Question 1

A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.

How can the data scientist meet this requirements?

Options :

A : Call the CreateNotebookInstanceLifecycleConfig API operation

B : Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance

C : Stop and then restart the SageMaker notebook instance

D : Call the UpdateNotebookInstanceLifecycleConfig API operation

Answer: C

Question 2

A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?

Options :

A : Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.

B : Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.

C : Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.

D : Randomly select 10% of the users. Split off all interaction data from these users for the test set.

Answer: D

Question 3

An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production. The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests. Which one model can serve user requests at a time. The company must measure the performance of the new experimental model without affecting the current live traffic Which solution will meet these requirements?

Options :

A : A/B testing

B : Canary release

C : Shadow deployment

D : Blue/green deployment

Answer: C

Question 4

An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.

What should the Specialist do to meet these requirements?

Options :

A : Create one-hot word encoding vectors.

B : Produce a set of synonyms for every word using Amazon Mechanical Turk.

C : Create word embedding factors that store edit distance with every other word.

D : Download word embedding-s pre-trained on a large corpus.

Answer: D

Question 5

A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model. Which solution will meet these requirements with the LEAST development effort?

Options :

A :

Use SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.

B :

Use AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report

C : Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.

D :

Use custom Amazon CloudWatch metrics to generate the explanation report. Attach the report to the predicted results.

Answer: C

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