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.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
• B.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
• C.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
• D.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

Comment: *

Name: *

Email: *

Verification: *

You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

• A.Use Al Platform for distributed training
• B.Create a cluster on Dataproc for training
• C.Create a Managed Instance Group with autoscaling
• D.Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.

Comment: *

Name: *

Email: *

Verification: *

A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs. The workflow consists of the following processes:
* Start the workflow as soon as data is uploaded to Amazon S3.
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3.
* Store the results of joining datasets in Amazon S3.
* If one of the jobs fails, send a notification to the Administrator.
Which configuration will meet these requirements?

• A.Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
• B.Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance. Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
• C.Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3. Use AWS Glue to join the datasets in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
• D.Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

Comment: *

Name: *

Email: *

Verification: *

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?

• A.Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
• B.Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
• C.Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
• D.Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model

Comment: *

Name: *

Email: *

Verification: *

Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?

• A.Initialize the model with random weights in all layers including the last fully connected layer.
• B.Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
• C.Initialize the model with random weights in all layers and replace the last fully connected layer.
• D.Initialize the model with pre-trained weights in all layers including the last fully connected layer.

Comment: *

Name: *

Email: *

Verification: *