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MLS-C01 Training Online - Testking MLS-C01 Learning Materials

MLS-C01 Training Online - Testking MLS-C01 Learning Materials

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Understanding functional and technical aspects of AWS Certified Machine Learning Specialty Exam Data Engineering

The following will be dicussed here:

  • Create data repositories for machine learning
  • Identify and implement a data-transformation solution
  • Identify and implement a data-ingestion solution

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q111-Q116):

NEW QUESTION # 111
A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population.
Which cross-validation strategy should the Data Scientist adopt?

  • A. A stratified k-fold cross-validation strategy with k=5
  • B. An 80/20 stratified split between training and validation
  • C. A k-fold cross-validation strategy with k=5 and 3 repeats
  • D. A k-fold cross-validation strategy with k=5

Answer: A

Explanation:
Explanation
A stratified k-fold cross-validation strategy is a technique that preserves the class distribution in each fold.
This is important for imbalanced datasets, such as the one in the question, where the disease is seen in only 3% of the population. If a random k-fold cross-validation strategy is used, some folds may have no positive cases or very few, which would lead to poor estimates of the model performance. A stratified k-fold cross-validation strategy ensures that each fold has the same proportion of positive and negative cases as the whole dataset, which makes the evaluation more reliable and robust. A k-fold cross-validation strategy with k=5 and 3 repeats is also a possible option, but it is more computationally expensive and may not be necessary if the stratification is done properly. An 80/20 stratified split between training and validation is another option, but it uses less data for training and validation than k-fold cross-validation, which may result in higher variance and lower accuracy of the estimates. References:
AWS Machine Learning Specialty Certification Exam Guide
AWS Machine Learning Training: Model Evaluation
How to Fix k-Fold Cross-Validation for Imbalanced Classification


NEW QUESTION # 112
A data scientist obtains a tabular dataset that contains 150 correlated features with different ranges to build a regression model. The data scientist needs to achieve more efficient model training by implementing a solution that minimizes impact on the model's performance. The data scientist decides to perform a principal component analysis (PCA) preprocessing step to reduce the number of features to a smaller set of independent features before the data scientist uses the new features in the regression model.
Which preprocessing step will meet these requirements?

  • A. Reduce the dimensionality of the dataset by removing the features that have the highest correlation Load the data into Amazon SageMaker Data Wrangler Perform a Standard Scaler transformation step to scale the data Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data
  • B. Reduce the dimensionality of the dataset by removing the features that have the lowest correlation.Load the data into Amazon SageMaker Data Wrangler. Perform a Min Max Scaler transformation step to scale the data. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.
  • C. Use the Amazon SageMaker built-in algorithm for PCA on the dataset to transform the data
  • D. Load the data into Amazon SageMaker Data Wrangler. Scale the data with a Min Max Scaler transformation step Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.

Answer: D

Explanation:
Principal component analysis (PCA) is a technique for reducing the dimensionality of datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. PCA is useful when dealing with datasets that have a large number of correlated features. However, PCA is sensitive to the scale of the features, so it is important to standardize or normalize the data before applying PCA. Amazon SageMaker provides a built-in algorithm for PCA that can be used to transform the data into a lower-dimensional representation. Amazon SageMaker Data Wrangler is a tool that allows data scientists to visually explore, clean, and prepare data for machine learning.
Data Wrangler provides various transformation steps that can be applied to the data, such as scaling, encoding, imputing, etc. Data Wrangler also integrates with SageMaker built-in algorithms, such as PCA, to enable feature engineering and dimensionality reduction. Therefore, option B is the correct answer, as it involves scaling the data with a Min Max Scaler transformation step, which rescales the data to a range of [0,
1], and then using the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.
Option A is incorrect, as it does not involve scaling the data before applying PCA, which can affect the results of the dimensionality reduction. Option C is incorrect, as it involves removing the features that have the highest correlation, which can lead to information loss and reduce the performance of the regression model.
Option D is incorrect, as it involves removing the features that have the lowest correlation, which can also lead to information loss and reduce the performance of the regression model. References:
* Principal Component Analysis (PCA) - Amazon SageMaker
* Scale data with a Min Max Scaler - Amazon SageMaker Data Wrangler
* Use Amazon SageMaker built-in algorithms - Amazon SageMaker Data Wrangler


NEW QUESTION # 113
A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes Which function will produce the desired output?

  • A. Smooth L1 loss
  • B. Dropout
  • C. Softmax
  • D. Rectified linear units (ReLU)

Answer: C

Explanation:
The softmax function is a function that can transform a vector of arbitrary real values into a vector of real values in the range (0,1) that sum to 1. This means that the softmax function can produce a valid probability distribution over multiple classes. The softmax function is often used as the activation function of the output layer in a neural network, especially for multi-class classification problems. The softmax function can assign higher probabilities to the classes with higher scores, which allows the network to make predictions based on the most likely class. In this case, the Machine Learning Specialist wants to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes of animals. Therefore, the softmax function is the most suitable function to produce the desired output.
Softmax Activation Function for Deep Learning: A Complete Guide
What is Softmax in Machine Learning? - reason.town
machine learning - Why is the softmax function often used as activation ...
Multi-Class Neural Networks: Softmax | Machine Learning | Google for ...


NEW QUESTION # 114
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. Create three columns to encode the color in RGB format.
  • B. Replace each color name by its training set frequency.
  • C. Apply integer transformation and set Red = 1, White = 5, and Green = 10.
  • D. Add new columns that store one-hot representation of colors.
  • E. Replace the color name string by its length.

Answer: A,C


NEW QUESTION # 115
A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings. The company stores audit documents in text format. Auditors have requested help from a data science team to quickly analyze the documents. The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members. Documents that describe adverse events must receive the highest priority.
A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic.
Which algorithms are best suited to this scenario? (Choose two.)

  • A. Latent Dirichlet allocation (LDA)
  • B. Linear support vector machine
  • C. Linear regression
  • D. Random Forest classifier
  • E. Neural topic modeling (NTM)

Answer: A,E

Explanation:
The algorithms that are best suited to this scenario are latent Dirichlet allocation (LDA) and neural topic modeling (NTM), as they are both unsupervised learning methods that can discover abstract topics from a collection of text documents. LDA and NTM can provide a list of the top words for each topic, as well as the topic distribution for each document, which can help the auditors assess the relevance and priority of the topic12.
The other options are not suitable because:
Option B: A random forest classifier is a supervised learning method that can perform classification or regression tasks by using an ensemble of decision trees. A random forest classifier is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes3.
Option D: A linear support vector machine is a supervised learning method that can perform classification or regression tasks by using a linear function that separates the data into different classes. A linear support vector machine is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes4.
Option E: A linear regression is a supervised learning method that can perform regression tasks by using a linear function that models the relationship between a dependent variable and one or more independent variables. A linear regression is not suitable for discovering abstract topics from text documents, as it requires labeled data and a continuous output variable5.
References:
1: Latent Dirichlet Allocation
2: Neural Topic Modeling
3: Random Forest Classifier
4: Linear Support Vector Machine
5: Linear Regression


NEW QUESTION # 116
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