Question 1

A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to
predict whether new customers will require long-term support.
Which modeling approach should the company use to meet this requirement?

Accepted Answer

Correct answer: C

Question 2

A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 in size and consists of
CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing
involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?

Accepted Answer

Correct answer: D

Question 3

A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day.
The model produces the predictions for the next day. The prediction process takes less than 1 minute to finish running.
How should the company deploy the model on Amazon SageMaker to meet these requirements?

Accepted Answer

Correct answer: D

Question 4

A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.
Which solution on AWS will detect duplicates in the dataset with the LEAST code development?

Accepted Answer

Correct answer: D

Question 5

A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every
second.
The company needs to implement a scalable solution on AWS to identify anomalous data points.
Which solution will meet these requirements with the LEAST operational overhead?

Accepted Answer

Correct answer: A

Question 6

A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The
company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.
Which solution will meet this requirement?

Accepted Answer

Correct answer: A

Question 7

An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's
performance improves substantially at first and then degrades after a specific number of epochs.
Which solutions will mitigate this problem? (Choose two.)

Accepted Answer

Correct answer: AB

Question 8

An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm. The model classifies transactions as either fraudulent
or legitimate.
During testing, the model excels at identifying fraud in the training dataset. However, the
model is inefficient at identifying fraud in new and unseen transactions.
What should the ML engineer do to improve the fraud detection for new transactions?

Accepted Answer

Correct answer: D

Question 9

A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker
Asynchronous Inference. Consumers are reporting delays in receiving the inference results.
An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model
quality occurs.
Which solution will meet these requirements?

Accepted Answer

Correct answer: A

Question 10

An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data
in the same way as the training data before passing the production inference data to the model for predictions.
Which solution will meet this requirement?

Accepted Answer

Correct answer: B