Get Amazon MLS-C01 Exam Dumps

Answer : B

The best solution to meet the requirements is to tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {''HyperParameterTuningJobObjective'': {''MetricName'': ''validation:f1'', ''Type'': ''Maximize''}}.

The csv_weight hyperparameter is used to specify the instance weights for the training data in CSV format. This can help handle imbalanced data by assigning higher weights to the minority class examples and lower weights to the majority class examples. The scale_pos_weight hyperparameter is used to control the balance of positive and negative weights. It is the ratio of the number of negative class examples to the number of positive class examples. Setting a higher value for this hyperparameter can increase the importance of the positive class and improve the recall. Both of these hyperparameters can help the XGBoost model capture as many instances of returned items as possible.

Automatic model tuning (AMT) is a feature of Amazon SageMaker that automates the process of finding the best hyperparameter values for a machine learning model. AMT uses Bayesian optimization to search the hyperparameter space and evaluate the model performance based on a predefined objective metric. The objective metric is the metric that AMT tries to optimize by adjusting the hyperparameter values. For imbalanced classification problems, accuracy is not a good objective metric, as it can be misleading and biased towards the majority class. A better objective metric is the F1 score, which is the harmonic mean of precision and recall. The F1 score can reflect the balance between precision and recall and is more suitable for imbalanced data. The F1 score ranges from 0 to 1, where 1 is the best possible value. Therefore, the type of the objective should be ''Maximize'' to achieve the highest F1 score.

By tuning the csv_weight and scale_pos_weight hyperparameters and optimizing on the F1 score, the data scientist can meet the requirements most cost-effectively. This solution requires tuning only two hyperparameters, which can reduce the computation time and cost compared to tuning all possible hyperparameters. This solution also uses the appropriate objective metric for imbalanced classification, which can improve the model performance and capture more instances of returned items.

References:

* XGBoost Hyperparameters

* Automatic Model Tuning

* How to Configure XGBoost for Imbalanced Classification

* Imbalanced Data

Answer : C, D

The best approaches to decrease the training time of the model are C and D, because they can improve the computational efficiency and parallelization of the training process. These approaches have the following benefits:

C: Replacing CPU-based EC2 instances with GPU-based EC2 instances can speed up the training of the DeepAR algorithm, as it can leverage the parallel processing power of GPUs to perform matrix operations and gradient computations faster than CPUs12.The DeepAR algorithm supports GPU-based EC2 instances such as ml.p2 and ml.p33.

D: Using multiple training instances can also reduce the training time of the DeepAR algorithm, as it can distribute the workload across multiple nodes and perform data parallelism4.The DeepAR algorithm supports distributed training with multiple CPU-based or GPU-based EC2 instances3.

The other options are not effective or relevant, because they have the following drawbacks:

A: Replacing On-Demand Instances with Spot Instances can reduce the cost of the training, but not necessarily the time, as Spot Instances are subject to interruption and availability5.Moreover, the DeepAR algorithm does not support checkpointing, which means that the training cannot resume from the last saved state if the Spot Instance is terminated3.

B: Configuring model auto scaling dynamically to adjust the number of instances automatically is not applicable, as this feature is only available for inference endpoints, not for training jobs6.

E: Using a pre-trained version of the model and running incremental training is not possible, as the DeepAR algorithm does not support incremental training or transfer learning3.The DeepAR algorithm requires a full retraining of the model whenever new data is added or the hyperparameters are changed7.

References:

1:GPU vs CPU: What Matters Most for Machine Learning? | by Louis (What's AI) Bouchard | Towards Data Science

2:How GPUs Accelerate Machine Learning Training | NVIDIA Developer Blog

3:DeepAR Forecasting Algorithm - Amazon SageMaker

4:Distributed Training - Amazon SageMaker

5:Managed Spot Training - Amazon SageMaker

6:Automatic Scaling - Amazon SageMaker

7:How the DeepAR Algorithm Works - Amazon SageMaker

Answer : A, E

Option A is correct because reducing the number of features with the SageMaker PCA algorithm can help remove noise and redundancy from the data, and improve the model's performance. PCA is a dimensionality reduction technique that transforms the original features into a smaller set of linearly uncorrelated features called principal components. The SageMaker linear learner algorithm supports PCA as a built-in feature transformation option.

Option E is correct because using the SageMaker k-NN algorithm with a dimension reduction target of less than 1,000 can help the model learn from the similarity of the data points, and improve the model's performance. k-NN is a non-parametric algorithm that classifies an input based on the majority vote of its k nearest neighbors in the feature space. The SageMaker k-NN algorithm supports dimension reduction as a built-in feature transformation option.

Option B is incorrect because using the scikit-learn MDS algorithm to reduce the number of features is not a feasible option, as MDS is a computationally expensive technique that does not scale well to large datasets. MDS is a dimensionality reduction technique that tries to preserve the pairwise distances between the original data points in a lower-dimensional space.

Option C is incorrect because setting the predictor type to regressor would change the model's objective from classification to regression, which is not suitable for the given problem. A regressor model would output a continuous value instead of a binary label for each phone.

Option D is incorrect because using the SageMaker k-means algorithm with k of less than 1,000 would not help the model classify the phones, as k-means is a clustering algorithm that groups the data points into k clusters based on their similarity, without using any labels. A clustering model would not output a binary label for each phone.

References:

Amazon SageMaker Linear Learner Algorithm

Amazon SageMaker K-Nearest Neighbors (k-NN) Algorithm

[Principal Component Analysis - Scikit-learn]

[Multidimensional Scaling - Scikit-learn]

Answer : A

AComprehensive Explanation: The best way to split the dataset into a training dataset and a validation dataset is to pick a date so that 80% of the data points precede the date and assign that group of data points as the training dataset. This method preserves the temporal order of the data and ensures that the validation dataset reflects the most recent trends and patterns in the commodity price. This is important for forecasting models that rely on time series analysis and sequential data. The other methods would either introduce bias or lose information by ignoring the temporal structure of the data.

References:

Time Series Forecasting - Amazon SageMaker

Time Series Splitting - scikit-learn

Time Series Forecasting - Towards Data Science

Answer : D, E

To detect anomalous transactions, the company can use a combination of Random Cut Forest (RCF) and XGBoost models. RCF is an unsupervised algorithm that can detect outliers in the data by measuring the depth of each data point in a collection of random decision trees. XGBoost is a supervised algorithm that can learn from the labeled data points generated by RCF and classify them as normal or anomalous. RCF can also provide anomaly scores that can be used as features for XGBoost to improve the accuracy of the classification.References:

1: Amazon SageMaker Random Cut Forest

2: Amazon SageMaker XGBoost Algorithm

3: Anomaly Detection with Amazon SageMaker Random Cut Forest and Amazon SageMaker XGBoost