AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY UPDATED TESTKINGS & AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY 100% EXAM COVERAGE

AWS-Certified-Machine-Learning-Specialty Updated Testkings & AWS-Certified-Machine-Learning-Specialty 100% Exam Coverage

AWS-Certified-Machine-Learning-Specialty Updated Testkings & AWS-Certified-Machine-Learning-Specialty 100% Exam Coverage

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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Certification Exam is designed to test the skills and knowledge of professionals who work with machine learning technologies within the Amazon Web Services (AWS) environment. AWS Certified Machine Learning - Specialty certification is ideal for individuals who want to demonstrate their proficiency in designing, implementing, and maintaining machine learning solutions on AWS. AWS-Certified-Machine-Learning-Specialty exam assesses candidates on a range of topics, including data engineering, machine learning algorithms, AWS services for machine learning, and model deployment and maintenance.

The AWS Certified Machine Learning - Specialty certification is a valuable credential for professionals who want to advance their careers in the field of ML. Certified individuals have a competitive edge in the job market, as they demonstrate their ability to design and implement cutting-edge ML solutions on the AWS platform. Moreover, the certification is recognized by industry leaders and organizations, which further enhances its value and credibility. Overall, the AWS Certified Machine Learning - Specialty exam is a challenging but rewarding certification that can help individuals prove their expertise in the field of ML and advance their careers.

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How to study the AWS Certified Machine Learning - Specialty

One of the selling points of this practice exam is that each question contains detailed explanations that will help you gain a deeper understanding of the AWS services. It not just explains what the correct answer is, but also explains why other answers are wrong. It is extremely helpful to make you recognize the difference between similar services.

These AMAZON MLS-C01 exam dumps are extremely useful, they give you a quick overview of the important AWS services that you need to know very well to be able to pass the AWS certification exams. They are concise and easy to read and follow. One of the most helpful parts is the comparison of similar services. Often in the exam, there are two similar answers that seem can solve the problems of the given scenarios. But the minor difference between the services leads to one answer but not the other. Familiar yourself with all the services with the comparison table/chart helps you to pass the exam.

Smart Candidates who want to build a solid foundation in all exam topics and related technologies usually combine video lectures with study guides to reap the benefits of both but there is one crucial preparation tool as often overlooked by most candidates the practice exams. Practice exams are built to make students comfortable with the real exam environment. Statistics have shown that most students fail not due to that preparation but due to exam anxiety the fear of the unknown. BraindumpsPass expert team recommends you to prepare some notes on these topics along with it don't forget to practice AWS Certified Machine Learning - Specialty exam dumps which been written by our expert team, Both these will help you a lot to clear this exam with good marks.

Although it is recommended to read all the related whitepapers and learn all the concepts and strategies from them, you can also learn quickly from AMAZON MLS-C01 practice exam and AMAZON MLS-C01 practice exams. AWS has just updated the whitepapers of the Well-Architected Framework and the Five Pillars. If you have sufficient time, reading those whitepapers will be a great supplement to your exam preparations and will help you gain a better understanding of the different AWS services.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q237-Q242):

NEW QUESTION # 237
A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among
200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.
What type of machine learning model should be used?

  • A. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
  • B. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
  • C. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
  • D. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.

Answer: C

Explanation:
Forecasting is a type of machine learning model that predicts future values of a target variable based on historical data and other features. Forecasting is suitable for problems that involve time-series data, such as the number of claims in each category from month to month. Forecasting can handle multiple categories of the target variable, as well as missing or partial information on some features. Therefore, option C is the best choice for the given problem.
Option A is incorrect because classification is a type of machine learning model that assigns a label to an input based on predefined categories. Classification is not suitable for predicting continuous or numerical values, such as the number of claims in each category from month to month. Moreover, classification requires sufficient and complete information on the features that are relevant to the target variable, which is not the case for the given problem. Option B is incorrect because reinforcement learning is a type of machine learning model that learns from its own actions and rewards in an interactive environment. Reinforcement learning is not suitable for problems that involve historical data and do not require an agent to take actions. Option D is incorrect because it combines two different types of machine learning models, which is unnecessary and inefficient. Moreover, classification is not suitable for predicting the number of claims in some categories, as explained in option A.
References:
* Forecasting | AWS Solutions for Machine Learning (AI/ML) | AWS Solutions Library
* Time Series Forecasting Service - Amazon Forecast - Amazon Web Services
* Amazon Forecast: Guide to Predicting Future Outcomes - Onica
* Amazon Launches What-If Analyses for Machine Learning Forecasting ...


NEW QUESTION # 238
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. Dropout
  • B. Softmax
  • C. Rectified linear units (ReLU)
  • D. Smooth L1 loss

Answer: B

Explanation:
https://medium.com/data-science-bootcamp/understand-the-softmax-function-in-minutes- f3a59641e86d


NEW QUESTION # 239
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. Dropout
  • B. Softmax
  • C. Rectified linear units (ReLU)
  • D. Smooth L1 loss

Answer: C


NEW QUESTION # 240
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. K-means
  • B. XGBoost
  • C. Random Cut Forest (RCF)
  • D. Seq2seq

Answer: B

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 # 241
A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
Which of the following will accomplish this? (Choose two.)

  • A. Download and apt-get installthe inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.
  • B. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.
  • C. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
  • D. Customize the built-in image classification algorithm to use Inception and use this for model training.
  • E. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network, and use this for model training.

Answer: D,E


NEW QUESTION # 242
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