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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?

A.

An optimization objective that minimizes Log loss

B.

An optimization objective that maximizes the Precision at a Recall value of 0.50

C.

An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value

D.

An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value

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?

A.

Write your data in TFRecords.

B.

Z-normalize all the numeric features.

C.

Oversample the fraudulent transaction 10 times.

D.

Use one-hot encoding on all categorical features.

You are training models in Vertex Al by using data that spans across multiple Google Cloud Projects You need to find track, and compare the performance of the different versions of your models Which Google Cloud services should you include in your ML workflow?

A.

Dataplex. Vertex Al Feature Store and Vertex Al TensorBoard

B.

Vertex Al Pipelines, Vertex Al Feature Store, and Vertex Al Experiments

C.

Dataplex. Vertex Al Experiments, and Vertex Al ML Metadata

D.

Vertex Al Pipelines: Vertex Al Experiments and Vertex Al Metadata

You work for a multinational organization that has recently begun operations in Spain. Teams within your organization will need to work with various Spanish documents, such as business, legal, and financial documents. You want to use machine learning to help your organization get accurate translations quickly and with the least effort. Your organization does not require domain-specific terms or jargon. What should you do?

A.

Create a Vertex Al Workbench notebook instance. In the notebook, convert the Spanish documents into plain text, and create a custom TensorFlow seq2seq translation model.

B.

Create a Vertex Al Workbench notebook instance. In the notebook, extract sentences from the documents, and train a custom AutoML text model.

C.

Use Google Translate to translate 1.000 phrases from Spanish to English. Using these translated pairs, train a custom AutoML Translation model.

D.

Use the Document Translation feature of the Cloud Translation API to translate the documents.

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 Al Platform notebook. What should you do?

A.

Use Al Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe

B.

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

C.

Download your table from BigQuery as a local CSV file, and upload it to your Al Platform notebook instance Use pandas. read_csv to ingest the file as a pandas dataframe

D.

From a bash cell in your Al Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutii cp to copy the data into the notebook Use pandas. read_csv to ingest the file as a pandas dataframe

You need to train a regression model based on a dataset containing 50,000 records that is stored in BigQuery. The data includes a total of 20 categorical and numerical features with a target variable that can include negative values. You need to minimize effort and training time while maximizing model performance. What approach should you take to train this regression model?

A.

Create a custom TensorFlow DNN model.

B.

Use BQML XGBoost regression to train the model

C.

Use AutoML Tables to train the model without early stopping.

D.

Use AutoML Tables to train the model with RMSLE as the optimization objective

You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard. What should you do?

A.

Create multiple models using AutoML Tables

B.

Automate multiple training runs using Cloud Composer

C.

Run multiple training jobs on Al Platform with similar job names

D.

Create an experiment in Kubeflow Pipelines to organize multiple runs

You work at a gaming startup that has several terabytes of structured data in Cloud Storage. This data includes gameplay time data user metadata and game metadata. You want to build a model that recommends new games to users that requires the least amount of coding. What should you do?

A.

Load the data in BigQuery Use BigQuery ML to tram an Autoencoder model.

B.

Load the data in BigQuery Use BigQuery ML to train a matrix factorization model.

C.

Read data to a Vertex Al Workbench notebook Use TensorFlow to train a two-tower model.

D.

Read data to a Vertex AI Workbench notebook Use TensorFlow to train a matrix factorization model.

You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

A.

Three individual features binned latitude, binned longitude, and one-hot encoded car type

B.

One feature obtained as an element-wise product between latitude, longitude, and car type

C.

One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type

D.

Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type

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?

A.

Classification

B.

Reinforcement Learning

C.

Recurrent Neural Networks (RNN)

D.

Convolutional Neural Networks (CNN)