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NEW QUESTION: 1
Case Study: 2 - TerramEarth Case Study
Company Overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries: About 80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive.
Company Background
TerramEarth formed in 1946, when several small, family owned companies combined to retool after World War II. The company cares about their employees and customers and considers them to be extended members of their family.
TerramEarth is proud of their ability to innovate on their core products and find new markets as their customers' needs change. For the past 20 years trends in the industry have been largely toward increasing productivity by using larger vehicles with a human operator.
Solution Concept
There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second.
Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced.
The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules.
Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second, with 22 hours of operation per day.
TerramEarth collects a total of about 9 TB/day from these connected vehicles.
Existing Technical Environment
TerramEarth's existing architecture is composed of Linux-based systems that reside in a data center. These systems gzip CSV files from the field and upload via FTP, transform and aggregate them, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old.
With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts.
Business Requirements
- Decrease unplanned vehicle downtime to less than 1 week, without
increasing the cost of carrying surplus inventory
- Support the dealer network with more data on how their customers use
their equipment IP better position new products and services.
- Have the ability to partner with different companies-especially with
seed and fertilizer suppliers in the fast-growing agricultural
business-to create compelling joint offerings for their customers
CEO Statement
We have been successful in capitalizing on the trend toward larger vehicles to increase the productivity of our customers. Technological change is occurring rapidly and TerramEarth has taken advantage of connected devices technology to provide our customers with better services, such as our intelligent farming equipment. With this technology, we have been able to increase farmers' yields by 25%, by using past trends to adjust how our vehicles operate. These advances have led to the rapid growth of our agricultural product line, which we expect will generate 50% of our revenues by 2020.
CTO Statement
Our competitive advantage has always been in the manufacturing process with our ability to build better vehicles for tower cost than our competitors. However, new products with different approaches are constantly being developed, and I'm concerned that we lack the skills to undergo the next wave of transformations in our industry. Unfortunately, our CEO doesn't take technology obsolescence seriously and he considers the many new companies in our industry to be niche players. My goals are to build our skills while addressing immediate market needs through incremental innovations.
For this question, refer to the TerramEarth case study. TerramEarth has equipped unconnected trucks with servers and sensors to collet telemetry dat
A. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable.
B. Have the vehicle's computer compress the data in hourly snapshots, a Store it in a GCS Coldline bucket.
C. Have the vehicle' computer compress the data in hourly snapshots, and store it in a Google Cloud storage (GCS) Nearline bucket.
D. Next year they want to use the data to train machine learning models. They want to store this data in the cloud while reducing costs. What should they do?
E. Push the telemetry data in Real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery.
Answer: A
Explanation:
Storage is the best choice for data that you plan to access at most once a year, due to its slightly lower availability, 90-day minimum storage duration, costs for data access, and higher per- operation costs. For example:
Cold Data Storage - Infrequently accessed data, such as data stored for legal or regulatory reasons, can be stored at low cost as Coldline Storage, and be available when you need it.
Disaster recovery - In the event of a disaster recovery event, recovery time is key. Cloud Storage provides low latency access to data stored as Coldline Storage.
References: https://cloud.google.com/storage/docs/storage-classes
NEW QUESTION: 2
A. Option B
B. Option E
C. Option D
D. Option A
E. Option C
Answer: C,E
NEW QUESTION: 3
A DevOps Engineer is designing a deployment strategy for a web application. The application will use an Auto Scaling group to launch Amazon EC2 instances using an AMI. The same infrastructure will be deployed in multiple environments (development, test, and quality assurance). The deployment strategy should meet the following requirements:
- Minimize the startup time for the instance
- Allow the same AMI to work in multiple environments
- Store secrets for multiple environments securely
How should this be accomplished?
A. Preconfigure the AMI by installing all the software using AWS Systems Manager automation and configure Auto Scaling to tag the instances at launch with their specific environment. Then use a bootstrap script in user data to read the tags and configure settings for the environment. Use the AWS Systems Manager Parameter Store to store the secrets using AWS KMS.
B. Use a standard AMI from the AWS Marketplace. Configure Auto Scaling to detect the current environment. Install the software using a script in Amazon EC2 user data. Use AWS Secrets Manager to store the credentials for all environments.
C. Preconfigure the AMI using an AWS Lambda function that launches an Amazon EC2 instance, and then runs a script to install the software and create the AMI. Configure an Auto Scaling lifecycle hook to determine which environment the instance is launched in, and, based on that finding, run a configuration script. Save the secrets on an .ini file and store them in Amazon S3.
Retrieve the secrets using a configuration script in EC2 user data.
D. Preconfigure the AMI by installing all the software and configuration for all environments.
Configure Auto Scaling to tag the instances at launch with their environment. Use the Amazon EC2 user data to trigger an AWS Lambda function that reads the instance ID and then reconfigures the setting for the proper environment. Use the AWS Systems Manager Parameter Store to store the secrets using AWS KMS.
Answer: A
NEW QUESTION: 4
You need to build an interactive website that will accept uploaded images, and then ask a series of predefined questions based on each image.
Which services should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Azure Bot Service
Box 2: Computer Vision
The Computer Vision Analyze an image feature, returns information about visual content found in an image.
Use tagging, domain-specific models, and descriptions in four languages to identify content and label it with confidence. Use Object Detection to get location of thousands of objects within an image. Apply the adult/racy settings to help you detect potential adult content. Identify image types and color schemes in pictures.
References:
https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/
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