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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q113-Q118):
NEW QUESTION # 113
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.
What technique should be used to convert this column to binary values.
- A. Tokenization
- B. Normalization transformation
- C. Binarization
- D. One-hot encoding
Answer: D
Explanation:
One-hot encoding is a technique that can be used to convert a categorical variable, such as the Day-Of_Week column, to binary values. One-hot encoding creates a new binary column for each unique value in the original column, and assigns a value of 1 to the column that corresponds to the value in the original column, and 0 to the rest. For example, if the original column has values Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday, one-hot encoding will create seven new columns, each representing one day of the week. If the value in the original column is Tuesday, then the column for Tuesday will have a value of 1, and the other columns will have a value of 0. One-hot encoding can help improve the performance of machine learning models, as it eliminates the ordinal relationship between the values and creates a more informative and sparse representation of the data.
References:
* One-Hot Encoding - Amazon SageMaker
* One-Hot Encoding: A Simple Guide for Beginners | by Jana Schmidt ...
* One-Hot Encoding in Machine Learning | by Nishant Malik | Towards ...
NEW QUESTION # 114
A company wants to predict the sale prices of houses based on available historical sales dat a. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?
- A. Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
- B. Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
- C. Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
- D. Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.
Answer: D
Explanation:
Feature selection is the process of reducing the number of input variables to those that are most relevant for predicting the target variable. One way to do this is to run a correlation check of all features against the target variable and remove features with low target variable correlation scores. This means that these features have little or no linear relationship with the target variable and are not useful for the prediction. This can reduce the model's complexity and improve its performance. References:
Feature engineering - Machine Learning Lens
Feature Selection For Machine Learning in Python
NEW QUESTION # 115
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
Answer: B
NEW QUESTION # 116
A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.
Which storage scheme is MOST adapted to this scenario?
- A. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
- B. Store datasets as files in Amazon S3.
- C. Store datasets as global tables in Amazon DynamoDB.
- D. Store datasets as tables in a multi-node Amazon Redshift cluster.
Answer: D
NEW QUESTION # 117
A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.
Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Select TWO)
- A. Use the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named
"enrolled" or "not enrolled."
- B. Use a regression algorithm to run predictions.
- C. Use a forecasting algorithm to run predictions.
- D. Use Amazon SageMaker Ground Truth to sort the data into two groups named "enrolled" or "not enrolled."
- E. Use a classification algorithm to run predictions
Answer: D,E
Explanation:
The data scientist should use Amazon SageMaker Ground Truth to sort the data into two groups named
"enrolled" or "not enrolled." This will create a labeled dataset that can be used for supervised learning. The data scientist should then use a classification algorithm to run predictions on the test data. A classification algorithm is a suitable choice for predicting a binary outcome, such as enrollment status, based on the input features, such as academic performance. A classification algorithm will output a probability for each class label and assign the most likely label to each observation.
Use Amazon SageMaker Ground Truth to Label Data
Classification Algorithm in Machine Learning
NEW QUESTION # 118
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