MLS-C01 NEW DUMPS SHEET | THE BEST AWS CERTIFIED MACHINE LEARNING - SPECIALTY 100% FREE RELIABLE CRAM MATERIALS

MLS-C01 New Dumps Sheet | The Best AWS Certified Machine Learning - Specialty 100% Free Reliable Cram Materials

MLS-C01 New Dumps Sheet | The Best AWS Certified Machine Learning - Specialty 100% Free Reliable Cram Materials

Blog Article

Tags: MLS-C01 New Dumps Sheet, Reliable MLS-C01 Cram Materials, Cert MLS-C01 Guide, Exam MLS-C01 Experience, MLS-C01 Certification Dump

BTW, DOWNLOAD part of ExamsLabs MLS-C01 dumps from Cloud Storage: https://drive.google.com/open?id=1zcQXNjftlSnrXG0VKluErH5xDA0-4eeH

For candidates, the quality is the first consideration when you buy MLS-C01 exam materials. With the professional specialists to compile the MLS-C01 exam braindumps, we can ensure you that the quality and accuracy is quite high. We have a professional team to study the first-hand information for the MLS-C01 Exam brainfumps, and so that you can get the latest information timely. Besides, we offer you free demo to have a try before buying, so that you can know the form of the complete version of the MLS-C01 exam dumps. If any other questions, just contact us.

Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) exam is a certification program designed for professionals who want to demonstrate their expertise in the field of machine learning. MLS-C01 Exam is intended to validate the knowledge and skills of candidates in building, training, and deploying machine learning models on the Amazon Web Services (AWS) platform.

>> MLS-C01 New Dumps Sheet <<

Reliable MLS-C01 Cram Materials & Cert MLS-C01 Guide

Our MLS-C01 study tool can help you obtain the MLS-C01 certification and own a powerful weapon for your interview. Our MLS-C01 qualification test will help you gain recognition with true talents and better adapted to society. Now, I would like to give you a brief introduction in order to make you deepen your impression of our MLS-C01 test guides. Our MLS-C01 test guides have a higher standard of practice and are rich in content. If you are anxious about how to get MLS-C01 certification, considering purchasing our MLS-C01 study tool is a wise choice and you will not feel regretted. Our learning materials will successfully promote your acquisition of certification.

Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Exam is an industry-recognized certification that validates your expertise in designing, deploying, and maintaining machine learning solutions on the Amazon Web Services platform. It is designed for professionals who want to demonstrate their ability to use AWS services to build and deploy machine learning models.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q312-Q317):

NEW QUESTION # 312
A medical device company is building a machine learning (ML) model to predict the likelihood of device recall based on customer data that the company collects from a plain text survey. One of the survey questions asks which medications the customer is taking. The data for this field contains the names of medications that customers enter manually. Customers misspell some of the medication names. The column that contains the medication name data gives a categorical feature with high cardinality but redundancy.
What is the MOST effective way to encode this categorical feature into a numeric feature?

  • A. Use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings Of vectors Of real numbers.
  • B. Use Amazon SageMaker Data Wrangler ordinal encoding on the column to encode categories into an integer between O and the total number Of categories in the column.
  • C. Fix the spelling in the column by using char-RNN. Use Amazon SageMaker Data Wrangler one-hot encoding to transform a categorical feature to a numerical feature.
  • D. Spell check the column. Use Amazon SageMaker one-hot encoding on the column to transform a categorical feature to a numerical feature.

Answer: A

Explanation:
The most effective way to encode this categorical feature into a numeric feature is to use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings of vectors of real numbers. Similarity encoding is a technique that transforms categorical features into numerical features by computing the similarity between the categories. Similarity encoding can handle high cardinality and redundancy in categorical features, as it can group similar categories together based on their string similarity. For example, if the column contains the values "aspirin", "asprin", and "ibuprofen", similarity encoding will assign a high similarity score to "aspirin" and "asprin", and a low similarity score to "ibuprofen". Similarity encoding can also create embeddings of vectors of real numbers, which can be used as input for machine learning models.
Amazon SageMaker Data Wrangler is a feature of Amazon SageMaker that enables you to prepare data for machine learning quickly and easily. You can use SageMaker Data Wrangler to apply similarity encoding to a column of categorical data, and generate embeddings of vectors of real numbers that capture the similarity between the categories1. The other options are either less effective or more complex to implement. Spell checking the column and using one-hot encoding would require additional steps and resources, and may not capture all the misspellings or redundancies. One-hot encoding would also create a large number of features, which could increase the dimensionality and sparsity of the data. Ordinal encoding would assign an arbitrary order to the categories, which could introduce bias or noise in the data. References:
* 1: Amazon SageMaker Data Wrangler - Amazon Web Services


NEW QUESTION # 313
A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

  • A. Seq2seq
  • B. Random Cut Forest (RCF)
  • C. K-means
  • D. XGBoost

Answer: D

Explanation:
Explanation
XGBoost is a built-in Amazon SageMaker machine learning algorithm that should be used for modeling the credit card fraud detection problem. XGBoost is an algorithm that implements a scalable and distributed gradient boosting framework, which is a popular and effective technique for supervised learning problems.
Gradient boosting is a method of combining multiple weak learners, such as decision trees, into a strong learner, by iteratively fitting new models to the residual errors of the previous models and adding them to the ensemble. XGBoost can handle various types of data, such as numerical, categorical, or text, and can perform both regression and classification tasks. XGBoost also supports various features and optimizations, such as regularization, missing value handling, parallelization, and cross-validation, that can improve the performance and efficiency of the algorithm.
XGBoost is suitable for the credit card fraud detection problem for the following reasons:
The problem is a binary classification problem, where the goal is to predict whether a transaction is fraudulent or not, based on the information from new transactions. XGBoost can perform binary classification by using a logistic regression objective function and outputting the probability of the positive class (fraudulent) for each transaction.
The problem involves a large and imbalanced dataset of historical data labeled as fraudulent. XGBoost can handle large-scale and imbalanced data by using distributed and parallel computing, as well as techniques such as weighted sampling, class weighting, or stratified sampling, to balance the classes and reduce the bias towards the majority class (non-fraudulent).
The problem requires a high accuracy and precision for detecting fraudulent transactions, as well as a low false positive rate for avoiding false alarms. XGBoost can achieve high accuracy and precision by using gradient boosting, which can learn complex and non-linear patterns from the data and reduce the variance and overfitting of the model. XGBoost can also achieve a low false positive rate by using regularization, which can reduce the complexity and noise of the model and prevent it from fitting spurious signals in the data.
The other options are not as suitable as XGBoost for the credit card fraud detection problem for the following reasons:
Seq2seq: Seq2seq is an algorithm that implements a sequence-to-sequence model, which is a type of neural network model that can map an input sequence to an output sequence. Seq2seq is mainly used for natural language processing tasks, such as machine translation, text summarization, or dialogue generation. Seq2seq is not suitable for the credit card fraud detection problem, because the problem is not a sequence-to-sequence task, but a binary classification task. The input and output of the problem are not sequences of words or tokens, but vectors of features and labels.
K-means: K-means is an algorithm that implements a clustering technique, which is a type of unsupervised learning method that can group similar data points into clusters. K-means is mainly used for exploratory data analysis, dimensionality reduction, or anomaly detection. K-means is not suitable for the credit card fraud detection problem, because the problem is not a clustering task, but a classification task. The problem requires using the labeled data to train a model that can predict the labels of new data, not finding the optimal number of clusters or the cluster memberships of the data.
Random Cut Forest (RCF): RCF is an algorithm that implements an anomaly detection technique, which is a type of unsupervised learning method that can identify data points that deviate from the normal behavior or distribution of the data. RCF is mainly used for detecting outliers, frauds, or faults in the data. RCF is not suitable for the credit card fraud detection problem, because the problem is not an anomaly detection task, but a classification task. The problem requires using the labeled data to train a model that can predict the labels of new data, not finding the anomaly scores or the anomalous data points in the data.
References:
XGBoost Algorithm
Use XGBoost for Binary Classification with Amazon SageMaker
Seq2seq Algorithm
K-means Algorithm
[Random Cut Forest Algorithm]


NEW QUESTION # 314
A machine learning (ML) developer for an online retailer recently uploaded a sales dataset into Amazon SageMaker Studio. The ML developer wants to obtain importance scores for each feature of the dataset. The ML developer will use the importance scores to feature engineer the dataset.
Which solution will meet this requirement with the LEAST development effort?

  • A. Use a SageMaker notebook instance to perform a singular value decomposition analysis.
  • B. Use SageMaker Data Wrangler to perform a Gini importance score analysis.
  • C. Use a SageMaker notebook instance to perform principal component analysis (PCA).
  • D. Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.

Answer: B

Explanation:
SageMaker Data Wrangler is a feature of SageMaker Studio that provides an end-to-end solution for importing, preparing, transforming, featurizing, and analyzing data. Data Wrangler includes built-in analyses that help generate visualizations and data insights in a few clicks. One of the built-in analyses is the Quick Model visualization, which can be used to quickly evaluate the data and produce importance scores for each feature. A feature importance score indicates how useful a feature is at predicting a target label. The feature importance score is between [0, 1] and a higher number indicates that the feature is more important to the whole dataset. The Quick Model visualization uses a random forest model to calculate the feature importance for each feature using the Gini importance method. This method measures the total reduction in node impurity (a measure of how well a node separates the classes) that is attributed to splitting on a particular feature. The ML developer can use the Quick Model visualization to obtain the importance scores for each feature of the dataset and use them to feature engineer the dataset. This solution requires the least development effort compared to the other options.


NEW QUESTION # 315
A Machine Learning Specialist is building a supervised model that will evaluate customers' satisfaction with their mobile phone service based on recent usage The model's output should infer whether or not a customer is likely to switch to a competitor in the next 30 days Which of the following modeling techniques should the Specialist use1?

  • A. Anomaly detection
  • B. Binary classification
  • C. Time-series prediction
  • D. Regression

Answer: D


NEW QUESTION # 316
A Machine Learning Engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML Engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%.
What should the ML Engineer do to minimize bias due to missing values?

  • A. Replace each missing value by the mean or median across non-missing values in same row.
  • B. For each feature, approximate the missing values using supervised learning based on other features.
  • C. Delete observations that contain missing values because these represent less than 5% of the data.
  • D. Replace each missing value by the mean or median across non-missing values in the same column.

Answer: B

Explanation:
Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C.
Supervised learning applied to the imputation of missing values is an active field of research.


NEW QUESTION # 317
......

Reliable MLS-C01 Cram Materials: https://www.examslabs.com/Amazon/AWS-Certified-Specialty/best-MLS-C01-exam-dumps.html

P.S. Free & New MLS-C01 dumps are available on Google Drive shared by ExamsLabs: https://drive.google.com/open?id=1zcQXNjftlSnrXG0VKluErH5xDA0-4eeH

Report this page