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Case Study 2 - MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to- many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
* Ensure secure and efficient transport and storage of telemetry data
* Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
* Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day
* Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

• A. Rowkey: device_id
  Column data: date, data_point
• B. Rowkey: date
  Column data: device_id, data_point
• C. Rowkey: data_point
  Column data: device_id, date
• D. Rowkey: date#data_point
  Column data: device_id
• E. Rowkey: date#device_id
  Column data: data_point

Cloud Bigtable is Google's ______ Big Data database service.

• A. mySQL
• B. NoSQL
• C. Relational
• D. SQL Server

You work for a farming company. You have one BigQuery table named sensors, which is about
500 MB and contains the list of your 5000 sensors, with columns for id, name, and location. This table is updated every hour. Each sensor generates one metric every 30 seconds along with a timestamp, which you want to store in BigQuery. You want to run an analytical query on the data once a week for monitoring purposes. You also want to minimize costs. What data model should you use?

• A. 1. Create a metrics table partitioned by timestamp.
  2. Create a sensorId column in the metrics table, which points to the id column in the sensors table.
  3. Use an UPDATE statement every 30 seconds to append new metrics to the metrics table.
  4. Join the two tables, if needed, when running the analytical query.
• B. 1. Create a metrics table partitioned by timestamp.
  2. Create a sensorId column in the metrics table, that points to the id column in the sensors table.
  3. Use an INSERT statement every 30 seconds to append new metrics to the metrics table.
  4. Join the two tables, if needed, when running the analytical query.
• C. 1. Create a metrics column in the sensors table.
  2. Set RECORD type and REPEATED mode for the metrics column.
  3. Use an UPDATE statement every 30 seconds to add new metrics.
• D. 1. Create a metrics column in the sensors table.
  2. Set RECORD type and REPEATED mode for the metrics column.
  3. Use an INSERT statement every 30 seconds to add new metrics.

You are building a real-time prediction engine that streams files, which may contain personally identifiable information (PII) data, into Cloud Storage and eventually into BigQuery. You want to ensure that the sensitive data is masked but still maintains referential integrity, because names and emails are often used as join keys. How should you use the Cloud Data Loss Prevention (Cloud DLP) API to ensure that the PII data is not accessible by unauthorized individuals?

• A. Create a pseudonym by replacing the Pll data with cryptogenic tokens, and store the non- tokenized data in a locked-down button.
• B. Redact all Pll data, and store a version of the unredacted data in a locked-down bucket
• C. Create a pseudonym by replacing Pll data with a cryptographic format-preserving token
• D. Scan every table in BigQuery, and mask the data it finds that has Pll

Which of the following is NOT a valid use case to select HDD (hard disk drives) as the storage for Google Cloud Bigtable?

• A. You will mostly run batch workloads with scans and writes, rather than frequently executing random reads of a small number of rows.
• B. You expect to store at least 10 TB of data.
• C. You will not use the data to back a user-facing or latency-sensitive application.
• D. You need to integrate with Google BigQuery.