A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable

What should be done to reduce the impact of having such a large number of features?

A. Perform one-hot encoding on highly correlated features

B. Use matrix multiplication on highly correlated features.

C. Create a new feature space using principal component analysis (PCA)

D. Apply the Pearson correlation coefficient

A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.

What should the Specialist do to meet this objective?

A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.

B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.

C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.

D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

A company is using a legacy telephony platform and has several years remaining on its contract. The company wants to move to AWS and wants to implement the following machine learning features:

1.

Call transcription in multiple languages

2.

Categorization of calls based on the transcript

3.

Detection of the main customer issues in the calls

4.

Customer sentiment analysis for each line of the transcript, with positive or negative indication and scoring of that sentiment

Which AWS solution will meet these requirements with the LEAST amount of custom model training?

A. Use Amazon Transcribe to process audio calls to produce transcripts, categorize calls, and detect issues. Use Amazon Comprehend to analyze sentiment.

B. Use Amazon Transcribe to process audio calls to produce transcripts. Use Amazon Comprehend to categorize calls, detect issues, and analyze sentiment

C. Use Contact Lens for Amazon Connect to process audio calls to produce transcripts, categorize calls, detect issues, and analyze sentiment.

D. Use Contact Lens for Amazon Connect to process audio calls to produce transcripts. Use Amazon Comprehend to categorize calls, detect issues, and analyze sentiment.

An online retail company wants to develop a natural language processing (NLP) model to improve customer service. A machine learning (ML) specialist is setting up distributed training of a Bidirectional Encoder Representations from Transformers (BERT) model on Amazon SageMaker. SageMaker will use eight compute instances for the distributed training.

The ML specialist wants to ensure the security of the data during the distributed training. The data is stored in an Amazon S3 bucket.

Which combination of steps should the ML specialist take to protect the data during the distributed training? (Choose three.)

A. Run distributed training jobs in a private VPC. Enable inter-container traffic encryption.

B. Run distributed training jobs across multiple VPCs. Enable VPC peering.

C. Create an S3 VPC endpoint. Then configure network routes, endpoint policies, and S3 bucket policies.

D. Grant read-only access to SageMaker resources by using an IAM role.

E. Create a NAT gateway. Assign an Elastic IP address for the NAT gateway.

F. Configure an inbound rule to allow traffic from a security group that is associated with the training instances.

A data scientist wants to build a financial trading bot to automate investment decisions. The financial bot should recommend the quantity and price of an asset to buy or sell to maximize long-term profit. The data scientist will continuously stream financial transactions to the bot for training purposes. The data scientist must select the appropriate machine learning (ML) algorithm to develop the financial trading bot.

Which type of ML algorithm will meet these requirements?

A. Supervised learning

B. Unsupervised learning

C. Semi-supervised learning

D. Reinforcement learning

A company has a podcast platform that has thousands of users. The company implemented an algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening to, pausing, and closing the podcast. A machine learning (ML) specialist is designing the ingestion process for these events. The ML specialist needs to transform the data to prepare the data for inference.

How should the ML specialist design the transformation step to meet these requirements with the LEAST operational effort?

A. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use Amazon Kinesis Data Analytics to transform the most recent 10 minutes of data before inference.

B. Use Amazon Kinesis Data Streams to ingest event data. Store the data in Amazon S3 by using Amazon Kinesis Data Firehose. Use AWS Lambda to transform the most recent 10 minutes of data before inference.

C. Use Amazon Kinesis Data Streams to ingest event data. Use Amazon Kinesis Data Analytics to transform the most recent 10 minutes of data before inference.

D. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use AWS Lambda to transform the most recent 10 minutes of data before inference.

A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also shows a right skew, with fewer older individuals participating in the workforce.

Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

A. Cross-validation

B. Numerical value binning

C. High-degree polynomial transformation

D. Logarithmic transformation

E. One hot encoding

A company offers an online shopping service to its customers. The company wants to enhance the site's security by requesting additional information when customers access the site from locations that are different from their normal location. The company wants to update the process to call a machine learning (ML) model to determine when additional information should be requested.

The company has several terabytes of data from its existing ecommerce web servers containing the source IP addresses for each request made to the web server. For authenticated requests, the records also contain the login name of the requesting user.

Which approach should an ML specialist take to implement the new security feature in the web application?

A. Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the factorization machines (FM) algorithm.

B. Use Amazon SageMaker to train a model using the IP Insights algorithm. Schedule updates and retraining of the model using new log data nightly.

C. Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the IP Insights algorithm.

D. Use Amazon SageMaker to train a model using the Object2Vec algorithm. Schedule updates and retraining of the model using new log data nightly.

A machine learning specialist is developing a regression model to predict rental rates from rental listings. A variable named Wall_Color represents the most prominent exterior wall color of the property. The following is the sample data, excluding all other variables:

The specialist chose a model that needs numerical input data.

Which feature engineering approaches should the specialist use to allow the regression model to learn from the Wall_Color data? (Choose two.)

A. Apply integer transformation and set Red = 1, White = 5, and Green = 10.

B. Add new columns that store one-hot representation of colors.

C. Replace the color name string by its length.

D. Create three columns to encode the color in RGB format.

E. Replace each color name by its training set frequency.

A data scientist is evaluating a GluonTS on Amazon SageMaker DeepAR model. The evaluation metrics on the test set indicate that the coverage score is 0.489 and 0.889 at the 0.5 and 0.9 quantiles, respectively. What can the data scientist reasonably conclude about the distributional forecast related to the test set?

A. The coverage scores indicate that the distributional forecast is poorly calibrated. These scores should be approximately equal to each other at all quantiles.

B. The coverage scores indicate that the distributional forecast is poorly calibrated. These scores should peak at the median and be lower at the tails.

C. The coverage scores indicate that the distributional forecast is correctly calibrated. These scores should always fall below the quantile itself.

D. The coverage scores indicate that the distributional forecast is correctly calibrated. These scores should be approximately equal to the quantile itself.