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Professionals who earn the Amazon MLS-C01 certification are highly sought after by employers because of their ability to design and implement machine learning solutions on AWS. AWS Certified Machine Learning - Specialty certification is designed for individuals who have experience working with AWS services and who want to specialize in machine learning. It is an excellent choice for data scientists, data engineers, software developers, and IT professionals who want to advance their careers in the field of AI and machine learning.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q151-Q156):

NEW QUESTION # 151
While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown What does this mean?

• A. The model might have prediction errors over a range of target values.
• B. The model is predicting its target values perfectly.
• C. There are too many variables in the model
• D. The dataset cannot be accurately represented using the regression model

Answer: A

Explanation:
Residuals are the differences between the actual and predicted values of the target variable in a regression model. A histogram of residuals is a graphical tool that can help evaluate the performance and assumptions of the model. Ideally, the histogram of residuals should have a zero-centered bell shape, which indicates that the residuals are normally distributed with a mean of zero and a constant variance. This means that the model has captured the true relationship between the input and output variables, and that the errors are random and unbiased. However, if the histogram of residuals does not have a zero-centered bell shape, as shown in the image, this means that the model might have prediction errors over a range of target values. This is because the residuals do not form a symmetrical and homogeneous distribution around zero, which implies that the model has some systematic bias or heteroscedasticity. This can affect the accuracy and validity of the model, and indicate that the model needs to be improved or modified.
Residual Analysis in Regression - Statistics By Jim
How to Check Residual Plots for Regression Analysis - dummies
Histogram of Residuals - Statistics How To

 

NEW QUESTION # 152
A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price.
Which techniques should the company use for feature selection? (Choose three.)

• A. Feature importance with a tree-based classifier
• B. Data scaling with standardization and normalization
• C. Data binning
• D. Data augmentation
• E. Univariate selection
• F. Correlation plot with heat maps

Answer: C,D,E

Explanation:
Reference:
https://towardsdatascience.com/feature-selection-using-python-for-classification-problem-b5f00a1c7028#:~:text=Univariate%20feature%20selection%20works%20by,analysis%20of%20variance%20(ANOVA).&text=That%20is%20why%20it%20is%20called%20'univariate'
https://arxiv.org/abs/2101.04530

 

NEW QUESTION # 153
A large consumer goods manufacturer has the following products on sale
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched Which solution should a Machine Learning Specialist apply?

• A. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
• B. Train a custom XGBoost model to forecast demand for the new product
• C. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
• D. Train a custom ARIMA model to forecast demand for the new product.

Answer: C

Explanation:
Explanation
The company wants to predict the demand for a new product that will soon be launched, based on the sales history of similar products. This is a time series forecasting problem, which requires a machine learning algorithm that can learn from historical data and generate future predictions.
One of the most suitable solutions for this problem is to use the Amazon SageMaker DeepAR algorithm, which is a supervised learning algorithm for forecasting scalar time series using recurrent neural networks (RNN). DeepAR can handle multiple related time series, such as the sales of different products, and learn a global model that captures the common patterns and trends across the time series.
DeepAR can also generate probabilistic forecasts that provide confidence intervals and quantify the uncertainty of the predictions.
DeepAR can outperform traditional forecasting methods, such as ARIMA, especially when the dataset contains hundreds or thousands of related time series. DeepAR can also use the trained model to forecast the demand for new products that are similar to the ones it has been trained on, by using the categorical features that encode the product attributes. For example, the company can use the product type, brand, flavor, size, and price as categorical features to group the products and learn the typical behavior for each group.
Therefore, the Machine Learning Specialist should apply the Amazon SageMaker DeepAR algorithm to forecast the demand for the new product, by using the sales history of the existing products as the training dataset, and the product attributes as the categorical features.
References:
DeepAR Forecasting Algorithm - Amazon SageMaker
Now available in Amazon SageMaker: DeepAR algorithm for more accurate time series forecasting

 

NEW QUESTION # 154
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. Dropout
• B. Smooth L1 loss
• C. Rectified linear units (ReLU)
• D. Softmax

Answer: D

Explanation:
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.
References:
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 # 155
A Machine Learning Specialist wants to bring a custom algorithm to Amazon SageMaker. The Specialist implements the algorithm in a Docker container supported by Amazon SageMaker.
How should the Specialist package the Docker container so that Amazon SageMaker can launch the training correctly?

• A. Modify the bash_profile file in the container and add a bash command to start the training program
• B. Use CMD config in the Dockerfile to add the training program as a CMD of the image
• C. Configure the training program as an ENTRYPOINT named train
• D. Copy the training program to directory /opt/ml/train

Answer: B

 

NEW QUESTION # 156
......

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