Free Professional-Cloud-Architect Practice Exam Questions

Topic 1, Mountkirk Games Case Study

Company Overview
Mountkirk Games makes online, session-based. multiplayer games for the most popular mobile platforms.
Company Background
Mountkirk Games builds all of their games with some server-side integration and has historically used cloud
providers to lease physical servers. A few of their games were more popular than expected, and they had
problems scaling their application servers, MySQL databases, and analytics tools.
Mountkirk's current model is to write game statistics to files and send them through an ETL tool that loads
them into a centralized MySQL database for reporting.
Solution Concept
Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the
game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics and
take advantage of its autoscaling server environment and integrate with a managed NoSQL database.
Technical Requirements
Requirements for Game Backend Platform
1. Dynamically scale up or down based on game activity.
2. Connect to a managed NoSQL database service.
3. Run customized Linx distro.
Requirements for Game Analytics Platform
1. Dynamically scale up or down based on game activity.
2. Process incoming data on the fly directly from the game servers
3. Process data that arrives late because of slow mobile networks.
4. Allow SQL queries to access at least 10 TB of historical data.
5. Process files that are regularly uploaded by users' mobile devices.
6. Use only fully managed services
CEO Statement
Our last successful game did not scale well with our previous cloud provider, resuming in lower user adoption
and affecting the game’s reputation. Our investors want more key performance indicators (KPIs) to evaluate
the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so
we can adapt the gams to target users.
CTO Statement
Our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an
environment that provides autoscaling, low latency load balancing, and frees us up from managing physical
servers.
CFO Statement
We are not capturing enough user demographic data usage metrics, and other KPIs. As a result, we do not
engage the right users. We are not confident that our marketing is targeting the right users, and we are not
selling enough premium Blast-Ups inside the games, which dramatically impacts our revenue.

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?

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.

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

For this question, refer to the TerramEarth case study.
TerramEarth's CTO wants to use the raw data from connected vehicles to help identify approximately when a
vehicle in the development team to focus their failure. You want to allow analysts to centrally query the
vehicle data. Which architecture should you recommend?

Option A

Option B

Option C

Option D

Option A

https://cloud.google.com/solutions/iot/
https://cloud.google.com/solutions/designing-connected-vehicle-platform
https://cloud.google.com/solutions/designing-connected-vehicle-platform#data_ingestion
http://www.eweek.com/big-data-and-analytics/google-touts-value-of-cloud-iot-core-for-analyzing-connected-car-data
https://cloud.google.com/solutions/iot/

For this question refer to the TerramEarth case study
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?

Have your 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.

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

Question #:9 - (Exam Topic 2)
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?

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.

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

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?

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
regional bucket and use a Cloud Dataproc cluster …..

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Professional-Cloud-Architect Practice Test