to a 32-core machine type to keep CPU usage below 75%.
3. Create a Stackdriver alert for replication lag, and shard the database to reduce replication time.
C. 1. Create a Stackdriver alert when storage exceeds 75%, and increase the available storage on the instance to create more space.
2. Deploy memcached to reduce CPU load.
3. Create a Stackdriver alert for replication lag, and change the instance type to a 32-core machine type to reduce replication lag.
D. 1. Create a Stackdriver alert when storage exceeds 75%, and increase the available storage on the instance to create more space.
2. Deploy memcached to reduce CPU load.
3. Change the instance type to a 32-core machine type to reduce replication lag.

Answer: A

NEW QUESTION # 61
You are creating a solution to remove backup files older than 90 days from your backup Cloud Storage bucket. You want to optimize ongoing Cloud Storage spend.
What should you do?

A. Write a lifecycle management rule in JSON and push it to the bucket with gsutil
B. Schedule a cron script using gsutil ls -lr gs://backups/**to find and remove items older than 90 days
C. Write a lifecycle management rule in XML and push it to the bucket with gsutil
D. Schedule a cron script using gsutil ls -l gs://backups/**to find and remove items older than
90 days and schedule it with cron

Answer: A

NEW QUESTION # 62
You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also to reliably deploy new versions of the application. Which set of steps should you take?

A. Perform the following:
1) Create a Kubernetes Engine (GKE) cluster with n1-standard-4 type machines.
2) Build a Docker image from the master branch will all of the dependencies, and tag it with "latest".
3) Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to "Always".
Restart the pods to automatically deploy new production releases.
B. Perform the following:
1) Create a managed instance group with f1-micro type machines.
2) Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.
3) Restart the instances to automatically deploy new production releases.
C. Perform the following:
1) Create a managed instance group with n1-standard-1 type machines.
2) Build a Compute Engine image from the production branch that contains all of the dependencies and automatically starts the Python app.
3) Rebuild the Compute Engine image, and update the instance template to deploy new production releases.
D. Perform the following:
1) Create a Kubernetes Engine cluster with n1-standard-1 type machines.
2) Build a Docker image from the production branch with all of the dependencies, and tag it with the
3) Create a Kubernetes Deployment with the imagePullPolicy set to "IfNotPresent" in the staging namespace, and then promote it to the production namespace after testing.

Answer: A

Explanation:
https://cloud.google.com/compute/docs/instance-templates

NEW QUESTION # 63
Case Study: 6 - TerramEarth
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.
Considering the technical requirements, how should you reduce the unplanned vehicle downtime in GCP?

A. Use Cloud Dataproc Hive as the data warehouse. Upload gzip files to a MultiRegional Cloud Storage bucket. Upload this data into BigQuery using gcloud. Use Google data Studio for analysis and reporting.
B. Use BigQuery as the data warehouse. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and reporting.
C. Use Cloud Dataproc Hive as the data warehouse. Directly stream data into prtitioned Hive tables.
Use Pig scripts to analyze data.
D. Use BigQuery as the data warehouse. Connect all vehicles to the network and upload gzip files to a Multi-Regional Cloud Storage bucket using gcloud. Use Google Data Studio for analysis and reporting.

Answer: B

Explanation:
https://cloud.google.com/architecture/designing-connected-vehicle-platform#data_ingestion

NEW QUESTION # 64
......

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