Practice Test 3 | Google Cloud Certified Professional Cloud Architect | Dumps | Mock Test
You are asked to design the next generation of smart helmet for accident detection and reporting system. Each helmet will push 10kb of biometric data In JSON format every 1 second to a collection platform that will process and use a trained machine learning model to predict and detect if an accident happens and send a notification. Management has tasked you to architect the platform ensuring the following requirements are met:
· Provide the ability for real-time analytics of the inbound biometric data
· Ensure the processing of the biometric data is highly durable. Elastic and parallel
· The results of the analytic processing should be persisted for data mining to improve the accident detection ML model in the future.
Which architecture outlined below meet the initial requirements for the platform?
A. Utilize Cloud Storage to collect the inbound sensor data, analyze data with Dataproc and save the results to BigQuery.
B. Utilize Cloud Pub/Sub to collect the inbound sensor data, process the data with DataFlow and save the results to BigQuery for further analysis.
C. Utilize Cloud Pub/Sub to collect the inbound sensor data, analyze the data with DataFlow and save the results to Cloud SQL.
D. Utilize Cloud Pub/Sub to collect the inbound sensor data, analyze the data with DataFlow and save the results to BigTable.
Correct Answer B
Answer B meet all of the 3 requirements:
Cloud Pub/Sub is a simple, reliable, scalable foundation for stream analytics and event-driven computing systems. As part of Google Cloud’s stream analytics solution, the service ingests event streams and delivers them to Cloud Dataflow for processing and BigQuery for analysis as a data warehousing solution. Relying on the Cloud Pub/Sub service for delivery of event data frees you to focus on transforming your business and data systems with applications such as:
· check Real-time personalization in gaming
· check Fast reporting, targeting and optimization in advertising and media
· check Processing device data for healthcare, manufacturing, oil and gas, and logistics
· check Syndicating market-related data streams for financial services
Also, Use Cloud Dataflow as a convenient integration point to bring predictive analytics to fraud detection, real-time personalization and similar use cases by adding TensorFlow-based Cloud Machine Learning models and APIs to your data processing pipelines. https://cloud.google.com/ml-engine/
BigQuery provides a flexible, powerful foundation for Machine Learning and Artificial Intelligence. BigQuery provides integration with CloudML Engine and TensorFlow to train powerful models on structured data. Moreover, BigQuery’s ability to transform and analyze data helps you get your data in shape for Machine Learning. https://cloud.google.com/bigquery/
Other solutions may work one way or other but only the combination of theses 3 components integrate well in data ingestion, collection, and real-time analysis, and data mining in a highly durable, elastic, and parallel manner.
A – Cloud storage is not suitable for this kind of real-time streaming data collection; Dataproc is GCP’s BigData Hadoop/Spark that can do ETL and analysis, but DataFlow provides a simple unified programming model for ETL and analysis in both real-time and batch.
C – Cloud SQL is mainly for OLTP (Transactional, CRUD) not for OLAP (On-line Analytical Processing, Data Warehouse). It does not have the scalability, elasticity, and parallel to absorb this amount of Data in real-time. Instead, BigQuery integrates well with DataFlow and can absorb both streaming and batch data from it.
D – Bigtable is one of the possible Data sink for DataFlow and have the capability to absorb this amount of real time data but it lacks the Data mining features like BigQuery.
Further Explanation
Pub/Sub is a kind of ‘shock absorber’, allowing asynchronous messaging between large numbers of devices. Cloud Dataflow acts as your data processing pipeline for ETL functions on both streaming and batch data. BigQuery is a data warehouse, able to run analysis on petabytes of data using SQL queries.
Below is a reference architect Google recommending for similar scenario in Real-time streaming data collection and analysis https://cloud.google.com/solutions/mobile/mobile-gaming-analysis-telemetry
Real-time processing of events from game clients and game servers
Data Transformation with Cloud Dataflow – Dataflow acts as your data processing pipeline for ETL functions on both streaming and batch data.
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