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MLS-C01 Exam Dumps - AWS Certified Machine Learning - Specialty

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Question # 57

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.)

A.

Emails exchanged by customers and the company’s customer service agents

B.

Social media posts containing the name of the company or its products

C.

A publicly available collection of news articles

D.

A publicly available collection of customer reviews

E.

Product sales revenue figures for the company

F.

Instruction manuals for the company’s products

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Question # 58

Example Corp has an annual sale event from October to December. The company has sequential sales data from the past 15 years and wants to use Amazon ML to predict the sales for this year's upcoming event. Which method should Example Corp use to split the data into a training dataset and evaluation dataset?

A.

Pre-split the data before uploading to Amazon S3

B.

Have Amazon ML split the data randomly.

C.

Have Amazon ML split the data sequentially.

D.

Perform custom cross-validation on the data

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Question # 59

A company has an ecommerce website with a product recommendation engine built in TensorFlow. The recommendation engine endpoint is hosted by Amazon SageMaker. Three compute-optimized instances support the expected peak load of the website.

Response times on the product recommendation page are increasing at the beginning of each month. Some users are encountering errors. The website receives the majority of its traffic between 8 AM and 6 PM on weekdays in a single time zone.

Which of the following options are the MOST effective in solving the issue while keeping costs to a minimum? (Choose two.)

A.

Configure the endpoint to use Amazon Elastic Inference (EI) accelerators.

B.

Create a new endpoint configuration with two production variants.

C.

Configure the endpoint to automatically scale with the Invocations Per Instance metric.

D.

Deploy a second instance pool to support a blue/green deployment of models.

E.

Reconfigure the endpoint to use burstable instances.

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Question # 60

A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training

What should the Specialist do to optimize the data for training on SageMaker'?

A.

Use the SageMaker batch transform feature to transform the training data into a DataFrame

B.

Use AWS Glue to compress the data into the Apache Parquet format

C.

Transform the dataset into the Recordio protobuf format

D.

Use the SageMaker hyperparameter optimization feature to automatically optimize the data

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Question # 61

A business to business (B2B) ecommerce company wants to develop a fair and equitable risk mitigation strategy to reject potentially fraudulent transactions. The company wants to reject fraudulent transactions despite the possibility of losing some profitable transactions or customers.

Which solution will meet these requirements with the LEAST operational effort?

A.

Use Amazon SageMaker to approve transactions only for products the company has sold in the past.

B.

Use Amazon SageMaker to train a custom fraud detection model based on customer data.

C.

Use the Amazon Fraud Detector prediction API to approve or deny any activities that Fraud Detector identifies as fraudulent.

D.

Use the Amazon Fraud Detector prediction API to identify potentially fraudulent activities so the company can review the activities and reject fraudulent transactions.

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Question # 62

A Machine Learning Specialist is attempting to build a linear regression model.

Given the displayed residual plot only, what is the MOST likely problem with the model?

A.

Linear regression is inappropriate. The residuals do not have constant variance.

B.

Linear regression is inappropriate. The underlying data has outliers.

C.

Linear regression is appropriate. The residuals have a zero mean.

D.

Linear regression is appropriate. The residuals have constant variance.

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Question # 63

A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.

Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.

How should the Data Scientist correct this issue?

A.

Drop all records from the dataset where age has been set to 0.

B.

Replace the age field value for records with a value of 0 with the mean or median value from the dataset.

C.

Drop the age feature from the dataset and train the model using the rest of the features.

D.

Use k-means clustering to handle missing features.

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Question # 64

A data scientist uses Amazon SageMaker Data Wrangler to analyze and visualize data. The data scientist wants to refine a training dataset by selecting predictor variables that are strongly predictive of the target variable. The target variable correlates with other predictor variables.

The data scientist wants to understand the variance in the data along various directions in the feature space.

Which solution will meet these requirements?

A.

Use the SageMaker Data Wrangler multicollinearity measurement features with a variance inflation factor (VIF) score. Use the VIF score as a measurement of how closely the variables are related to each other.

B.

Use the SageMaker Data Wrangler Data Quality and Insights Report quick model visualization to estimate the expected quality of a model that is trained on the data.

C.

Use the SageMaker Data Wrangler multicollinearity measurement features with the principal component analysis (PCA) algorithm to provide a feature space that includes all of the predictor variables.

D.

Use the SageMaker Data Wrangler Data Quality and Insights Report feature to review features by their predictive power.

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