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NEW QUESTION 26
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now
automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to
query all of the tables for the past 30 days in legacy SQL. What should you do?

• A. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
• B. Use the WHERE_PARTITIONTIME pseudo column
• C. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
• D. Use the TABLE_DATE_RANGE function

Answer: D

NEW QUESTION 27
MJTelco Case Study
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#data_point
  Column data: device_id
• C. Rowkey: data_point
  Column data: device_id,date
• D. Rowkey: date#device_id
  Column data: data_point
• E. Rowkey: date
  Column data: device_id,data_point

Answer: C

NEW QUESTION 28
You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?

• A. Create an initialization action to execute the jobs
• B. Create a Cloud Dataproc Workflow Template
• C. Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster
• D. Create a Directed Acyclic Graph in Cloud Composer

Answer: B

Explanation:
https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows

NEW QUESTION 29
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