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MLS-C01試験の準備方法｜信頼的なMLS-C01受験トレーリング試験｜正確的なAWS Certified Machine Learning - Specialty勉強ガイド

さらに、GoShiken MLS-C01ダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1n1jLqkB4gYwO8GIYOZLkYHhJ7E2xNZC6

チャンスは常に準備ができあがった者に属します。しかし、我々に属する成功の機会が来たとき、それをつかむことができましたか。AmazonのMLS-C01認定試験を受験するために準備をしているあなたは、GoShikenという成功できるチャンスを掴みましたか。GoShikenのMLS-C01問題集はあなたが楽に試験に合格する保障です。この問題集は大量な時間を節約させ、効率的に試験に準備させることができます。GoShikenの練習資料を利用すれば、あなたはこの資料の特別と素晴らしさをはっきり感じることができます。この問題集は間違いなくあなたの成功への近道で、あなたが十分にMLS-C01試験を準備させます。

MLS-C01認定は、機械学習に関する個人の専門知識とAWSサービスと協力する能力を示しているため、業界で非常に評価されています。また、個人が仲間と自分自身を区別し、キャリアを前進させる素晴らしい方法です。この認定は、個人がデータサイエンティスト、機械学習エンジニア、AI開発者などの役割を確保するのに役立ちます。

AWS認定機械学習 - 専門認定試験は、データの準備、機能エンジニアリング、モデルトレーニングと評価、展開と実装、機械学習アルゴリズムなど、MLに関連するさまざまなトピックを対象としています。この試験は、Amazon SageMakerを使用してMLモデルを設計および実装する候補者の能力をテストするように設計されています。さらに、この試験では、MLモデルを最適化およびチューニングして、望ましい結果を達成する候補者の能力もテストします。

>> MLS-C01受験トレーリング <<

便利-一番優秀なMLS-C01受験トレーリング試験-試験の準備方法MLS-C01勉強ガイド

MLS-C01ガイド資料の改革に関する専門家の絶え間ない努力により、MLS-C01テストの準備中に最短時間で集中してターゲットを絞ることができ、複雑で曖昧なコンテンツを簡素化できます。。私たちGoShikenのMLS-C01研究急流の助けを借りて、あなたは同じ時間でより有用な何かをするためにあなたのフラグメント時間を最大限に活用することを学ぶので、あなたはあなたの仲間の労働者よりも独特です。弊社のMLS-C01模擬テストの上記のすべてのサービスにより、より多くの時間、省エネ、省力化を実現できます。

Amazon AWS Certified Machine Learning - Specialty 認定 MLS-C01 試験問題 (Q128-Q133):

質問 # 128
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.
What technique should be used to convert this column to binary values.

A. Normalization transformation
B. One-hot encoding
C. Binarization
D. Tokenization

正解：B

解説：
One-hot encoding is a technique that can be used to convert a categorical variable, such as the Day-Of_Week column, to binary values. One-hot encoding creates a new binary column for each unique value in the original column, and assigns a value of 1 to the column that corresponds to the value in the original column, and 0 to the rest. For example, if the original column has values Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday, one-hot encoding will create seven new columns, each representing one day of the week. If the value in the original column is Tuesday, then the column for Tuesday will have a value of 1, and the other columns will have a value of 0. One-hot encoding can help improve the performance of machine learning models, as it eliminates the ordinal relationship between the values and creates a more informative and sparse representation of the data.
One-Hot Encoding - Amazon SageMaker
One-Hot Encoding: A Simple Guide for Beginners | by Jana Schmidt ...
One-Hot Encoding in Machine Learning | by Nishant Malik | Towards ...

質問 # 129
A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.

How should the data scientist transform the data?

A. Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata dataset. Upload them directly to Forecast from a local machine.
B. Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata dataset. Upload both datasets as tables in Amazon Aurora.
C. Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Upload both datasets as .csv files to Amazon S3.
D. Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO format. Upload the dataset in this format to Amazon S3.

正解：C

解説：
Amazon Forecast requires the input data to be in a specific format. The data scientist should use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. The target time series dataset should contain the timestamp, item_id, and demand columns, while the item metadata dataset should contain the item_id, category, and lead_time columns. Both datasets should be uploaded as .csv files to Amazon S3 . References:
How Amazon Forecast Works - Amazon Forecast
Choosing Datasets - Amazon Forecast

質問 # 130
A beauty supply store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to generate a report of hourly visitors from the recordings. The report should group visitors by hair style and hair color.
Which solution will meet these requirements with the LEAST amount of effort?

A. Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
B. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
C. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair.
D. Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair color.

正解：B

解説：
The solution that will meet the requirements with the least amount of effort is to use a semantic segmentation algorithm to identify a visitor's hair in video frames, and pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color. This solution can leverage the existing Amazon SageMaker algorithms and frameworks to perform the tasks of hair segmentation and classification.
Semantic segmentation is a computer vision technique that assigns a class label to every pixel in an image, such that pixels with the same label share certain characteristics. Semantic segmentation can be used to identify and isolate different objects or regions in an image, such as a visitor's hair in a video frame. Amazon SageMaker provides a built-in semantic segmentation algorithm that can train and deploy models for semantic segmentation tasks. The algorithm supports three state-of-the-art network architectures: Fully Convolutional Network (FCN), Pyramid Scene Parsing Network (PSP), and DeepLab v3. The algorithm can also use pre-trained or randomly initialized ResNet-50 or ResNet-101 as the backbone network. The algorithm can be trained using P2/P3 type Amazon EC2 instances in single machine configurations1.
ResNet-50 is a convolutional neural network that is 50 layers deep and can classify images into 1000 object categories. ResNet-50 is trained on more than a million images from the ImageNet database and can achieve high accuracy on various image recognition tasks. ResNet-50 can be used to determine hair style and hair color from the segmented hair regions in the video frames. Amazon SageMaker provides a built-in image classification algorithm that can use ResNet-50 as the network architecture. The algorithm can also perform transfer learning by fine-tuning the pre-trained ResNet-50 model with new data. The algorithm can be trained using P2/P3 type Amazon EC2 instances in single or multiple machine configurations2.
The other options are either less effective or more complex to implement. Using an object detection algorithm to identify a visitor's hair in video frames would not segment the hair at the pixel level, but only draw bounding boxes around the hair regions. This could result in inaccurate or incomplete hair segmentation, especially if the hair is occluded or has irregular shapes. Using an XGBoost algorithm to determine hair style and hair color would require transforming the segmented hair images into numerical features, which could lose some information or introduce noise. XGBoost is also not designed for image classification tasks, and may not achieve high accuracy or performance.
References:
1: Semantic Segmentation Algorithm - Amazon SageMaker
2: Image Classification Algorithm - Amazon SageMaker

質問 # 131
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?

A. The precision of the model is 86%, which is less than the accuracy of the model.
B. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
C. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
D. The precision of the model is 86%, which is greater than the accuracy of the model.

正解：B

解説：
Based on the model evaluation results, this is a viable model for production because the model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives. The accuracy of the model is the proportion of correct predictions out of the total predictions, which can be calculated by adding the true positives and true negatives and dividing by the total number of observations. In this case, the accuracy of the model is (10 + 76) / 100 = 0.86, which means that the model correctly predicted
86% of the customers' churn status. The cost incurred by the company as a result of false positives and false negatives is the loss or damage that the company suffers when the model makes incorrect predictions. A false positive is when the model predicts that a customer will churn, but the customer actually does not churn. A false negative is when the model predicts that a customer will not churn, but the customer actually churns. In this case, the cost of a false positive is the incentive that the company offers to the customer who is predicted to churn, which is a relatively low cost. The cost of a false negative is the revenue that the company loses when the customer churns, which is a relatively high cost. Therefore, the cost of a false positive is less than the cost of a false negative, and the company would prefer to have more false positives than false negatives.
The model has 10 false positives and 4 false negatives, which means that the company's cost is lower than if the model had more false negatives and fewer false positives.

質問 # 132
A Data Scientist is building a linear regression model and will use resulting p-values to evaluate the statistical significance of each coefficient. Upon inspection of the dataset, the Data Scientist discovers that most of the features are normally distributed. The plot of one feature in the dataset is shown in the graphic.

What transformation should the Data Scientist apply to satisfy the statistical assumptions of the linear regression model?

A. Polynomial transformation
B. Sinusoidal transformation
C. Exponential transformation
D. Logarithmic transformation

正解：D

解説：
Explanation
The plot in the graphic shows a right-skewed distribution, which violates the assumption of normality for linear regression. To correct this, the Data Scientist should apply a logarithmic transformation to the feature.
This will help to make the distribution more symmetric and closer to a normal distribution, which is a key assumption for linear regression. References:
Linear Regression
Linear Regression with Amazon Machine Learning
Machine Learning on AWS

質問 # 133
......

なにごとによらず初手は难しいです、どのようにAmazon MLS-C01試験への復習を始めて悩んでいますか。我々のAmazon MLS-C01問題集を購買するのはあなたの試験に準備する第一歩です。我々の提供するAmazon MLS-C01問題集はあなたの需要に満足できるだけでなく、試験に合格する必要があることです。あなたはまだ躊躇しているなら、GoShikenのMLS-C01問題集デモを参考しましょ。

MLS-C01勉強ガイド: https://www.goshiken.com/Amazon/MLS-C01-mondaishu.html

2025年GoShikenの最新MLS-C01 PDFダンプおよびMLS-C01試験エンジンの無料共有：https://drive.google.com/open?id=1n1jLqkB4gYwO8GIYOZLkYHhJ7E2xNZC6

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