A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day, the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.
Which storage scheme is MOST adapted to this scenario?

• A.Store datasets as tables in a multi-node Amazon Redshift cluster.
• B.Store datasets as files in Amazon S3.
• C.Store datasets as global tables in Amazon DynamoDB.
• D.Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.

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: *

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

Comment: *

Name: *

Email: *

Verification: *

A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population How should the Data Scientist correct this issue?

• A.Drop all records from the dataset where age has been set to 0.
• B.Replace the age field value for records with a value of 0 with the mean or median value from the dataset
• C.Drop the age feature from the dataset and train the model using the rest of the features.
• D.Use k-means clustering to handle missing features

Comment: *

Name: *

Email: *

Verification: *

You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?

• A.Compare the loss performance for each model on a held-out dataset.
• B.Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool
• C.Compare the loss performance for each model on the validation data
• D.Compare the mean average precision across the models using the Continuous Evaluation feature

Comment: *

Name: *

Email: *

Verification: *