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NEW QUESTION: 1
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. 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.)
A. Adopt a schema versioning tool to reduce downtime when adding new game features that require storing additional player data in the database.
B. 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.
C. 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.
D. Begin packaging their game backend artifacts in container images and running them on Kubernetes Engine to improve the availability to scale up or down based on game activity.
E. Set up a CI/CD pipeline using Jenkins and Spinnaker to automate canary deployments and improve development velocity.
Answer: B,E

NEW QUESTION: 2
You have an Apache Storm cluster.
You need to ingest data from a Kafka queue.
Which component should you use to consume data emitted from Kaka?
A. Flume
B. a Microsoft Azure Service Bus queue
C. a spout
D. a bolt
Answer: C
Explanation:
Explanation
To perform real-time computation on Storm, we create "topologies." A topology is a graph of a computation, containing a network of nodes called "Spouts" and "Bolts." In a Storm topology, a Spout is the source of data streams and a Bolt holds the business logic for analyzing and processing those streams.
The org.apache.storm.kafka.KafkaSpout component reads data from Kafka.
Example:

References:
https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-apache-storm-with-kafka
https://hortonworks.com/blog/storm-kafka-together-real-time-data-refinery/

NEW QUESTION: 3
Welche Aussage ist richtig, wenn das Recht auf Privatsphäre nach Artikel 8 der Europäischen Menschenrechtskonvention (EMRK) geprüft wird?
A. Das Recht auf Privatsphäre muss gegen andere Rechte nach der EMRK abgewogen werden
B. Das Recht auf Privatsphäre schützt das Recht, Meinungen zu vertreten und Ideen ohne Einmischung zu empfangen und weiterzugeben
C. Das Recht auf freie Meinungsäußerung nach Artikel 10 EMRK hat immer Vorrang vor dem Recht auf Privatsphäre
D. Das Recht auf Privatsphäre ist ein absolutes Recht
Answer: A