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NEW QUESTION 50
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
* Decoupling producer from consumer
* Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
* Near real-time SQL query
* Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

• A. Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
• B. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
• C. Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
• D. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.

Answer: C

NEW QUESTION 51
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.
You need to compose visualization for operations teams with the following requirements:
* Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)
* The report must not be more than 3 hours delayed from live data.
* The actionable report should only show suboptimal links.
* Most suboptimal links should be sorted to the top.
* Suboptimal links can be grouped and filtered by regional geography.
* User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

• A. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
• B. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
• C. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
• D. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.

Answer: D

NEW QUESTION 52
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

• A. Convert the streaming insert code to batch load for individual messages.
• B. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
• C. Re-write the application to load accumulated data every 2 minutes.
• D. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.

Answer: B

NEW QUESTION 53
You want to rebuild your batch pipeline for structured data on Google Cloud You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run To expedite development and pipeline run time, you want to use a serverless tool and SQL syntax You have already moved your raw data into Cloud Storage How should you build the pipeline on Google Cloud while meeting speed and processing requirements?

• A. Ingest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQL
  queries to transform the data, and then write the transformations to a new table
• B. Use Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery
• C. Ingest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the
  data, and then use federated queries from BigQuery for machine learning.
• D. Convert your PySpark commands into SparkSQL queries to transform the data; and then run your pipeline
  on Dataproc to write the data into BigQuery

Answer: D

NEW QUESTION 54
Does Dataflow process batch data pipelines or streaming data pipelines?

• A. Only Streaming Data Pipelines
• B. None of the above
• C. Only Batch Data Pipelines
• D. Both Batch and Streaming Data Pipelines

Answer: D

Explanation:
Dataflow is a unified processing model, and can execute both streaming and batch data pipelines

NEW QUESTION 55
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