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Amazon MLS-C01 exam is a certification that validates the skills and knowledge of individuals in the field of machine learning. It is designed for professionals who want to demonstrate their expertise in building, training, and deploying machine learning models using Amazon Web Services (AWS). AWS Certified Machine Learning - Specialty certification is ideal for data scientists, machine learning engineers, and developers who use AWS services to build and deploy machine learning solutions.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q202-Q207):

NEW QUESTION # 202
A company wants to segment a large group of customers into subgroups based on shared characteristics. The company's data scientist is planning to use the Amazon SageMaker built-in k-means clustering algorithm for this task. The data scientist needs to determine the optimal number of subgroups (k) to use.
Which data visualization approach will MOST accurately determine the optimal value of k?

A. Calculate the principal component analysis (PCA) components. Run the k-means clustering algorithm for a range of k by using only the first two PCA components. For each value of k, create a scatter plot with a different color for each cluster. The optimal value of k is the value where the clusters start to look reasonably separated.
B. Create a t-distributed stochastic neighbor embedding (t-SNE) plot for a range of perplexity values. The optimal value of k is the value of perplexity, where the clusters start to look reasonably separated.
C. Calculate the principal component analysis (PCA) components. Create a line plot of the number of components against the explained variance. The optimal value of k is the number of PCA components after which the curve starts decreasing in a linear fashion.
D. Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of squared errors (SSE). Plot a line chart of the SSE for each value of k. The optimal value of k is the point after which the curve starts decreasing in a linear fashion.

Answer: D

Explanation:
The solution D is the best data visualization approach to determine the optimal value of k for the k-means clustering algorithm. The solution D involves the following steps:
Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of squared errors (SSE). The SSE is a measure of how well the clusters fit the data. It is calculated by summing the squared distances of each data point to its closest cluster center. A lower SSE indicates a better fit, but it will always decrease as the number of clusters increases. Therefore, the goal is to find the smallest value of k that still has a low SSE1.
Plot a line chart of the SSE for each value of k. The line chart will show how the SSE changes as the value of k increases. Typically, the line chart will have a shape of an elbow, where the SSE drops rapidly at first and then levels off. The optimal value of k is the point after which the curve starts decreasing in a linear fashion. This point is also known as the elbow point, and it represents the balance between the number of clusters and the SSE1.
The other options are not suitable because:
Option A: Calculating the principal component analysis (PCA) components, running the k-means clustering algorithm for a range of k by using only the first two PCA components, and creating a scatter plot with a different color for each cluster will not accurately determine the optimal value of k. PCA is a technique that reduces the dimensionality of the data by transforming it into a new set of features that capture the most variance in the data. However, PCA may not preserve the original structure and distances of the data, and it may lose some information in the process. Therefore, running the k-means clustering algorithm on the PCA components may not reflect the true clusters in the data. Moreover, using only the first two PCA components may not capture enough variance to represent the data well. Furthermore, creating a scatter plot may not be reliable, as it depends on the subjective judgment of the data scientist to decide when the clusters look reasonably separated2.
Option B: Calculating the PCA components and creating a line plot of the number of components against the explained variance will not determine the optimal value of k. This approach is used to determine the optimal number of PCA components to use for dimensionality reduction, not for clustering. The explained variance is the ratio of the variance of each PCA component to the total variance of the data. The optimal number of PCA components is the point where adding more components does not significantly increase the explained variance. However, this number may not correspond to the optimal number of clusters, as PCA and k-means clustering have different objectives and assumptions2.
Option C: Creating a t-distributed stochastic neighbor embedding (t-SNE) plot for a range of perplexity values will not determine the optimal value of k. t-SNE is a technique that reduces the dimensionality of the data by embedding it into a lower-dimensional space, such as a two-dimensional plane. t-SNE preserves the local structure and distances of the data, and it can reveal clusters and patterns in the data. However, t-SNE does not assign labels or centroids to the clusters, and it does not provide a measure of how well the clusters fit the data. Therefore, t-SNE cannot determine the optimal number of clusters, as it only visualizes the data. Moreover, t-SNE depends on the perplexity parameter, which is a measure of how many neighbors each point considers. The perplexity parameter can affect the shape and size of the clusters, and there is no optimal value for it. Therefore, creating a t-SNE plot for a range of perplexity values may not be consistent or reliable3.
References:
1: How to Determine the Optimal K for K-Means?
2: Principal Component Analysis
3: t-Distributed Stochastic Neighbor Embedding

NEW QUESTION # 203
A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.
The company requires at least 85% accuracy to make use of the model.
After an exhaustive grid search, the optimal hyperparameters produced the following:
68% accuracy on the training set
67% accuracy on the validation set
What can the machine learning specialist do to improve the system's accuracy?

A. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.
B. Train a new model using the current neural network architecture.
C. Add more data to the training set and retrain the model using transfer learning to reduce the bias.
D. Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.

Answer: C

NEW QUESTION # 204
A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.
Which approach should the Specialist use to continue working?

A. Download the SageMaker notebook to their local environment, then install Jupyter Notebooks on their laptop and continue the development in a local notebook.
B. Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.
C. Install Python 3 and boto3 on their laptop and continue the code development using that environment.
D. Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.

Answer: B

Explanation:
https://aws.amazon.com/blogs/machine-learning/use-the-amazon-sagemaker-local-mode-to-train- on-your-notebook-instance/

NEW QUESTION # 205
A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team The dataset includes 1 200 products The labeled dataset has
15 features for each product such as title dimensions, weight, and price Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?

A. A DeepAR forecasting model based on a recurrent neural network (RNN)
B. A regression forest where the number of trees is set equal to the number of product categories
C. A deep convolutional neural network (CNN) with a softmax activation function for the last layer
D. An XGBoost model where the objective parameter is set to multi: softmax

Answer: D

Explanation:
XGBoost is a machine learning framework that can be used for classification, regression, ranking, and other tasks. It is based on the gradient boosting algorithm, which builds an ensemble of weak learners (usually decision trees) to produce a strong learner. XGBoost has several advantages over other algorithms, such as scalability, parallelization, regularization, and sparsity handling. For categorizing new products using the provided dataset, an XGBoost model would be a suitable choice, because it can handle multiple features and multiple classes efficiently and accurately. To train an XGBoost model for multi-class classification, the objective parameter should be set to multi: softmax, which means that the model will output a probability distribution over the classes and predict the class with the highest probability. Alternatively, the objective parameter can be set to multi: softprob, which means that the model will output the raw probability of each class instead of the predicted class label. This can be useful for evaluating the model performance or for post- processing the predictions. References:
* XGBoost: A tutorial on how to use XGBoost with Amazon SageMaker.
* XGBoost Parameters: A reference guide for the parameters of XGBoost.

NEW QUESTION # 206
A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome.
The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome ...gones from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years.
The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance.
Which solution will meet these requirements?

A. Perform classification by using supervised learning of the outcome categories for which partial information on claim contents is provided. Perform forecasting by using claim IDs and dates for all other outcome categories.
B. Perform reinforcement learning by using claim IDs and dates Instruct the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month
C. Perform forecasting by using claim IDs and dates to identify the expected number ot claims in each outcome category every month.
D. Perform classification every month by using supervised learning of the 20X3 outcome categories based on claim contents.

Answer: C

Explanation:
The best solution for this scenario is to perform forecasting by using claim IDs and dates to identify the expected number of claims in each outcome category every month. This solution has the following advantages:
* It leverages the historical data of claim outcomes and dates to capture the temporal patterns and trends of the claims in each category1.
* It does not require the claim contents or any other features to make predictions, which simplifies the data preparation and reduces the impact of missing or incomplete data2.
* It can handle the high cardinality of the outcome categories, as forecasting models can output multiple values for each time point3.
* It can provide predictions for several months in advance, which is useful for planning and budgeting purposes4.
The other solutions have the following drawbacks:
* A: Performing classification every month by using supervised learning of the 200 outcome categories based on claim contents is not suitable, because it assumes that the claim contents are available and complete for all the records, which is not the case in this scenario2. Moreover, classification models usually output a single label for each input, which is not adequate for predicting the number of claims in each category3. Additionally, classification models do not account for the temporal aspect of the data, which is important for forecasting1.
* B: Performing reinforcement learning by using claim IDs and dates and instructing the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month is not feasible, because it requires a feedback loop between the model and the agents, which might not be available or reliable in this scenario5. Furthermore, reinforcement learning is more suitable for sequential decision making problems, where the model learns from its actions and rewards, rather than forecasting problems, where the model learns from historical data and outputs future values6.
* D: Performing classification by using supervised learning of the outcome categories for which partial information on claim contents is provided and performing forecasting by using claim IDs and dates for all other outcome categories is not optimal, because it combines two different methods that might not be consistent or compatible with each other7. Also, this solution suffers from the same limitations as solution A, such as the dependency on claim contents, the inability to handle multiple outputs, and the ignorance of temporal patterns123.
1: Time Series Forecasting - Amazon SageMaker
2: Handling Missing Data for Machine Learning | AWS Machine Learning Blog
3: Forecasting vs Classification: What's the Difference? | DataRobot
4: Amazon Forecast - Time Series Forecasting Made Easy | AWS News Blog
5: Reinforcement Learning - Amazon SageMaker
6: What is Reinforcement Learning? The Complete Guide | Edureka
7: Combining Machine Learning Models | by Will Koehrsen | Towards Data Science

NEW QUESTION # 207
......

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