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Amazon AWS-Certified-Machine-Learning-Specialty技術問題、AWS-Certified-Machine-Learning-Specialty試験関連情報

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AWS Certified Machine Learning - Specialty Certification Examは、機械学習の分野でキャリアを進めたい専門家にとって貴重な資格です。これは、AWSプラットフォーム上で機械学習ソリューションを設計・実装する能力を示し、世界的に認められています。この認定は、競争が激しい就職市場で専門家を目立たせ、機械学習の分野で新しいキャリアの機会を提供することができます。

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この人材があちこちいる社会で、多くのプレッシャーを感じませんか。学歴はどんなに高くても実力を代表できません。学歴はただ踏み台だけで、あなたの地位を確保できる礎は実力です。AmazonのAWS-Certified-Machine-Learning-Specialty認定試験は人気がある認証で、その認証を持ちたい人がたくさんいます。この試験に受かったら自分のキャリアを固定することができます。JpexamのAmazonのAWS-Certified-Machine-Learning-Specialty試験トレーニング資料はとても良いトレーニングツールで、あなたが首尾よく試験に合格ことを助けられます。試験に合格したら、あなたは国際的に認可され、解雇される心配する必要はありません。

Amazon MLS-C01認定を取得することは、AWSプラットフォーム上の機械学習の専門知識を示し、機械学習分野でのさまざまなキャリア機会を開くことができます。この認定は、データサイエンティスト、ソフトウェア開発者、および機械学習エンジニアのようなプロフェッショナルがAWSプラットフォーム上で機械学習の分野に特化したいと思う人向けのものです。

Amazon AWS Certified Machine Learning - Specialty 認定 AWS-Certified-Machine-Learning-Specialty 試験問題 (Q169-Q174):

質問 # 169
A retail company uses a machine learning (ML) model for daily sales forecasting. The company's brand manager reports that the model has provided inaccurate results for the past 3 weeks.
At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company's ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies.
What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?

• A. Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.
• B. Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.
• C. Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.
• D. Create a line chart with the weekly mean absolute error (MAE) of the model.

正解：A

解説：
The best way to visualize the model's degradation is to create a histogram of the model errors over the last 3 weeks and compare it with a histogram of the model errors from before that period. A histogram is a graphical representation of the distribution of numerical data. It shows how often each value or range of values occurs in the data. A model error is the difference between the actual value and the predicted value. A high model error indicates a poor fit of the model to the data. By comparing the histograms of the model errors, the ML team can see if there is a significant change in the shape, spread, or center of the distribution. This can indicate if the model is underfitting, overfitting, or drifting from the data. A line chart or a scatter plot would not be as effective as a histogram for this purpose, because they do not show the distribution of the errors. A line chart would only show the trend of the errors over time, which may not capture the variability or outliers. A scatter plot would only show the relationship between the errors and another variable, such as daily sales, which may not be relevant or informative for the model's performance. References:
Histogram - Wikipedia
Model error - Wikipedia
SageMaker Model Monitor - visualizing monitoring results

質問 # 170
A company uses camera images of the tops of items displayed on store shelves to determine which items were removed and which ones still remain. After several hours of data labeling, the company has a total of
1,000 hand-labeled images covering 10 distinct items. The training results were poor.
Which machine learning approach fulfills the company's long-term needs?

• A. Augment training data for each item using image variants like inversions and translations, build the model, and iterate.
• B. Attach different colored labels to each item, take the images again, and build the model
• C. Convert the images to grayscale and retrain the model
• D. Reduce the number of distinct items from 10 to 2, build the model, and iterate

正解：A

解説：
Explanation
Data augmentation is a technique that can increase the size and diversity of the training data by applying various transformations to the original images, such as inversions, translations, rotations, scaling, cropping, flipping, and color variations. Data augmentation can help improve the performance and generalization of image classification models by reducing overfitting and introducing more variability to the data. Data augmentation is especially useful when the original data is limited or imbalanced, as in the case of the company's problem. By augmenting the training data for each item using image variants, the company can build a more robust and accurate model that can recognize the items on the store shelves from different angles, positions, and lighting conditions. The company can also iterate on the model by adding more data or fine-tuning the hyperparameters to achieve better results.
References:
Build high performing image classification models using Amazon SageMaker JumpStart The Effectiveness of Data Augmentation in Image Classification using Deep Learning Data augmentation for improving deep learning in image classification problem Class-Adaptive Data Augmentation for Image Classification

質問 # 171
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. 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.
• B. 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.
• C. 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.
• 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.

正解：D

解説：
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

質問 # 172
A company's data scientist has trained a new machine learning model that performs better on test data than the company's existing model performs in the production environment. The data scientist wants to replace the existing model that runs on an Amazon SageMaker endpoint in the production environment. However, the company is concerned that the new model might not work well on the production environment data.
The data scientist needs to perform A/B testing in the production environment to evaluate whether the new model performs well on production environment data.
Which combination of steps must the data scientist take to perform the A/B testing? (Choose two.)

• A. Update the existing endpoint to activate the new model.
• B. Deploy the new model to the existing endpoint.
• C. Create a new endpoint configuration that includes two target variants that point to different endpoints.
• D. Update the existing endpoint to use the new endpoint configuration.
• E. Create a new endpoint configuration that includes a production variant for each of the two models.

正解：D、E

解説：
Explanation
The combination of steps that the data scientist must take to perform the A/B testing are to create a new endpoint configuration that includes a production variant for each of the two models, and update the existing endpoint to use the new endpoint configuration. This approach will allow the data scientist to deploy both models on the same endpoint and split the inference traffic between them based on a specified distribution.
Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to quickly build, train, and deploy machine learning models. Amazon SageMaker supports A/B testing on machine learning models by allowing the data scientist to run multiple production variants on an endpoint. A production variant is a version of a model that is deployed on an endpoint. Each production variant has a name, a machine learning model, an instance type, an initial instance count, and an initial weight. The initial weight determines the percentage of inference requests that the variant will handle. For example, if there are two variants with weights of 0.5 and 0.5, each variant will handle 50% of the requests. The data scientist can use production variants to test models that have been trained using different training datasets, algorithms, and machine learning frameworks; test how they perform on different instance types; or a combination of all of the above1.
To perform A/B testing on machine learning models, the data scientist needs to create a new endpoint configuration that includes a production variant for each of the two models. An endpoint configuration is a collection of settings that define the properties of an endpoint, such as the name, the production variants, and the data capture configuration. The data scientist can use the Amazon SageMaker console, the AWS CLI, or the AWS SDKs to create a new endpoint configuration. The data scientist needs to specify the name, model name, instance type, initial instance count, and initial variant weight for each production variant in the endpoint configuration2.
After creating the new endpoint configuration, the data scientist needs to update the existing endpoint to use the new endpoint configuration. Updating an endpoint is the process of deploying a new endpoint configuration to an existing endpoint. Updating an endpoint does not affect the availability or scalability of the endpoint, as Amazon SageMaker creates a new endpoint instance with the new configuration and switches the DNS record to point to the new instance when it is ready. The data scientist can use the Amazon SageMaker console, the AWS CLI, or the AWS SDKs to update an endpoint. The data scientist needs to specify the name of the endpoint and the name of the new endpoint configuration to update the endpoint3.
The other options are either incorrect or unnecessary. Creating a new endpoint configuration that includes two target variants that point to different endpoints is not possible, as target variants are only used to invoke a specific variant on an endpoint, not to define an endpoint configuration. Deploying the new model to the existing endpoint would replace the existing model, not run it side-by-side with the new model. Updating the existing endpoint to activate the new model is not a valid operation, as there is no activation parameter for an endpoint.
References:
1: A/B Testing ML models in production using Amazon SageMaker | AWS Machine Learning Blog
2: Create an Endpoint Configuration - Amazon SageMaker
3: Update an Endpoint - Amazon SageMaker

質問 # 173
An online store is predicting future book sales by using a linear regression model that is based on past sales dat a. The data includes duration, a numerical feature that represents the number of days that a book has been listed in the online store. A data scientist performs an exploratory data analysis and discovers that the relationship between book sales and duration is skewed and non-linear.
Which data transformation step should the data scientist take to improve the predictions of the model?

• A. Cartesian product transformation
• B. Normalization
• C. One-hot encoding
• D. Quantile binning

正解：D

解説：
Quantile binning is a data transformation technique that can be used to handle skewed and non-linear numerical features. It divides the range of a feature into equal-sized bins based on the percentiles of the data. Each bin is assigned a numerical value that represents the midpoint of the bin. This way, the feature values are transformed into a more uniform distribution that can improve the performance of linear models. Quantile binning can also reduce the impact of outliers and noise in the data.
One-hot encoding, Cartesian product transformation, and normalization are not suitable for this scenario. One-hot encoding is used to transform categorical features into binary features. Cartesian product transformation is used to create new features by combining existing features. Normalization is used to scale numerical features to a standard range, but it does not change the shape of the distribution. References:
Data Transformations for Machine Learning
Quantile Binning Transformation

質問 # 174
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