[Google] GCP-PCA - Professional Cloud Architect Exam Dumps & Study Guide

Free [Google] GCP-PCA - Professional Cloud Architect Practice Questions Preview

• Question 1

  Your company has decided to make a major revision of their API in order to create better experiences for their developers. They need to keep the old version of the API available and deployable, while allowing new customers and testers to try out the new API. They want to keep the same SSL and DNS records in place to serve both APIs.
  What should they do?
  

  • A. Configure a new load balancer for the new version of the API
  • B. Reconfigure old clients to use a new endpoint for the new API
  • C. Have the old API forward traffic to the new API based on the path
  • D. Use separate backend pools for each API path behind the load balancer

  Correct Answer: D

  Explanation:

  The best approach is to use separate backend pools for each API path behind the load balancer. Therefore, the suggested answer is D.

  Reasoning: This solution effectively addresses the problem by maintaining a single entry point (same SSL and DNS records) while allowing both old and new API versions to coexist. The load balancer can route traffic to the appropriate backend pool based on the request path. This ensures that existing clients continue to use the old API without any changes, while new clients and testers can access the new API via a different path. The reason for choosing this answer is that it meets all the requirements: maintaining the same SSL/DNS records, allowing both API versions to be available, and not requiring client reconfiguration.

  Reasons for not choosing other options:

  • A. Configure a new load balancer for the new version of the API: This option violates the requirement to keep the same SSL and DNS records. A new load balancer would necessitate changes to DNS records, which the problem statement explicitly prohibits.
  • B. Reconfigure old clients to use a new endpoint for the new API: This option requires changes to existing clients, which is undesirable. The goal is to allow existing clients to continue using the old API without any changes.
  • C. Have the old API forward traffic to the new API based on the path: This option introduces unnecessary complexity and potential performance bottlenecks. It also tightly couples the old and new APIs, making independent deployment and scaling more difficult.

  Using separate backend pools behind a load balancer is a common and effective strategy for API versioning and traffic management. This approach allows for a smooth transition to the new API while minimizing disruption to existing clients.

  • Google Cloud Load Balancing Documentation, https://cloud.google.com/load-balancing/docs

• Question 2

  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?
  

  • A. Load data into Google BigQuery
  • B. Insert data into Google Cloud SQL
  • C. Put flat files into Google Cloud Storage
  • D. Stream data into Google Cloud Datastore

  Correct Answer: A

  Explanation:

  The best solution for storing a multi-petabyte dataset for analysis, accessible 24/7, with business analysts familiar with SQL, is to **A. Load data into Google BigQuery**.

  Reasoning:

  • BigQuery is designed for large-scale data warehousing and analytics: It can handle multi-petabyte datasets efficiently. This aligns with the requirement of handling a multi-petabyte dataset.
  • BigQuery provides a SQL interface: This allows business analysts to use their existing SQL skills, meeting the requirement for a SQL interface.
  • BigQuery is a fully-managed, serverless data warehouse: This ensures high availability and reduces the operational overhead.
  • BigQuery is optimized for analytical queries: This means that the system is designed to handle complex analytical queries that data analysts frequently run.
  • Availability: BigQuery is designed for high availability, ensuring the data is accessible 24/7.

  Why other options are not suitable:

  • B. Insert data into Google Cloud SQL: Cloud SQL is a relational database service suitable for transactional workloads (OLTP), not analytical workloads (OLAP). It does not scale to petabytes efficiently. Cloud SQL is not designed for multi-petabyte data sets and is not optimized for analytical queries.
  • C. Put flat files into Google Cloud Storage: While Cloud Storage is excellent for storing large amounts of data, it does not provide a SQL interface for querying the data. It requires additional tools and expertise to analyze the data. Cloud Storage requires additional processing to make the data queryable and does not provide a SQL interface directly.
  • D. Stream data into Google Cloud Datastore: Cloud Datastore (now Firestore in Datastore mode) is a NoSQL database, which does not have a SQL-like interface and is not intended for large-scale analytical queries. Cloud Datastore is a NoSQL database and doesn't provide a SQL interface.

  Therefore, BigQuery is the most suitable choice because it is designed for analytical workloads, scales to petabytes, offers a SQL interface, and provides high availability.

  Citations:

  • BigQuery Overview, https://cloud.google.com/bigquery/docs/introduction

• Question 3

  The operations manager asks you for a list of recommended practices that she should consider when migrating a J2EE application to the cloud.
  Which three practices should you recommend? (Choose three.)
  

  • A. Port the application code to run on Google App Engine
  • B. Integrate Cloud Dataflow into the application to capture real-time metrics
  • C. Instrument the application with a monitoring tool like Stackdriver Debugger
  • D. Select an automation framework to reliably provision the cloud infrastructure
  • E. Deploy a continuous integration tool with automated testing in a staging environment
  • F. Migrate from MySQL to a managed NoSQL database like Google Cloud Datastore or Bigtable

  Correct Answer: CDE

  Explanation:

  Based on the question and discussion, the recommended practices for migrating a J2EE application to the cloud are: selecting an automation framework for infrastructure provisioning, instrumenting the application with a monitoring tool, and deploying a CI/CD pipeline with automated testing. Therefore, the suggested answers are C, D, and E.

  Here's a detailed explanation:

  The suggested answer is C, D, and E.

  Reasoning for choosing C: Instrumenting the application with a monitoring tool is essential for understanding application performance and identifying issues after migration. While Stackdriver Debugger may be deprecated, the principle of having monitoring in place remains valid, and other tools can be used to achieve the same goal. This provides operational visibility, crucial for managing the application in the cloud.

  Reasoning for choosing D: Selecting an automation framework is crucial for reliably provisioning cloud infrastructure. Automation ensures consistency, reduces manual errors, and enables infrastructure as code, which is a key best practice in cloud environments.

  Reasoning for choosing E: Deploying a continuous integration (CI) tool with automated testing in a staging environment is vital for application quality and faster release cycles. Automated testing helps to identify and fix bugs early in the development process, leading to a more stable and reliable application. A staging environment allows for testing changes in a production-like setting before deployment.

  Reasoning for not choosing A: Porting the application code to run on Google App Engine may be a valid option in some cases, but it's not a universally recommended practice. It depends on the specific application requirements and whether App Engine is a suitable platform. It might involve significant code changes, and other options like Compute Engine or Kubernetes Engine might be more appropriate for some J2EE applications. This answer is too specific and might not be applicable in all scenarios.

  Reasoning for not choosing B: Integrating Cloud Dataflow into the application to capture real-time metrics is not a general best practice for all J2EE application migrations. Dataflow is suitable for specific use cases involving stream and batch data processing. It's not a core requirement for a successful migration and is more relevant to applications that require real-time data analytics.

  Reasoning for not choosing F: Migrating from MySQL to a managed NoSQL database like Google Cloud Datastore or Bigtable is also not a universally recommended practice. It depends on the application's data model and requirements. If the application is well-suited for a relational database, migrating to NoSQL might not be necessary or beneficial. A thorough assessment of the data requirements is needed before making such a change. This answer is too specific and might not be applicable in all scenarios.

  Citations:

  • Google Cloud Documentation on Monitoring: https://cloud.google.com/monitoring/
  • Google Cloud Documentation on Infrastructure as Code: https://cloud.google.com/solutions/infrastructure-as-code
  • Google Cloud Documentation on CI/CD: https://cloud.google.com/devops/continuous-delivery/

• Question 4

  A news feed web service has the following code running on Google App Engine. During peak load, users report that they can see news articles they already viewed.
  What is the most likely cause of this problem?
  
  

  • A. The session variable is local to just a single instance
  • B. The session variable is being overwritten in Cloud Datastore
  • C. The URL of the API needs to be modified to prevent caching
  • D. The HTTP Expires header needs to be set to -1 stop caching

  Correct Answer: A

  Explanation:

  The most likely cause of the problem is A. The session variable is local to just a single instance. This is because App Engine applications often run on multiple instances to handle traffic. If the session data is stored only in the memory of a single instance, subsequent requests from the same user might be routed to a different instance that doesn't have the session data, resulting in the user seeing previously viewed articles again. The reason for choosing this answer is that it directly addresses the issue of session management in a distributed environment like App Engine. Session affinity (sticky sessions) can mitigate this, but the question does not imply sticky sessions are in use. Without a shared session store, data is not persisted across instances.

  Here's why the other options are less likely:

  * **B. The session variable is being overwritten in Cloud Datastore:** While Datastore could be used for session storage, the code snippet doesn't explicitly show Datastore usage for sessions. Overwriting would also likely cause a broader issue than just seeing articles again. * **C. The URL of the API needs to be modified to prevent caching:** Modifying the URL is not typically a solution for session inconsistency. Caching issues would usually result in seeing *old* data, not necessarily data the user has already viewed in the same session. * **D. The HTTP Expires header needs to be set to -1 stop caching:** Setting the Expires header relates to browser or proxy caching, not the server-side session issue described. It's about preventing the browser from storing the response, not about maintaining session consistency across App Engine instances. The reasons for not choosing options B, C, and D are that they do not directly address the problem of session data consistency in a multi-instance App Engine environment. They are related to other aspects of web application behavior (data storage, caching), but not the core issue of why a user would see previously viewed content within the same browsing session.

  To resolve this issue, a shared session store such as Memcached or Cloud Datastore should be used to persist session data across all instances.

  • Google Cloud App Engine Documentation on Sessions, https://cloud.google.com/appengine/docs

• Question 5

  An application development team believes their current logging tool will not meet their needs for their new cloud-based product. They want a better tool to capture errors and help them analyze their historical log data. You want to help them find a solution that meets their needs.
  What should you do?
  

  • A. Direct them to download and install the Google StackDriver logging agent
  • B. Send them a list of online resources about logging best practices
  • C. Help them define their requirements and assess viable logging tools
  • D. Help them upgrade their current tool to take advantage of any new features

  Correct Answer: A

  Explanation:

  The best approach is to first understand the team's specific needs before recommending a solution. Therefore, the most suitable answer is C. Here's a detailed breakdown:

  Suggested Answer: C

  Reason for choosing this answer: Option C, "Help them define their requirements and assess viable logging tools," is the most appropriate initial step. Before recommending any specific tool, it's crucial to understand the application development team's specific requirements. This includes the types of logs they need to capture, the volume of log data they expect, their storage and retention needs, their analysis requirements, and any integration needs with existing systems. By helping them define these requirements, you can then assess which logging tools are viable and best suited to their needs. This approach ensures that the chosen solution is a good fit for their cloud-based product.

  Reasons for not choosing the other answers:

  • A: "Direct them to download and install the Google StackDriver logging agent" - This is premature. Recommending a specific tool like Stackdriver (now known as Cloud Logging) without understanding the requirements might lead to choosing a tool that doesn't fully meet their needs or is overly complex for their use case.
  • B: "Send them a list of online resources about logging best practices" - While helpful in the long run, providing general resources doesn't directly address their immediate need for a better logging tool. It's also not proactive in helping them select the right tool.
  • D: "Help them upgrade their current tool to take advantage of any new features" - If their current tool fundamentally doesn't meet their needs, upgrading it may not solve the underlying problem. It's more important to first determine if the tool is even suitable before investing time and effort in upgrading it.

  In conclusion, understanding the requirements before suggesting a solution is the most effective way to address the team's problem.

  • Cloud Logging Documentation, https://cloud.google.com/logging/docs

• Question 6

  You need to reduce the number of unplanned rollbacks of erroneous production deployments in your company's web hosting platform. Improvement to the QA/
  Test processes accomplished an 80% reduction.
  Which additional two approaches can you take to further reduce the rollbacks? (Choose two.)
  

  • A. Introduce a green-blue deployment model
  • B. Replace the QA environment with canary releases
  • C. Fragment the monolithic platform into microservices
  • D. Reduce the platform's dependency on relational database systems
  • E. Replace the platform's relational database systems with a NoSQL database

  Correct Answer: AC

  Explanation:

  The best approach to further reduce unplanned rollbacks, after achieving an 80% reduction through QA/Test improvements, is to implement practices that minimize the impact of deployments and simplify the rollback process if necessary. Therefore, the recommended answer is A and C.

  Here's why:

  A: Introduce a green-blue deployment model: This is a strong choice because green-blue deployments provide a near-instant rollback mechanism. If issues arise after switching traffic to the "green" environment, you can quickly revert to the "blue" environment. This drastically reduces the impact of faulty deployments. The other benefit is the zero downtime deployment.

  C: Fragment the monolithic platform into microservices: This is another good choice because microservices architecture allows for independent deployments of individual services. This reduces the blast radius of a faulty deployment. If one microservice has an issue, it doesn't necessarily impact the entire application. Also, rollback would be less impacting to the entire application.

  Here's why the other options are less suitable:

  B: Replace the QA environment with canary releases: This is incorrect. While canary releases are valuable, they are a deployment testing strategy, not a replacement for a QA environment. QA environments are essential for comprehensive testing before any type of release. This option is attempting to replace a comprehensive testing with a targeted one.

  D: Reduce the platform's dependency on relational database systems: Reducing database dependency might improve performance or scalability in certain situations, but it's not directly related to reducing deployment rollbacks.

  E: Replace the platform's relational database systems with a NoSQL database: Similar to option D, changing the database system might offer other benefits, but it doesn't inherently reduce the risk of deployment rollbacks. It's also a significant architectural change that could introduce new risks.

  Therefore, implementing a green-blue deployment model (A) and fragmenting the platform into microservices (C) can significantly improve deployment safety and reduce rollbacks, and it is the correct answer.

  • Green-Blue Deployments, https://cloud.google.com/solutions/blue-green-deployments
  • Microservices, https://cloud.google.com/learn/what-are-microservices
  • Canary Releases, https://cloud.google.com/solutions/continuous-delivery-canary-deployments

• Question 7

  To reduce costs, the Director of Engineering has required all developers to move their development infrastructure resources from on-premises virtual machines
  (VMs) to Google Cloud Platform. These resources go through multiple start/stop events during the day and require state to persist. You have been asked to design the process of running a development environment in Google Cloud while providing cost visibility to the finance department.
  Which two steps should you take? (Choose two.)
  

  • A. Use the - -no-auto-delete flag on all persistent disks and stop the VM
  • B. Use the - -auto-delete flag on all persistent disks and terminate the VM
  • C. Apply VM CPU utilization label and include it in the BigQuery billing export
  • D. Use Google BigQuery billing export and labels to associate cost to groups
  • E. Store all state into local SSD, snapshot the persistent disks, and terminate the VM
  • F. Store all state in Google Cloud Storage, snapshot the persistent disks, and terminate the VM

  Correct Answer: AD

  Explanation:

  The best approach to address the Director of Engineering's requirements is to combine persistent disks with the appropriate flags and utilize BigQuery billing exports with labels. Therefore, the recommended answers are A and D.

  Suggested Answers: A and D

  Reasoning for choosing A: Using the --no-auto-delete flag on persistent disks ensures that the data persists even when the VM is stopped. This is crucial because the development resources undergo multiple start/stop events during the day, and retaining the state is a requirement. By preventing auto-deletion, you ensure data is preserved across these events. Stopping the VM reduces costs compared to running it continuously.

  Reasoning for choosing D: Google BigQuery billing export, combined with labels, provides the necessary cost visibility. By applying labels to the development infrastructure resources, you can then use BigQuery to analyze the costs associated with those resources, enabling the finance department to track and manage expenses effectively. This provides detailed cost allocation and reporting capabilities.

  Reasoning for not choosing B: Option B suggests using the --auto-delete flag and terminating the VM. This is not suitable because terminating the VM would delete the persistent disk and the data stored on it, which contradicts the requirement for state persistence during start/stop events.

  Reasoning for not choosing C: While applying VM CPU utilization labels could provide some insight, it doesn't offer a comprehensive solution for associating costs to specific groups or providing cost visibility to the finance department. It lacks the detailed reporting capabilities of BigQuery billing exports.

  Reasoning for not choosing E: Storing all state in local SSD is not ideal because local SSDs are ephemeral and do not persist when the VM is stopped or terminated. Snapshotting the persistent disks can be part of a backup strategy but doesn't directly address the need for persistent storage during frequent start/stop events.

  Reasoning for not choosing F: While storing state in Google Cloud Storage (GCS) is a valid option for certain use cases, it's not the most efficient solution for resources that need to be quickly accessed and modified during development. Persistent disks are generally better suited for this purpose. Also, snapshotting the persistent disks can be part of a backup strategy but doesn't directly address the need for persistent storage during frequent start/stop events.

  In summary, options A and D provide a cost-effective and persistent solution for development resources in Google Cloud, while also offering cost visibility to the finance department.

  • Google Compute Engine persistent disks, https://cloud.google.com/compute/docs/disks
  • Export billing data to BigQuery, https://cloud.google.com/billing/docs/how-to/export-data-bigquery

• Question 8

  Your company wants to track whether someone is present in a meeting room reserved for a scheduled meeting. There are 1000 meeting rooms across 5 offices on 3 continents. Each room is equipped with a motion sensor that reports its status every second. The data from the motion detector includes only a sensor ID and several different discrete items of information. Analysts will use this data, together with information about account owners and office locations.
  Which database type should you use?
  

  • A. Flat file
  • B. NoSQL
  • C. Relational
  • D. Blobstore

  Correct Answer: B

  Explanation:

  The suggested answer is B. NoSQL. Here's a detailed explanation:

  Reasoning for choosing NoSQL:

  The scenario involves high-velocity data ingestion from a large number of sensors (1000 meeting rooms reporting every second). This generates a very high volume of data. Additionally, the data includes sensor ID and "several different discrete items of information," suggesting the data might be semi-structured or unstructured. NoSQL databases are designed to handle such high volumes, high velocities, and varied data structures, making them ideal for this use case.

  Furthermore, the analysts will be using this data "together with information about account owners and office locations." NoSQL databases can handle joins although they are not their primary strength, some NoSQL databases are optimized to handle these types of operations. Columnar NoSQL databases or Graph databases could be used.

  The discussion summary highlights that NoSQL databases are well-suited for time-series data and offer the flexibility needed for evolving sensor data schemas. As the data is near real-time, NoSQL would be better compared to the alternative.

  Reasons for not choosing other options:

  • A. Flat file: Flat files are not suitable for high-volume, high-velocity data and lack indexing and querying capabilities needed for analysis. Flat files are hard to query and manage, especially when dealing with data spread across multiple offices and continents.
  • C. Relational: Relational databases are not the best option for near real-time data. They can become bottlenecks when ingesting and processing data at such a high rate. Relational databases require a predefined schema, which can be inflexible when dealing with evolving sensor data. While you *could* make it work, it is not the *best* solution.
  • D. Blobstore: Blobstores are primarily for storing unstructured data like images or videos, not for data that needs to be queried and analyzed. While the sensor data might contain different discrete items of information, it would eventually need to be analyzed using information from different account owners and office locations, which makes Blobstore inappropriate.

  Based on these considerations, NoSQL is the most appropriate database type for this scenario.

  • Choosing the Right Database, https://cloud.google.com/learn/what-is-a-database#choosing-the-right_database

• Question 9

  You set up an autoscaling instance group to serve web traffic for an upcoming launch. After configuring the instance group as a backend service to an HTTP(S) load balancer, you notice that virtual machine (VM) instances are being terminated and re-launched every minute. The instances do not have a public IP address.
  You have verified the appropriate web response is coming from each instance using the curl command. You want to ensure the backend is configured correctly.
  What should you do?
  

  • A. Ensure that a firewall rules exists to allow source traffic on HTTP/HTTPS to reach the load balancer.
  • B. Assign a public IP to each instance and configure a firewall rule to allow the load balancer to reach the instance public IP.
  • C. Ensure that a firewall rule exists to allow load balancer health checks to reach the instances in the instance group.
  • D. Create a tag on each instance with the name of the load balancer. Configure a firewall rule with the name of the load balancer as the source and the instance tag as the destination.

  Correct Answer: C

  Explanation:

  The correct answer is C. Ensure that a firewall rule exists to allow load balancer health checks to reach the instances in the instance group.

  Reasoning: The problem describes a scenario where VMs in an autoscaling instance group are being terminated and relaunched frequently. This behavior is a strong indicator of failing health checks. Load balancers use health checks to determine if an instance is healthy and able to serve traffic. If an instance fails a health check, the load balancer considers it unhealthy and removes it from the pool of available instances, triggering a termination and relaunch in an autoscaling environment. Therefore, ensuring that the load balancer's health checks can reach the instances is crucial for preventing this issue.

  Why other options are incorrect:

  • A. Ensure that a firewall rule exists to allow source traffic on HTTP/HTTPS to reach the load balancer. While this is necessary for users to access the web application, it does not address the problem of instances being terminated and relaunched. The question specifies that the appropriate web response is coming from each instance, indicating that external traffic is not the primary problem.
  • B. Assign a public IP to each instance and configure a firewall rule to allow the load balancer to reach the instance public IP. Assigning public IPs to instances is generally discouraged for security reasons. Internal communication should occur over private networks. The problem also states the instances do not have public IPs, suggesting a private-only network configuration. Moreover, health checks can be performed internally without public IPs.
  • D. Create a tag on each instance with the name of the load balancer. Configure a firewall rule with the name of the load balancer as the source and the instance tag as the destination. Using tags for firewall rules can be a valid approach, but it's not the most direct or standard way to allow health checks. It also doesn't inherently ensure that the health checks are properly configured and reaching the instances. The core issue is that the load balancer health checks needs to reach the instances.

  To reiterate, the health checks are essential for proper load balancer functionality, and Option C directly addresses this requirement, making it the most appropriate solution.

  • Google Cloud Documentation on Health Checks, https://cloud.google.com/load-balancing/docs/health-checks

• Question 10

  You write a Python script to connect to Google BigQuery from a Google Compute Engine virtual machine. The script is printing errors that it cannot connect to
  BigQuery.
  What should you do to fix the script?
  

  • A. Install the latest BigQuery API client library for Python
  • B. Run your script on a new virtual machine with the BigQuery access scope enabled
  • C. Create a new service account with BigQuery access and execute your script with that user
  • D. Install the bq component for gcloud with the command gcloud components install bq.

  Correct Answer: C

  Explanation:

  The best answer is B. Run your script on a new virtual machine with the BigQuery access scope enabled.

  Reasoning:

  The most direct reason the Python script cannot connect to BigQuery from a Compute Engine VM is likely due to insufficient access scopes configured on the VM instance. Access scopes control what Google Cloud services a Compute Engine instance can access. If the BigQuery access scope isn't enabled, the VM won't be able to connect, regardless of IAM permissions. Creating a new service account might address IAM issues, but if the VM itself doesn't have the appropriate scope, the connection will still fail.

  Why other options are not the best:

  • A. Install the latest BigQuery API client library for Python: While having the latest client library is good practice, it doesn't address the underlying issue of the VM's access scope. The script likely already has the library installed, but the connection is being blocked at a lower level.
  • C. Create a new service account with BigQuery access and execute your script with that user: Using a service account is a good security practice for applications. However, creating a service account and granting it BigQuery access will not solve the connection problem if the Compute Engine instance it is running on does not have the BigQuery access scope enabled. The instance itself needs permission to make calls on behalf of the service account.
  • D. Install the bq component for gcloud with the command gcloud components install bq: The `bq` command-line tool is useful for interacting with BigQuery, but it's not directly relevant to a Python script connecting to BigQuery using the API. This would be more relevant if the problem was with using the `bq` command itself from the VM.

  Therefore, the most direct and likely solution is to run the script on a VM with the BigQuery access scope enabled.

  Here are some resources to support this reasoning:

  • Google Cloud Documentation on Service Accounts: https://cloud.google.com/iam/docs/service-accounts
  • Google Cloud Documentation on Compute Engine Access Scopes: https://cloud.google.com/compute/docs/access-scopes