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AWS-Certified-Machine-Learning-Specialty模擬対策問題 & AWS-Certified-Machine-Learning-Specialty復習解答例AWS-Certified-Machine-Learning-Specialty学習教材は、試験にすばやく合格し、希望する証明書を取得するのに役立ちます。その後、あなたは良い仕事を得るためにもう一つのチップを持っています。 AWS-Certified-Machine-Learning-Specialty学習教材を使用すると、より高い出発点に立って、AWS-Certified-Machine-Learning-Specialty試験に他の人よりも一歩早く合格し、他の人よりも早くチャンスを活用できます。このペースの速い社会では、あなたの時間はとても貴重です。 1人の力だけに頼る場合、あなたが優位に立つことは困難です。 AWS-Certified-Machine-Learning-Specialtyの学習に関する質問は、最も満足のいくアシスタントになります。
Amazon AWS Certified Machine Learning - Specialty 認定 AWS-Certified-Machine-Learning-Specialty 試験問題 (Q70-Q75):質問 # 70
A machine learning (ML) specialist wants to create a data preparation job that uses a PySpark script with complex window aggregation operations to create data for training and testing. The ML specialist needs to evaluate the impact of the number of features and the sample count on model performance.
Which approach should the ML specialist use to determine the ideal data transformations for the model?

• A. Add an Amazon SageMaker Debugger hook to the script to capture key parameters. Run the script as a SageMaker processing job.
• B. Add an Amazon SageMaker Experiments tracker to the script to capture key metrics. Run the script as an AWS Glue job.
• C. Add an Amazon SageMaker Debugger hook to the script to capture key metrics. Run the script as an AWS Glue job.
• D. Add an Amazon SageMaker Experiments tracker to the script to capture key parameters. Run the script as a SageMaker processing job.
  

正解：B

質問 # 71
A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.
Which action will resolve the problem?

• A. Choose a different weight initialization type.
• B. Adjust hyperparameters related to the attention mechanism.
• C. Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.
• D. Change preprocessing to use n-grams.
  

正解：B
解説：
The data scientist should adjust hyperparameters related to the attention mechanism to resolve the problem.
The attention mechanism is a technique that allows the decoder to focus on different parts of the input sequence when generating the output sequence. It helps the model cope with long input sequences and improve the translation quality. The Amazon SageMaker seq2seq algorithm supports different types of attention mechanisms, such as dot, general, concat, and mlp. The data scientist can use the hyperparameter attention_type to choose the type of attention mechanism. The data scientist can also use the hyperparameter attention_coverage_type to enable coverage, which is a mechanism that penalizes the model for attending to the same input positions repeatedly. By adjusting these hyperparameters, the data scientist can fine-tune the attention mechanism and reduce the number of false negative predictions by the model.
Sequence-to-Sequence Algorithm - Amazon SageMaker
Attention Mechanism - Sockeye Documentation

質問 # 72
A machine learning (ML) specialist is training a linear regression model. The specialist notices that the model is overfitting. The specialist applies an L1 regularization parameter and runs the model again. This change results in all features having zero weights.
What should the ML specialist do to improve the model results?

• A. Decrease the L1 regularization parameter. Do not change any other training parameters.
• B. Introduce a large L2 regularization parameter. Do not change the current L1 regularization value.
• C. Increase the L1 regularization parameter. Do not change any other training parameters.
• D. Introduce a small L2 regularization parameter. Do not change the current L1 regularization value.
  

正解：A
解説：
Applying L1 regularization encourages sparsity by penalizing weights directly, often driving many weights to zero. In this case, the ML specialist observes that all weights become zero, which suggests that the L1 regularization parameter is set too high. This high value overly penalizes non-zero weights, effectively removing all features from the model.
To improve the model, the ML specialist should reduce the L1 regularization parameter, allowing some features to retain non-zero weights. This adjustment will make the model less prone to excessive sparsity, allowing it to better capture essential patterns in the data without dropping all features. Introducing L2 regularization is another approach but may not directly resolve this specific issue of all-zero weights as effectively as reducing L1.

質問 # 73
A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.
Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

• A. Numerical value binning
• B. High-degree polynomial transformation
• C. One hot encoding
• D. Logarithmic transformation
• E. Cross-validation
  

正解：A、D
解説：
Explanation
To fix the incorrectly skewed data, the Data Scientist can apply two feature transformations: numerical value binning and logarithmic transformation. Numerical value binning is a technique that groups continuous values into discrete bins or categories. This can help reduce the skewness of the data by creating more balanced frequency distributions. Logarithmic transformation is a technique that applies the natural logarithm function to each value in the data. This can help reduce the right skewness of the data by compressing the large values and expanding the small values. Both of these transformations can make the data more suitable for machine learning algorithms that assume normality of the data. References:
Data Transformation - Amazon SageMaker
Transforming Skewed Data for Machine Learning

質問 # 74
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000
Test set images = 100 (constant test set)
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

• A. Increase the training data by adding variation in rotation for training images.
• B. Increase the dropout rate for the second-to-last layer.
• C. Increase the number of epochs for model training
• D. Increase the number of layers for the neural network.
  

正解：A
解説：
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.

質問 # 75
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