Question-25: Running a firm effectively requires making the most of its available assets, which may include time, people, equipment, inventory, and financial resources, among others. Efficient firms are leaner, agile and more lucrative. On each of TerramEarth's vehicles, operational factors such as oil pressure may be adjusted to boost the vehicle's efficiency. These adjustments are made in response to the circumstances in which the vehicle is operating. Your major objective is to improve the efficiency of running all 20 million cellular and unconnected cars that are now out in the field. What steps can you take to reach your objective?
A. Have your engineers examine the data to look for trends, and then design a programme with rules that can automatically change the operations.
B. Capture all of the operating data, train machine learning models to identify optimum operations, and execute them locally so that improvements to the operating system may be made automatically.
C. Implement a streaming task in Google Cloud Dataflow with a sliding window, and utilise Google Cloud Messaging (GCM) to dynamically alter the operating settings.
D. Gather all of the operating data, teach machine learning models to determine the optimal operations, and put the models on the Google Cloud Machine Learning (ML) Platform so that updates to the operating parameters may be made automatically.
Correct Answer
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: 2 Explanation: Only 200k vehicle's are connected so need to run updates locally. run locally referring to the machine learning models. Machine learning models is the subject of the sentence. Nowhere in the sentence says run updates locally. So running machine learning models would only make sense in Google's ML platform, not locally. Both Option-2 and Option-4 starts with Capture all operating data, train machine learning models that identify ideal operations. so they are offering the same method for training the data. The keypoint here is make operational adjustments such as adjusting the oil pressure so if we host in GCP-ML, how are we going to instruct vehicles on field to adjust their oil pressure if they have no internet connection? There is no way to use GCP-ML model generated parameters to command the not connected field vehicles to make operational adjustments automatically. Therefore, I believe running it locally on the servers sitting in the vehicles is the only option. Making operational adjustments is an operational problem after recommendations are made by ML. In my mind, new data will keep feeding and total operational data changes every day for model and which would impact model performance over time. Monitoring model performance to achieve required efficiency levels would need some sort of centralization of efforts, as every machine environment condition might be different and there might be a need to create multiple models and test and operate them. (one shoe doesn't fit all).