You are developing an ML model that uses sliced frames from video feed and creates bounding boxes around specific objects. You want to automate the following steps in your training pipeline: ingestion and preprocessing of data in Cloud Storage, followed by training and hyperparameter tuning of the object model using Vertex AI jobs, and finally deploying the model to an endpoint. You want to orchestrate the entire pipeline with minimal cluster management. What approach should you use?
A. Use Kubeflow Pipelines on Google Kubernetes Engine.
B. Use Vertex AI Pipelines with TensorFlow Extended (TFX) SDK.
C. Use Vertex AI Pipelines with Kubeflow Pipelines SDK.
D. Use Cloud Composer for the orchestration.
You work on an operations team at an international company that manages a large fleet of on-premises servers located in few data centers around the world. Your team collects monitoring data from the servers, including CPU/memory consumption. When an incident occurs on a server, your team is responsible for fixing it. Incident data has not been properly labeled yet. Your management team wants you to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. What should you do first?
A. Train a time-series model to predict the machines' performance values. Configure an alert if a machine's actual performance values significantly differ from the predicted performance values.
B. Implement a simple heuristic (e.g., based on z-score) to label the machines' historical performance data. Train a model to predict anomalies based on this labeled dataset.
C. Develop a simple heuristic (e.g., based on z-score) to label the machines' historical performance data. Test this heuristic in a production environment.
D. Hire a team of qualified analysts to review and label the machines' historical performance data. Train a model based on this manually labeled dataset.
You work as an ML engineer at a social media company, and you are developing a visual filter for users' profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company's iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones. What should you do?
A. Train a model using AutoML Vision and use the "export for Core ML" option.
B. Train a model using AutoML Vision and use the "export for Coral" option.
C. Train a model using AutoML Vision and use the "export for TensorFlow.js" option.
D. Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).
You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?
A. Use Vertex AI Workbench user-managed notebooks to generate the report.
B. Use the Google Data Studio to create the report.
C. Use the output from TensorFlow Data Validation on Dataflow to generate the report.
D. Use Dataprep to create the report.
You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company's logo. In the dataset, 96% of examples don't have the logo, so the dataset is very skewed. Which metrics would give you the most confidence in your model?
A. F-score where recall is weighed more than precision
B. RMSE
C. F1 score
D. F-score where precision is weighed more than recall
You work on the data science team for a multinational beverage company. You need to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations. You are provided with historical data that includes product types, product sales volumes, expenses, and profits for all regions. What should you use as the input and output for your model?
A. Use latitude, longitude, and product type as features. Use profit as model output.
B. Use latitude, longitude, and product type as features. Use revenue and expenses as model outputs.
C. Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use profit as model output.
D. Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use revenue and expenses as model outputs.
You are an ML engineer at a bank. You have developed a binary classification model using AutoML Tables to predict whether a customer will make loan payments on time. The output is used to approve or reject loan requests. One customer's loan request has been rejected by your model, and the bank's risks department is asking you to provide the reasons that contributed to the model's decision. What should you do?
A. Use local feature importance from the predictions.
B. Use the correlation with target values in the data summary page.
C. Use the feature importance percentages in the model evaluation page.
D. Vary features independently to identify the threshold per feature that changes the classification.
You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company's historical data as your training set, you created a TensorFlow model and deployed it to AI Platform. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?
A. Use AI Platform notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.
B. Stream prediction results to BigQuery. Use BigQuery's CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.
C. Use the AI Explanations feature on AI Platform. Submit each prediction request with the `explain' keyword to retrieve feature attributions using the sampled Shapley method.
D. Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.
You have been given a dataset with sales predictions based on your company's marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the data. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks. You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?
A. Use BigQuery ML to run several regression models, and analyze their performance.
B. Read the data from BigQuery using Dataproc, and run several models using SparkML.
C. Use Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics.
D. Train a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.
You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?
A. Store your tf.logging data in BigQuery.
B. Manage all relational entities in the Hive Metastore.
C. Store all ML metadata in Google Cloud's operations suite.
D. Manage your ML workflows with Vertex ML Metadata.
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