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Google Professional-Cloud-Architect Exam Syllabus Topics:

SectionObjectives
Topic 1: Manage and provision the solution infrastructure- Automate infrastructure deployment
- Provision compute, storage, and network resources
Topic 2: Design for security and compliance- Implement identity and access management (IAM)
- Configure data protection and encryption
Topic 3: Design and plan a cloud solution architecture- Plan security and compliance requirements
- Design for reliability, scalability, and availability
- Design Google Cloud solutions based on business requirements
Topic 4: Manage implementations of cloud architecture- Ensure solution reliability and operational excellence
- Manage deployment and production systems
Topic 5: Analyze and optimize technical and business processes- Monitor, log, and optimize system performance
- Optimize cost and resource usage

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q293-Q298):

NEW QUESTION # 293
You have deployed an application to Kubernetes Engine, and are using the Cloud SQL proxy container to make the Cloud SQL database available to the services running on Kubernetes. You are notified that the application is reporting database connection issues. Your company policies require a post-mortem. What should you do?

Answer: C


NEW QUESTION # 294
Your company is rapidly deploying containerized microservices on Google Kubernetes Engine (GKE) using a robust CI/CD pipeline. Security is a top priority, and you need to implement a comprehensive and efficient strategy to prevent container image vulnerabilities from reaching your GKE production environment. What should you do? (Choose two.)

Answer: C,D

Explanation:
Artifact Analysis provides automatic vulnerability scanning for container images stored in Artifact Registry, allowing visibility into known vulnerabilities before deployment. Enforcing a policy that blocks deployments when critical vulnerabilities are detected ensures that insecure images never reach the GKE production environment, providing an effective and scalable security control integrated into the CI/CD pipeline.


NEW QUESTION # 295
You are implementing the infrastructure for a web service on Google Cloud. The web service needs to receive and store the data from 500,000 requests per second. The data will be queried later in real time, based on exact matches of a known set of attributes. There will be periods where the web service will not receive any requests. The business wants to keep costs low. Which web service platform and database should you use for the application?

Answer: A

Explanation:
https://cloud.google.com/run/docs/about-instance-autoscaling
https://cloud.google.com/blog/topics/developers-practitioners/bigtable-vs-bigquery-whats-difference


NEW QUESTION # 296
For this question, refer to the JencoMart case study.
The JencoMart security team requires that all Google Cloud Platform infrastructure is deployed using a least privilege model with separation of duties for administration between production and development resources. What Google domain and project structure should you recommend?

Answer: D

Explanation:
Note: The principle of least privilege and separation of duties are concepts that, although semantically different, are intrinsically related from the standpoint of security. The intent behind both is to prevent people from having higher privilege levels than they actually need Principle of Least Privilege: Users should only have the least amount of privileges required to perform their job and no more. This reduces authorization exploitation by limiting access to resources such as targets, jobs, or monitoring templates for which they are not authorized.
Separation of Duties: Beyond limiting user privilege level, you also limit user duties, or the specific jobs they can perform. No user should be given responsibility for more than one related function. This limits the ability of a user to perform a malicious action and then cover up that action.
References: https://cloud.google.com/kms/docs/separation-of-duties


NEW QUESTION # 297
You are developing a deep learning model that requires high-performance access to large volumes of media data currently stored in Cloud Storage. Model training will be executed on multiple VM instances with GPUs attached, but your application must interact with the data as if it were on the local file system. You need to minimize complexity and cost. What should you do?

Answer: C

Explanation:
According to Google Cloud documentation on Cloud Storage FUSE , gcsfuse is the recommended tool for applications that require a POSIX-compliant file system interface to Cloud Storage buckets. This allows deep learning frameworks (like TensorFlow or PyTorch) to read media data using standard file I/O commands (open, read) as if the data were on a local disk, without requiring the application code to be rewritten to use the Cloud Storage API.
Cloud Storage FUSE is the most cost-effective and least complex solution because it avoids the need to duplicate data. Option B (Filestore) and Option C (Persistent Disk) would require you to pay for expensive block/file storage in addition to your Cloud Storage costs, and would require time-consuming data copying steps. Option A (download ing) is impractical for " large volumes of media data " as it would consume significant time and local disk space on every GPU instance.
While gcsfuse does not provide the same performance as a local SSD for random small-file I/O, it is highly optimized for the sequential read patterns typical of media processing and deep learning training. By mounting the bucket directly, you minimize operational overhead and only pay for the data you store in Cloud Storage and the egress associated with the reads, aligning perfectly with the goal of " minimizing complexity and cost. "


NEW QUESTION # 298
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