You work for a retailer that sells clothes to customers around the world. You have been tasked with ensuring that ML models are built in a secure manner. Specifically, you need to protect sensitive customer data that might be used in the models. You have identified four fields containing sensitive data that are being used by your data science team: AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE. What should you do with the data before it is made available to the data science team for training purposes?

• A.Use principal component analysis (PCA) to reduce the four sensitive fields to one PCA vector.
• B.Coarsen the data by putting AGE into quantiles and rounding LATITUDE_LONGTTUDE into single precision. The other two fields are already as coarse as possible.
• C.Remove all sensitive data fields, and ask the data science team to build their models using non-sensitive data.
• D.Tokenize all of the fields using hashed dummy values to replace the real values.

Comment: *

Name: *

Email: *

Verification: *

You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?

• A.* Validate the accuracy of the model that you trained on preprocessed data
  * Create a new model that uses the raw data and is available in real time
  * Deploy the new model onto Al Platform for online prediction
• B.* Send incoming prediction requests to a Pub/Sub topic
  * Transform the incoming data using a Dataflow job
  * Submit a prediction request to Al Platform using the transformed data
  * Write the predictions to an outbound Pub/Sub queue
• C.* Stream incoming prediction request data into Cloud Spanner
  * Create a view to abstract your preprocessing logic.
  * Query the view every second for new records
  * Submit a prediction request to Al Platform using the transformed data
  * Write the predictions to an outbound Pub/Sub queue.
• D.* Send incoming prediction requests to a Pub/Sub topic
  * Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
  * Implement your preprocessing logic in the Cloud Function
  * Submit a prediction request to Al Platform using the transformed data
  * Write the predictions to an outbound Pub/Sub queue

Comment: *

Name: *

Email: *

Verification: *

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 the feature importance percentages in the model evaluation page.
• B.Use the correlation with target values in the data summary page.
• C.Use local feature importance from the predictions.
• D.Vary features independently to identify the threshold per feature that changes the classification.

Comment: *

Name: *

Email: *

Verification: *

A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?

• A.Redeploy the model as a batch transform job on an M5 instance.
• B.Redeploy the model on a P3dn instance.
• C.Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
• D.Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

Comment: *

Name: *

Email: *

Verification: *

A monitoring service generates 1 TB of scale metrics record data every minute. A Research team performs queries on this data using Amazon Athena. The queries run slowly due to the large volume of data, and the team requires better performance.
How should the records be stored in Amazon S3 to improve query performance?

• A.CSV files
• B.RecordIO
• C.Parquet files
• D.Compressed JSON

Comment: *

Name: *

Email: *

Verification: *