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For this question, refer to the TerramEarth case study.

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?

A.

Vehicles write data directly to GCS.

B.

Vehicles write data directly to Google Cloud Pub/Sub.

C.

Vehicles stream data directly to Google BigQuery.

D.

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

Your agricultural division is experimenting with fully autonomous vehicles.

You want your architecture to promote strong security during vehicle operation.

Which two architecture should you consider?

Choose 2 answers:

A.

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

B.

Require IPv6 for connectivity to ensure a secure address space.

C.

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

D.

Use a functional programming language to isolate code execution cycles.

E.

Use multiple connectivity subsystems for redundancy.

F.

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

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?

A.

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

B.

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

C.

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

D.

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

For this question, refer to the TerramEarth case study.

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?

A.

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

B.

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

C.

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.

D.

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 jo

For this question refer to the TerramEarth case study.

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

A.

Opex/capex allocation, LAN changes, capacity planning

B.

Capacity planning, TCO calculations, opex/capex allocation

C.

Capacity planning, utilization measurement, data center expansion

D.

Data Center expansion, TCO calculations, utilization measurement

For this question, refer to the TerramEarth case study.

TerramEarth has equipped unconnected trucks with servers and sensors to collet 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?

A.

Have the vehicle’ computer compress the data in hourly snapshots, and store it in a Google Cloud storage (GCS) Nearline bucket.

B.

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

C.

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

D.

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

For this question, refer to the Mountkirk Games case study.

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

A.

Tests should scale well beyond the prior approaches.

B.

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

C.

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

D.

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

For this question, refer to the Mountkirk Games case study.

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 Europe.

• Only frontend services are exposed on the public internet.

• They can provide a single frontend IP for their fleet of services.

• Deployment artifacts are immutable.

Which set of products should they use?

A.

Google Cloud Storage, Google Cloud Dataflow, Google Compute Engine

B.

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

C.

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

D.

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

For this question, refer to the Mountkirk Games case study.

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?

A.

Container Engine, Cloud Pub/Sub, and Cloud SQL

B.

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

C.

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

D.

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

E.

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

For this question, refer to the Mountkirk Games case study.

Mountkirk Games has deployed their new backend on Google Cloud Platform (GCP). You want to create a thorough 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?

A.

Create a scalable environment in GCP for simulating production load.

B.

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

C.

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

D.

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