Google (PMLE) Exam Questions And Answers page 6
You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?
Use a convolutional neural network with max pooling and softmax activation.
Downsample the data with upweighting to create a sample with 10% positive examples.
Remove negative examples until the numbers of positive and negative examples are equal.
Data Preparation and Feature Engineering
Model Development and Evaluation
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?
Write your data in TFRecords.
Z-normalize all the numeric features.
Use one-hot encoding on all categorical features.
Oversample the fraudulent transaction 10 times.
Data Preparation and Feature Engineering
Model Development and Evaluation
You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?
An optimization objective that minimizes Log loss
An optimization objective that maximizes the Precision at a Recall value of 0.50
An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value
Model Development and Evaluation
Machine Learning Infrastructure
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 20 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?
Manually combine all columns that contain a time signal into an array. AIlow AutoML to interpret this array appropriately. Choose an automatic data split across the training, validation, and testing sets.
Submit the data for training without performing any manual transformations. AIlow AutoML to handle the appropriate transformations. Choose an automatic data split across the training, validation, and testing sets.
Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column. AIlow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets.
Submit the data for training without performing any manual transformations. Use the columns that have a time signal to manually split your data. Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing sets from 30 days after your validation set.
Data Preparation and Feature Engineering
Model Development and Evaluation
You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?
Use the AI Platform Training built-in algorithms to create a custom model.
Use AutoMlL Natural Language to extract custom entities for classification.
Use the Cloud Natural Language API to extract custom entities for classification.
Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm.
Data Preparation and Feature Engineering
Model Development and Evaluation
You work for an advertising company and want to understand the effectiveness of your company s latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an AI Platform notebook. What should you do?
Use AI Platform Notebooks BigQuery cell magic to query the data, and ingest the results as a pandas dataframe.
Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance.
Download your table from BigQuery as a local CSV file, and upload it to your AI Platform notebook instance. Use pandas.read_csv to ingest he file as a pandas dataframe.
From a bash cell in your AI Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutil cp to copy the data into the notebook. Use pandas.read_csv to ingest the file as a pandas dataframe.
Data Preparation and Feature Engineering
Deployment and Productionization
You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company s product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use AI Platform s continuous evaluation service to ensure that the models have high accuracy on your test dataset. What should you do?
Keep the original test dataset unchanged even if newer products are incorporated into retraining.
Extend your test dataset with images of the newer products when they are introduced to retraining.
Replace your test dataset with images of the newer products when they are introduced to retraining.
Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.
Data Preparation and Feature Engineering
Model Development and Evaluation
You work for an online travel agency that also sells advertising placements on its website to other companies. You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor. You want to Implement the simplest solution. How should you configure the prediction pipeline?
Embed the client on the website, and then deploy the model on AI Platform Prediction.
Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.
Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user s navigation context, and then deploy the model on AI Platform Prediction.
Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user s navigation context, and then deploy the model on Google Kubernetes Engine.
Data Preparation and Feature Engineering
Model Development and Evaluation
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?
Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
Use a model trained and deployed on BigQuery ML, and trigger retraining with the scheduled query feature in BigQuery.
Write a Cloud Functions script that launches a training and deploying job on AI Platform that is triggered by Cloud Scheduler.
Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model.
Data Preparation and Feature Engineering
Model Development and Evaluation
You work for a social media company. You need to detect whether posted images contain cars. Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to AI Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the AI Platform Prediction model version. You notice that the precision is lower than your business requirements allow. How should you adjust the model s final layer softmax threshold to increase precision?
Increase the recall.
Decrease the recall.
Increase the number of false positives.
Decrease the number of false negatives.
Model Development and Evaluation
Machine Learning Infrastructure
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