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素敵なMLS-C01参考書内容試験-試験の準備方法-正確的なMLS-C01認定試験

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AWS認定マシンラーニングスペシャリティ認定試験は、AWSプラットフォームでの機械学習に関する専門知識を実証したい専門家にとって貴重な資格です。この認定は、AWSに機械学習モデルを設計、実装、および展開する個人の能力を検証し、試験に合格すると、AWS認定コミュニティへの雇用機会の増加とアクセスにつながる可能性があります。

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MLS-C01認定試験 & MLS-C01資格関連題

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Amazon AWS Certified Machine Learning - Specialty 認定 MLS-C01 試験問題 (Q69-Q74):

質問 # 69
A logistics company needs a forecast model to predict next month's inventory requirements for a single item in
10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.
Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)

A. Set PerformHPO to true.
B. Set PerformAutoML to true.
C. Set FeaturizationMethodName to filling.
D. Set ForecastHorizon to 4.
E. Set ForecastFrequency to W for weekly.

正解：A、E

解説：
Explanation/Reference: https://docs.aws.amazon.com/forecast/latest/dg/forecast.dg.pdf

質問 # 70
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. Softmax
B. Smooth L1 loss
C. Rectified linear units (ReLU)
D. Dropout

正解：A

質問 # 71
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines.
* Provide a quick and easy way to understand metadata.
Which approach meets trfese requirements?

A. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
B. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.
D. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.

正解：D

質問 # 72
A credit card company wants to identify fraudulent transactions in real time. A data scientist builds a machine learning model for this purpose. The transactional data is captured and stored in Amazon S3. The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative). The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker. The model produces the following results:
* True positive rate (TPR): 0.700
* False negative rate (FNR): 0.300
* True negative rate (TNR): 0.977
* False positive rate (FPR): 0.023
* Overall accuracy: 0.949
Which solution should the data scientist use to improve the performance of the model?

A. Undersample the minority class.
B. Oversample the majority class.
C. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset. Retrain the model with the updated training data.
D. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data.

正解：C

解説：
The solution that the data scientist should use to improve the performance of the model is to apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset, and retrain the model with the updated training data. This solution can address the problem of class imbalance in the dataset, which can affect the model's ability to learn from the rare but important positive class (fraud).
Class imbalance is a common issue in machine learning, especially for classification tasks. It occurs when one class (usually the positive or target class) is significantly underrepresented in the dataset compared to the other class (usually the negative or non-target class). For example, in the credit card fraud detection problem, the positive class (fraud) is much less frequent than the negative class (fair transactions). This can cause the model to be biased towards the majority class, and fail to capture the characteristics and patterns of the minority class. As a result, the model may have a high overall accuracy, but a low recall or true positive rate for the minority class, which means it misses many fraudulent transactions.
SMOTE is a technique that can help mitigate the class imbalance problem by generating synthetic samples for the minority class. SMOTE works by finding the k-nearest neighbors of each minority class instance, and randomly creating new instances along the line segments connecting them. This way, SMOTE can increase the number and diversity of the minority class instances, without duplicating or losing any information. By applying SMOTE on the minority class in the training dataset, the data scientist can balance the classes and improve the model's performance on the positive class1.
The other options are either ineffective or counterproductive. Applying SMOTE on the majority class would not balance the classes, but increase the imbalance and the size of the dataset. Undersampling the minority class would reduce the number of instances available for the model to learn from, and potentially lose some important information. Oversampling the majority class would also increase the imbalance and the size of the dataset, and introduce redundancy and overfitting.
References:
1: SMOTE for Imbalanced Classification with Python - Machine Learning Mastery

質問 # 73
While working on a neural network project, a Machine Learning Specialist discovers thai some features in the data have very high magnitude resulting in this data being weighted more in the cost function What should the Specialist do to ensure better convergence during backpropagation?

A. Data augmentation for the minority class
B. Data normalization
C. Dimensionality reduction
D. Model regulanzation

正解：B

解説：
Explanation
Data normalization is a data preprocessing technique that scales the features to a common range, such as [0, 1] or [-1, 1]. This helps reduce the impact of features with high magnitude on the cost function and improves the convergence during backpropagation. Data normalization can be done using different methods, such as min-max scaling, z-score standardization, or unit vector normalization. Data normalization is different from dimensionality reduction, which reduces the number of features; model regularization, which adds a penalty term to the cost function to prevent overfitting; and data augmentation, which increases the amount of data by creating synthetic samples. References:
Data processing options for AI/ML | AWS Machine Learning Blog
Data preprocessing - Machine Learning Lens
How to Normalize Data Using scikit-learn in Python
Normalization | Machine Learning | Google for Developers

質問 # 74
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MLS-C01認定試験: https://www.passtest.jp/Amazon/MLS-C01-shiken.html

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