Mountkirk Games wants you to design their new testing strategy. How should the test coverage differ from their existing backends on the other platforms?

Tests should scale well beyond the prior approaches

Unit tests are no longer required, only end-to-end tests

Tests should be applied after the release is in the production environment

Tests should include directly testing the Google Cloud Platform (GCP) infrastructure

Mountkirk Games has deployed their new backend on Google Cloud Platform (GCP). You want to create a through testing process for new versions of the backend before they are released to the public. You want the testing environment to scale in an economical way. How should you design the process?

Create a scalable environment in GCP for simulating production load

Use the existing infrastructure to test the GCP-based backend at scale

Build stress tests into each component of your application using resources internal to GCP to simulate load

Create a set of static environments in GCP to test different levels of load - for example, high, medium, and low

Mountkirk Games wants to set up a continuous delivery pipeline. Their architecture includes many small services that they want to be able to update and roll back quickly. Mountkirk Games has the following requirements:Services are deployed redundantly across multiple regions in the US and EuropeOnly frontend services are exposed on the public internetThey can provide a single frontend IP for their fleet of servicesDeployment artifacts are immutableWhich set of products should they use?

Google Kubernetes Registry, Google Container Engine, Google HTTP(S) Load Balancer

Google Cloud Storage, Google Cloud Dataflow, Google Compute Engine

Google Cloud Storage, Google App Engine, Google Network Load Balancer

Google Kubernetes Registry, Google Container Engine, Google HTTP(S) Load Balancer

Google Cloud Functions, Google Cloud Pub/Sub, Google Cloud Deployment Manager

Mountkirk Games' gaming servers are not automatically scaling properly. Last month, they rolled out a new feature, which suddenly became very popular. A record number of users are trying to use the service, but many of them are getting 503 errors and very slow response times. What should they investigate first?

Verify that the project quota hasn't been exceeded

Verify that the database is online

Verify that the project quota hasn't been exceeded

Verify that the new feature code did not introduce any performance bugs

Verify that the load-testing team is not running their tool against production

Mountkirk Games needs to create a repeatable and configurable mechanism for deploying isolated application environments. Developers and testers can access each other's environments and resources, but they cannot access staging or production resources. The staging environment needs access to some services from production.What should you do to isolate development environments from staging and production?

Create one project for development, a second for staging and a third for production

Create a project for development and test and another for staging and production

Create a network for development and test and another for staging and production

Create one subnetwork for development and another for staging and production

Create one project for development, a second for staging and a third for production

Mountkirk Games wants to set up a real-time analytics platform for their new game. The new platform must meet their technical requirements.Which combination of Google technologies will meet all of their requirements?

Cloud Dataflow, Cloud Storage, Cloud Pub/Sub, and BigQuery

Kubernetes Engine, Cloud Pub/Sub, and Cloud SQL

Cloud Dataflow, Cloud Storage, Cloud Pub/Sub, and BigQuery

Cloud SQL, Cloud Storage, Cloud Pub/Sub, and Cloud Dataflow

Cloud Dataproc, Cloud Pub/Sub, Cloud SQL, and Cloud Dataflow

Cloud Pub/Sub, Compute Engine, Cloud Storage, and Cloud Dataproc

For this question, refer to the Mountkirk Games case study. Mountkirk Games wants to migrate from their current analytics and statistics reporting model to one that meets their technical requirements on Google Cloud Platform.Which two steps should be part of their migration plan? (Choose two.)

Evaluate the impact of migrating their current batch ETL code to Cloud Dataflow.

Write a schema migration plan to denormalize data for better performance in BigQuery.

Evaluate the impact of migrating their current batch ETL code to Cloud Dataflow.

Write a schema migration plan to denormalize data for better performance in BigQuery.

Draw an architecture diagram that shows how to move from a single MySQL database to a MySQL cluster.

Load 10 TB of analytics data from a previous game into a Cloud SQL instance, and run test queries against the full dataset to confirm that they complete successfully.

Integrate Cloud Armor to defend against possible SQL injection attacks in analytics files uploaded to Cloud Storage.

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

Create a global load balancer with managed instance groups and autoscaling policies. Use non-preemptible Compute Engine instances.

Create network load balancers. Use preemptible Compute Engine instances.

Create network load balancers. Use non-preemptible Compute Engine instances.

Create a global load balancer with managed instance groups and autoscaling policies. Use preemptible Compute Engine instances.

Create a global load balancer with managed instance groups and autoscaling policies. Use non-preemptible Compute Engine instances.

For this question, refer to the Mountkirk Games case study. Mountkirk Games wants to design their solution for the future in order to take advantage of cloud and technology improvements as they become available. Which two steps should they take? (Choose two.)

Set up a CI/CD pipeline using Jenkins and Spinnaker to automate canary deployments and improve development velocity.

Implement a weekly rolling maintenance process for the Linux virtual machines so they can apply critical kernel patches and package updates and reduce the risk of 0-day vulnerabilities.

Store as much analytics and game activity data as financially feasible today so it can be used to train machine learning models to predict user behavior in the future.

Begin packaging their game backend artifacts in container images and running them on Google Engine to improve the ability to scale up or down based on game activity.

Set up a CI/CD pipeline using Jenkins and Spinnaker to automate canary deployments and improve development velocity.

Adopt a schema versioning tool to reduce downtime when adding new game features that require storing additional player data in the database.

Implement a weekly rolling maintenance process for the Linux virtual machines so they can apply critical kernel patches and package updates and reduce the risk of 0-day vulnerabilities.

For this question, refer to the Mountkirk Games case study. Mountkirk Games wants you to design a way to test the analytics platform's resilience to changes in mobile network latency. What should you do?

Add the ability to introduce a random amount of delay before beginning to process analytics files uploaded from mobile devices.

Deploy failure injection software to the game analytics platform that can inject additional latency to mobile client analytics traffic.

Build a test client that can be run from a mobile phone emulator on a Compute Engine virtual machine, and run multiple copies in Google Cloud Platform regions all over the world to generate realistic traffic.

Add the ability to introduce a random amount of delay before beginning to process analytics files uploaded from mobile devices.

Create an opt-in beta of the game that runs on players' mobile devices and collects response times from analytics endpoints running in Google Cloud Platform regions all over the world.

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?

Use Cloud Bigtable to replace MySQL, and use BigQuery for historical data queries.

Use Cloud SQL for time series data, and use Cloud Bigtable for historical data queries.

Use Cloud SQL to replace MySQL, and use Cloud Spanner for historical data queries.

Use Cloud Bigtable to replace MySQL, and use BigQuery for historical data queries.

Use Cloud Bigtable for time series data, use Cloud Spanner for transactional data, and use BigQuery for historical data queries.

For this question, refer to the Mountkirk Games case study. You are in charge of the new Game Backend Platform architecture. The game communicates with the backend over a REST API.You want to follow Google-recommended practices. How should you design the backend?

Create an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L4 load balancer.

Create an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L4 load balancer.

Create an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L7 load balancer.

Create an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L7 load balancer.

You need to optimize batch file transfers into Cloud Storage for Mountkirk Games' new Google Cloud solution. The batch files contain game statistics that need to be staged in Cloud Storage and be processed by an extract transform load (ETL) tool. What should you do?

Use gsutil to batch copy the files in parallel.

Use gsutil to batch move files in sequence.

Use gsutil to batch copy the files in parallel.

Use gsutil to extract the files as the first part of ETL.

Use gsutil to load the files as the last part of ETL.

You are implementing Firestore for Mountkirk Games. Mountkirk Games wants to give a new game programmatic access to a legacy game's Firestore database. Access should be as restricted as possible. What should you do?

Create a service account (SA) in the legacy game's Google Cloud project, add this SA in the new game's IAM page, and then give it the Firebase Admin role in both projects.

Create a service account (SA) in the legacy game's Google Cloud project, add a second SA in the new game's IAM page, and then give the Organization Admin role to both SAs.

Create a service account (SA) in the legacy game's Google Cloud project, give the SA the Organization Admin role, and then give it the Firebase Admin role in both projects.

Create a service account (SA) in the legacy game's Google Cloud project, add this SA in the new game's IAM page, and then give it the Firebase Admin role in both projects.

Create a service account (SA) in the legacy game's Google Cloud project, give it the Firebase Admin role, and then migrate the new game to the legacy game's project.

Mountkirk Games wants to limit the physical location of resources to their operating Google Cloud regions. What should you do?

Configure the quotas for resources in the regions not being used to 0.

Configure an organizational policy which constrains where resources can be deployed.

Configure IAM conditions to limit what resources can be configured.

Configure the quotas for resources in the regions not being used to 0.

Configure a custom alert in Cloud Monitoring so you can disable resources as they are created in other regions.

You need to implement a network ingress for a new game that meets the defined business and technical requirements. Mountkirk Games wants each regional game instance to be located in multiple Google Cloud regions. What should you do?

Configure a global load balancer connected to a managed instance group running Compute Engine instances.

Configure kubemci with a global load balancer and Google Kubernetes Engine.

Configure a global load balancer with Google Kubernetes Engine.

Configure Ingress for Anthos with a global load balancer and Google Kubernetes Engine.

Your development teams release new versions of games running on Google Kubernetes Engine (GKE) daily. You want to create service level indicators (SLIs) to evaluate the quality of the new versions from the user's perspective. What should you do?

Create CPU Utilization and Request Latency as service level indicators.

Create GKE CPU Utilization and Memory Utilization as service level indicators.

Create Request Latency and Error Rate as service level indicators.

Create Server Uptime and Error Rate as service level indicators.

Mountkirk Games wants you to secure the connectivity from the new gaming application platform to Google Cloud. You want to streamline the process and follow Google-recommended practices. What should you do?

Configure Workload Identity and service accounts to be used by the application platform.

Use Kubernetes Secrets, which are obfuscated by default. Configure these Secrets to be used by the application platform.

Configure Kubernetes Secrets to store the secret, enable Application-Layer Secrets Encryption, and use Cloud Key Management Service (Cloud KMS) to manage the encryption keys. Configure these Secrets to be used by the application platform.

Configure HashiCorp Vault on Compute Engine, and use customer managed encryption keys and Cloud Key Management Service (Cloud KMS) to manage the encryption keys. Configure these Secrets to be used by the application platform.

Your development team has created a mobile game app. You want to test the new mobile app on Android and iOS devices with a variety of configurations. You need to ensure that testing is efficient and cost-effective. What should you do?

Create Android and iOS containers on Google Kubernetes Engine (GKE), install the mobile app on the containers, and test the mobile app.

Upload your mobile app to the Firebase Test Lab, and test the mobile app on Android and iOS devices.

Create Android and iOS VMs on Google Cloud, install the mobile app on the VMs, and test the mobile app.

Create Android and iOS containers on Google Kubernetes Engine (GKE), install the mobile app on the containers, and test the mobile app.

Upload your mobile app with different configurations to Firebase Hosting and test each configuration.

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?

Use Google App Engine with Google Cloud Endpoints. Focus on an API for dealers and partners

Use Google App Engine with a JAX-RS Jersey Java-based framework. Focus on an API for the public

Use Google App Engine with the Swagger (Open API Specification) framework. Focus on an API for the public

Use Google Container Engine with a Django Python container. Focus on an API for the public

Use Google Container Engine with a Tomcat container with the Swagger (Open API Specification) framework. Focus on an API for dealers and partners

Your development team has created a structured API to retrieve vehicle data. They want to allow third parties to develop tools for dealerships that use this vehicle event data. You want to support delegated authorization against this data.What should you do?

Build or leverage an OAuth-compatible access control system

Build SAML 2.0 SSO compatibility into your authentication system

Restrict data access based on the source IP address of the partner systems

Create secondary credentials for each dealer that can be given to the trusted third party

TerramEarth plans to connect all 20 million vehicles in the field to the cloud. This increases the volume to 20 million 600 byte records a second for 40 TB an hour.How should you design the data ingestion?

Vehicles stream data directly to Google BigQuery

Vehicles write data directly to GCS

Vehicles write data directly to Google Cloud Pub/Sub

Vehicles stream data directly to Google BigQuery

Vehicles continue to write data using the existing system (FTP)

You analyzed TerramEarth's business requirement to reduce downtime, and found that they can achieve a majority of time saving by reducing customer's 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?

Increase fleet cellular connectivity to 80%, migrate from FTP to streaming transport, and develop machine learning analysis of metrics

Migrate from CSV to binary format, migrate from FTP to SFTP transport, and develop machine learning analysis of metrics

Migrate from FTP to streaming transport, migrate from CSV to binary format, and develop machine learning analysis of metrics

Increase fleet cellular connectivity to 80%, migrate from FTP to streaming transport, and develop machine learning analysis of metrics

Migrate from FTP to SFTP transport, develop machine learning analysis of metrics, and increase dealer local inventory by a fixed factor

Which of TerramEarth's legacy enterprise processes will experience significant change as a result of increased Google Cloud Platform adoption?

Capacity planning, TCO calculations, opex/capex allocation

Opex/capex allocation, LAN changes, capacity planning

Capacity planning, TCO calculations, opex/capex allocation

Capacity planning, utilization measurement, data center expansion

Data Center expansion, TCO calculations, utilization measurement

To speed up data retrieval, more vehicles will be upgraded to cellular connections and be able to transmit data to the ETL process. The current FTP process is error-prone and restarts the data transfer from the start of the file when connections fail, which happens often. You want to improve the reliability of the solution and minimize data transfer time on the cellular connections.What should you do?

Directly transfer the files to a different Google Cloud Regional Storage bucket location in US, EU, and Asia using Google APIs over HTTP(S). Run the ETL process to retrieve the data from each Regional bucket

Use one Google Container Engine cluster of FTP servers. Save the data to a Multi-Regional bucket. Run the ETL process using data in the bucket

Use multiple Google Container Engine clusters running FTP servers located in different regions. Save the data to Multi-Regional buckets in US, EU, and Asia. Run the ETL process using the data in the bucket

Directly transfer the files to different Google Cloud Multi-Regional Storage bucket locations in US, EU, and Asia using Google APIs over HTTP(S). Run the ETL process using the data in the bucket

Directly transfer the files to a different Google Cloud Regional Storage bucket location in US, EU, and Asia using Google APIs over HTTP(S). Run the ETL process to retrieve the data from each Regional bucket

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?

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 job

Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job

Move all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the job

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 job

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 job

TerramEarth has equipped all connected trucks with servers and sensors to collect telemetry data. Next year they want to use the data to train machine learning models. They want to store this data in the cloud while reducing costs.What should they do?

Have the vehicle's computer compress the data in hourly snapshots, and store it in a GCS Coldline bucket

Have the vehicle's computer compress the data in hourly snapshots, and store it in a Google Cloud Storage (GCS) Nearline bucket

Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery

Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable

Have the vehicle's computer compress the data in hourly snapshots, and store it in a GCS Coldline bucket

Your agricultural division is experimenting with fully autonomous vehicles. You want your architecture to promote strong security during vehicle operation. Which two architectures should you consider? (Choose two.)

Treat every micro service call between modules on the vehicle as untrusted.

Use a trusted platform module (TPM) and verify firmware and binaries on boot.

Treat every micro service call between modules on the vehicle as untrusted.

Require IPv6 for connectivity to ensure a secure address space.

Use a trusted platform module (TPM) and verify firmware and binaries on boot.

Use a functional programming language to isolate code execution cycles.

Use multiple connectivity subsystems for redundancy.

Enclose the vehicle's drive electronics in a Faraday cage to isolate chips.

Operational parameters such as oil pressure are adjustable on each of TerramEarth's vehicles to increase their efficiency, depending on their environmental conditions. Your primary goal is to increase the operating efficiency of all 20 million cellular and unconnected vehicles in the field.How can you accomplish this goal?

Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically

Have you engineers inspect the data for patterns, and then create an algorithm with rules that make operational adjustments automatically

Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically

Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) to make operational adjustments automatically

Capture all operating data, train machine learning models that identify ideal operations, and host in Google Cloud Machine Learning (ML) Platform to make operational adjustments automatically

For this question, refer to the TerramEarth case study. To be compliant with European GDPR regulation, TerramEarth is required to delete data generated from its European customers after a period of 36 months when it contains personal data. In the new architecture, this data will be stored in both Cloud Storage and BigQuery. What should you do?

Create a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.

Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.

Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action when with an Age condition of 36 months.

Create a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.

Create a BigQuery time-partitioned table for the European data, and set the partition period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action with an Age condition of 36 months.

For this question, refer to the TerramEarth case study. TerramEarth has decided to store data files in Cloud Storage. You need to configure Cloud Storage lifecycle rule to store 1 year of data and minimize file storage cost. Which two actions should you take?

Create a Cloud Storage lifecycle rule with Age: "30", Storage Class: "Standard", and Action: "Set to Coldline", and create a second GCS life-cycle rule with Age: "365", Storage Class: "Coldline", and Action: "Delete".

Create a Cloud Storage lifecycle rule with Age: "30", Storage Class: "Coldline", and Action: "Set to Nearline", and create a second GCS life-cycle rule with Age: "91", Storage Class: "Coldline", and Action: "Set to Nearline".

Create a Cloud Storage lifecycle rule with Age: "90", Storage Class: "Standard", and Action: "Set to Nearline", and create a second GCS life-cycle rule with Age: "91", Storage Class: "Nearline", and Action: "Set to Coldline".

Create a Cloud Storage lifecycle rule with Age: "30", Storage Class: "Standard", and Action: "Set to Coldline", and create a second GCS life-cycle rule with Age: "365", Storage Class: "Nearline", and Action: "Delete".

For this question, refer to the TerramEarth case study. You need to implement a reliable, scalable GCP solution for the data warehouse for your company, TerramEarth. Considering the TerramEarth business and technical requirements, what should you do?

Replace the existing data warehouse with BigQuery. Use table partitioning.

Replace the existing data warehouse with a Compute Engine instance with 96 CPUs.

Replace the existing data warehouse with BigQuery. Use federated data sources.

Replace the existing data warehouse with a Compute Engine instance with 96 CPUs. Add an additional Compute Engine pre-emptible instance with 32 CPUs.

For this question, refer to the TerramEarth case study. A new architecture that writes all incoming data to BigQuery has been introduced. You notice that the data is dirty, and want to ensure data quality on an automated daily basis while managing cost.What should you do?

Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.

Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.

Create a Cloud Function that reads data from BigQuery and cleans it. Trigger the Cloud Function from a Compute Engine instance.

Create a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.

Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.

For this question, refer to the TerramEarth case study. Considering the technical requirements, how should you reduce the unplanned vehicle downtime in GCP?

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.

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.

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.

Use Cloud Dataproc Hive as the data warehouse. Directly stream data into partitioned Hive tables. Use Pig scripts to analyze data.

For this question, refer to the TerramEarth case study. You need to implement a reliable, scalable GCP solution for the data warehouse for your company, TerramEarth.Considering the TerramEarth business and technical requirements, what should you do?

Replace the existing data warehouse with BigQuery. Use table partitioning.

Replace the existing data warehouse with a Compute Engine instance with 96 CPUs.

Replace the existing data warehouse with BigQuery. Use federated data sources.

Replace the existing data warehouse with a Compute Engine instance with 96 CPUs. Add an additional Compute Engine pre-emptible instance with 32 CPUs.

For this question, refer to the TerramEarth case study. You are asked to design a new architecture for the ingestion of the data of the 200,000 vehicles that are connected to a cellular network. You want to follow Google-recommended practices.Considering the technical requirements, which components should you use for the ingestion of the data?

Cloud IoT Core with public/private key pairs

Google Kubernetes Engine with an SSL Ingress

Cloud IoT Core with public/private key pairs

Compute Engine with project-wide SSH keys

Compute Engine with specific SSH keys

For this question, refer to the TerramEarth case study. You start to build a new application that uses a few Cloud Functions for the backend. One use case requires a Cloud Function func_display to invoke another Cloud Function func_query. You want func_query only to accept invocations from func_display. You also want to follow Google's recommended best practices. What should you do?

Make func_query 'Require authentication.' Create a unique service account and associate it to func_display. Grant the service account invoker role for func_query. Create an id token in func_display and include the token to the request when invoking func_query.

Create a token and pass it in as an environment variable to func_display. When invoking func_query, include the token in the request. Pass the same token to func_query and reject the invocation if the tokens are different.

Make func_query 'Require authentication.' Create a unique service account and associate it to func_display. Grant the service account invoker role for func_query. Create an id token in func_display and include the token to the request when invoking func_query.

Make func_query 'Require authentication' and only accept internal traffic. Create those two functions in the same VPC. Create an ingress firewall rule for func_query to only allow traffic from func_display.

Create those two functions in the same project and VPC. Make func_query only accept internal traffic. Create an ingress firewall for func_query to only allow traffic from func_display. Also, make sure both functions use the same service account.

Google Professional Cloud Architect Practice Test 7 - Free Online Exam Prep & Questions

You are managing several internal applications that are deployed on Compute Engine. Business users inform you that an application has become very slow over the past few days. You want to find the underlying cause in order to solve the problem. What should you do first?

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