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?

1. A. Perform one-hot encoding on highly correlated features
2. B. Use matrix multiplication on highly correlated features.
3. C. Create a new feature space using principal component analysis (PCA)
4. D. Apply the Pearson correlation coefficient

Correct Answer: B

A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.
Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)

1. A. Emails exchanged by customers and the company’s customer service agents
2. B. Social media posts containing the name of the company or its products
3. C. A publicly available collection of news articles
4. D. A publicly available collection of customer reviews
5. E. Product sales revenue figures for the company
6. F. Instruction manuals for the company’s products

Correct Answer: BDF

A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?

1. A. Decision tree
2. B. Linear support vector machine (SVM)
3. C. Naive Bayesian classifier
4. D. Single Perceptron with sigmoidal activation function

Correct Answer: C

A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1 000 records and 50 features Prior to training, the ML Specialist notices that two features are perfectly linearly dependent
Why could this be an issue for the linear least squares regression model?

1. A. It could cause the backpropagation algorithm to fail during training
2. B. It could create a singular matrix during optimization which fails to define a unique solution
3. C. It could modify the loss function during optimization causing it to fail during training
4. D. It could introduce non-linear dependencies within the data which could invalidate the linear assumptions of the model

Correct Answer: C

A company wants to use automatic speech recognition (ASR) to transcribe messages that are less than 60 seconds long from a voicemail-style application. The company requires the correct identification of 200 unique product names, some of which have unique spellings or pronunciations.
The company has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts it can use to customize the chosen ASR model. The company needs to ensure that everyone can update their customizations multiple times each hour.
Which approach will maximize transcription accuracy during the development phase?

1. A. Use a voice-driven Amazon Lex bot to perform the ASR customizatio
2. B. Create customer slots within the bot that specifically identify each of the required product name
3. C. Use the Amazon Lex synonym mechanism to provide additional variations of each product name as mis-transcriptions are identified in development.
4. D. Use Amazon Transcribe to perform the ASR customizatio
5. E. Analyze the word confidence scores in the transcript, and automatically create or update a custom vocabulary file with any word that has a confidence score below an acceptable threshold valu
6. F. Use this updated custom vocabulary file in all future transcription tasks.
7. G. Create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customizatio
8. H. Analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.
9. I. Use the audio transcripts to create a training dataset and build an Amazon Transcribe custom language mode
10. J. Analyze the transcripts and update the training dataset with a manually corrected version of transcripts where product names are not being transcribed correctl
11. K. Create an updated custom language model.

Correct Answer: A