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Question #56

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 30 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?

A . Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately Choose an automatic data split across the training, validation, and testing sets
B . Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate transformations Choose an automatic data split across the training, validation, and testing sets
C . Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
D . 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 set is from 30 days after your validation set

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Question #62

Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user’s account balance will drop below the $25 threshold

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Question #63

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?

A . Export the model to BigQuery ML.
B . Deploy and version the model on Al Platform.
C . Use Dataflow with the SavedModel to read the data from BigQuery
D . Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.

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Question #63

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?

A . Export the model to BigQuery ML.
B . Deploy and version the model on Al Platform.
C . Use Dataflow with the SavedModel to read the data from BigQuery
D . Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.

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Question #63

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?

A . Export the model to BigQuery ML.
B . Deploy and version the model on Al Platform.
C . Use Dataflow with the SavedModel to read the data from BigQuery
D . Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.

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Question #63

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?

A . Export the model to BigQuery ML.
B . Deploy and version the model on Al Platform.
C . Use Dataflow with the SavedModel to read the data from BigQuery
D . Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.

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Question #67

Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

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Question #68

You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving.

What should you do?

A . Configure AutoML Tables to perform the classification task
B . Run a BigQuery ML task to perform logistic regression for the classification
C . Use Al Platform Notebooks to run the classification model with pandas library
D . Use Al Platform to run the classification model job configured for hyperparameter tuning

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Question #69

You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform.

How should you find the data that you need?

A . Use Data Catalog to search the BigQuery datasets by using keywords in the table description.
B . Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
C . Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.
D . Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.

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Question #70

You are working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven’t explored using any sophisticated algorithms or spent any time on hyperparameter tuning.

What should your next step be to identify and fix the problem?

A . Address the model overfitting by using a less complex algorithm.
B . Address data leakage by applying nested cross-validation during model training.
C . Address data leakage by removing features highly correlated with the target value.
D . Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

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Correct Answer: B
B

Explanation:

Data leakage is a problem where information from outside the training dataset is used to create the model, resulting in an overly optimistic or invalid estimate of the model performance. Data leakage can occur in time series data when the temporal order of the data is not preserved during data preparation or model evaluation. For example, if the data is shuffled before splitting into train and test sets, or if future data is used to impute missing values in past data, then data leakage can occur. One way to address data leakage in time series data is to apply nested cross-validation during model training. Nested cross-validation is a technique that allows you to perform both model selection and model evaluation in a robust way, while preserving the temporal order of the data. Nested cross-validation involves two levels of cross-validation: an inner loop for model selection and an outer loop for model evaluation. The inner loop splits the training data into k folds, trains and tunes the model on k-1 folds, and validates the model on the remaining fold. The inner loop repeats this process for each fold and selects the best model based on the validation performance. The outer loop splits the data into n folds, trains the best model from the inner loop on n-1 folds, and tests the model on the remaining fold. The outer loop repeats this process for each fold and evaluates the model performance based on the test results.

Nested cross-validation can help to avoid data leakage in time series data by ensuring that the model is trained and tested on non-overlapping data, and that the data used for validation is never seen by the model during training. Nested cross-validation can also provide a more reliable estimate of the model performance than a single train-test split or a simple cross-validation, as it reduces the variance and bias of the estimate.

Reference: Data Leakage in Machine Learning

How to Avoid Data Leakage When Performing Data Preparation Classification on a single time series – prevent leakage between train and test