A news teed 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 to stop caching.

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Case Study: 7 - Mountkirk Games
Company Overview
Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They build all of their games using some server-side integration. Historically, they have used cloud providers to lease physical servers.
Due to the unexpected popularity of some of their games, they have had problems scaling their global audience, application servers, MySQL databases, and analytics tools.
Their 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.
Business Requirements
Increase to a global footprint.

Improve uptime - downtime is loss of players.

Increase efficiency of the cloud resources we use.

Reduce latency to all customers.

Technical Requirements
Requirements for Game Backend Platform
Dynamically scale up or down based on game activity.

Connect to a transactional database service to manage user profiles and game state.

Store game activity in a timeseries database service for future analysis.

As the system scales, ensure that data is not lost due to processing backlogs.

Run hardened Linux distro.

Requirements for Game Analytics Platform
Dynamically scale up or down based on game activity

Process incoming data on the fly directly from the game servers

Process data that arrives late because of slow mobile networks

Allow queries to access at least 10 TB of historical data

Process files that are regularly uploaded by users' mobile devices

Executive Statement
Our last successful game did not scale well with our previous cloud provider, resulting 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 game to target users.
Additionally, 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.
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?

• A.Create an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L7 load balancer.
• B.Create an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L4 load balancer.
• C.Create an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L7 load balancer.
• D.Create an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L4 load balancer.

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Your company is forecasting a sharp increase in the number and size of Apache Spark and Hadoop jobs being run on your local datacenter You want to utilize the cloud to help you scale this upcoming demand with the least amount of operations work and code change. Which product should you use?

• A.Google Cloud Dataflow
• B.Google Cloud Dataproc
• C.Google Compute Engine
• D.Google Container Engine

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You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also to reliably deploy new versions of the application. Which set of steps should you take?

• A.Perform the following:
  1) Create a managed instance group with f1-micro type machines.
  2) Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.
  3) Restart the instances to automatically deploy new production releases.
• B.Perform the following:
  1) Create a managed instance group with n1-standard-1 type machines.
  2) Build a Compute Engine image from the production branch that contains all of the dependencies and automatically starts the Python app.
  3) Rebuild the Compute Engine image, and update the instance template to deploy new production releases.
• C.Perform the following:
  1) Create a Kubernetes Engine cluster with n1-standard-1 type machines.
  2) Build a Docker image from the production branch with all of the dependencies, and tag it with the
  3) Create a Kubernetes Deployment with the imagePullPolicy set to "IfNotPresent" in the staging namespace, and then promote it to the production namespace after testing.
• D.Perform the following:
  1) Create a Kubernetes Engine cluster with n1-standard-4 type machines.
  2) Build a Docker image from the master branch will all of the dependencies, and tag it with "latest".
  3) Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to "Always".
  Restart the pods to automatically deploy new production releases.

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

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