Google Professional-Machine-Learning-Engineer Quiz 2 Topic 6 Questions 6-10

Question: 1

You deployed an ML model into production a year ago. Every month, you collect all raw requests that were sent to your model prediction service during the previous month. You send a subset of these requests to a human labeling service to evaluate your model's performance. After a year, you notice that your model's performance sometimes degrades significantly after a month, while other times it takes several months to notice any decrease in performance. The labeling service is costly, but you also need to avoid large performance degradations. You want to determine how often you should retrain your model to maintain a high level of performance while minimizing cost. What should you do?

ATrain an anomaly detection model on the training dataset, and run all incoming requests through this model. If an anomaly is detected, send the most recent serving data to the labeling service.

BIdentify temporal patterns in your model's performance over the previous year. Based on these patterns, create a schedule for sending serving data to the labeling service for the next year.

CCompare the cost of the labeling service with the lost revenue due to model performance degradation over the past year. If the lost revenue is greater than the cost of the labeling service, increase the frequency of model retraining; otherwise, decrease the model retraining frequency.

DRun training-serving skew detection batch jobs every few days to compare the aggregate statistics of the features in the training dataset with recent serving data. If skew is detected, send the most recent serving data to the labeling service.

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Question: 2

You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

AExport the model to BigQuery ML.

BDeploy and version the model on Al Platform.

CUse Dataflow with the SavedModel to read the data from BigQuery

DSubmit a batch prediction job on Al Platform that points to the model location in Cloud Storage.

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Question: 3

You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don't overfit the model. What should you do?

AStandardize the data by transforming it with a logarithmic function.

BApply a principal component analysis (PCA) to minimize the effect of any particular feature.

CUse a binning strategy to replace the magnitude of each feature with the appropriate bin number.

DNormalize the data by scaling it to have values between 0 and 1.

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Question: 4

You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your models features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

AClassification

BReinforcement Learning

CRecurrent Neural Networks (RNN)

DConvolutional Neural Networks (CNN)

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Question: 5

You have trained a DNN regressor with TensorFlow to predict housing prices using a set of predictive features. Your default precision is tf.float64, and you use a standard TensorFlow estimator;

estimator = tf.estimator.DNNRegressor(

feature_columns=[YOUR_LIST_OF_FEATURES],

hidden_units-[1024, 512, 256],

dropout=None)

Your model performs well, but Just before deploying it to production, you discover that your current serving latency is 10ms @ 90 percentile and you currently serve on CPUs. Your production requirements expect a model latency of 8ms @ 90 percentile. You are willing to accept a small decrease in performance in order to reach the latency requirement Therefore your plan is to improve latency while evaluating how much the model's prediction decreases. What should you first try to quickly lower the serving latency?

AIncrease the dropout rate to 0.8 in_PREDICT mode by adjusting the TensorFlow Serving parameters

BIncrease the dropout rate to 0.8 and retrain your model.

CSwitch from CPU to GPU serving

DApply quantization to your SavedModel by reducing the floating point precision to tf.float16.

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Google Professional-Machine-Learning-Engineer Quiz:2 Topic:6 Questions:6-10