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The AWS Certified Machine Learning - Specialty certification is ideal for individuals who want to enhance their career in the field of machine learning and data science. It is also a valuable credential for IT professionals who are looking for ways to improve their skills and stay up-to-date with the latest trends in cloud-based machine learning solutions. By earning the AWS Certified Machine Learning - Specialty Certification, individuals can demonstrate their expertise and credibility in this rapidly growing field, making them more attractive to potential employers and clients.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is a valuable credential for professionals who want to specialize in machine learning. AWS-Certified-Machine-Learning-Specialty Exam Tests the candidate's ability to design, implement, and maintain machine learning solutions on the AWS platform. To prepare for the exam, candidates should have a solid understanding of machine learning algorithms, data modeling, and cloud computing concepts, and should take advantage of the training and certification resources available through AWS.

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The Amazon AWS-Certified-Machine-Learning-Specialty Exam covers a wide range of topics, including data engineering, data analysis, ML models, AWS services, and deployment and implementation. Candidates are expected to have a strong understanding of ML concepts and techniques, as well as experience working with AWS services such as Amazon SageMaker, Amazon Elastic MapReduce (EMR), and Amazon Simple Storage Service (S3).

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q49-Q54):

NEW QUESTION # 49
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)

A. Increase dropout.
B. Decrease feature combinations.
C. Increase regularization.
D. Decrease regularization.
E. Decrease dropout.
F. Increase feature combinations.

Answer: A,B,C

Explanation:
The problem of poor performance on the test data is a sign of overfitting, which means the model has learned the training data too well and failed to generalize to new and unseen data. To correct this, the Machine Learning Specialist should consider using methods that reduce the complexity of the model and increase its ability to generalize. Some of these methods are:
Increase regularization: Regularization is a technique that adds a penalty term to the loss function of the model, which reduces the magnitude of the model weights and prevents overfitting. There are different types of regularization, such as L1, L2, and elastic net, that apply different penalties to the weights1.
Increase dropout: Dropout is a technique that randomly drops out some units or connections in the neural network during training, which reduces the co-dependency of the units and prevents overfitting. Dropout can be applied to different layers of the network, and the dropout rate can be tuned to control the amount of dropout2.
Decrease feature combinations: Feature combinations are the interactions between different input features that can be used to create new features for the model. However, too many feature combinations can increase the complexity of the model and cause overfitting. Therefore, the Specialist should decrease the number of feature combinations and select only the most relevant and informative ones for the model3.
References:
1: Regularization for Deep Learning - Amazon SageMaker
2: Dropout - Amazon SageMaker
3: Feature Engineering - Amazon SageMaker

NEW QUESTION # 50
IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data The following are high priorities for the company:
* Solution simplicity
* Fast development time
* Low cost
* High flexibility
What technologies meet the company's requirements?

A. Amazon DynamoDB and DynamoDB Accelerator (DAX)
B. Amazon S3 and Amazon Athena
C. Amazon RDS and Amazon ES
D. Amazon Redshift and AWS Glue

Answer: D

NEW QUESTION # 51
An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

A. Listwise deletion
B. Mean substitution
C. Last observation carried forward
D. Multiple imputation

Answer: D

Explanation:
Multiple imputation is a technique that uses machine learning to generate multiple plausible values for each missing value in a dataset, based on the observed data and the relationships among the variables. Multiple imputation preserves the integrity of the dataset by accounting for the uncertainty and variability of the missing data, and avoids the bias and loss of information that may result from other methods, such as listwise deletion, last observation carried forward, or mean substitution. Multiple imputation can improve the accuracy and validity of statistical analysis and machine learning models that use the imputed dataset. References:
* Managing missing values in your target and related datasets with automated imputation support in Amazon Forecast
* Imputation by feature importance (IBFI): A methodology to impute missing data in large datasets
* Multiple Imputation by Chained Equations (MICE) Explained

NEW QUESTION # 52
A company that runs an online library is implementing a chatbot using Amazon Lex to provide book recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are only three categories implemented as the custom slot types: "comedy," "adventure," and "documentary." A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because Amazon Lex cannot understand the category spoken by users with utterances such as "funny," "fun," and "humor." The ML specialist needs to fix the problem without changing the Lambda code or data in DynamoDB.
How should the ML specialist fix the problem?

A. Create a new custom slot type, add the unrecognized words to this slot type as enumeration values, and use this slot type for the slot.
B. Add the unrecognized words as synonyms in the custom slot type.
C. Add the unrecognized words in the enumeration values list as new values in the slot type.
D. Use the AMAZON.SearchQuery built-in slot types for custom searches in the database.

Answer: B

Explanation:
The best way to fix the problem without changing the Lambda code or data in DynamoDB is to add the unrecognized words as synonyms in the custom slot type. This way, Amazon Lex can resolve the synonyms to the corresponding slot values and pass them to the Lambda function. For example, if the slot type has a value "comedy" with synonyms "funny", "fun", and "humor", then any of these words entered by the user will be resolved to "comedy" and the Lambda function can query the DynamoDB table for the book titles in that category. Adding synonyms to the custom slot type can be done easily using the Amazon Lex console or API, and does not require any code changes.
The other options are not correct because:
Option A: Adding the unrecognized words in the enumeration values list as new values in the slot type would not fix the problem, because the Lambda function and the DynamoDB table are not aware of these new values. The Lambda function would not be able to query the DynamoDB table for the book titles in the new categories, and the request would still fail. Moreover, adding new values to the slot type would increase the complexity and maintenance of the chatbot, as the Lambda function and the DynamoDB table would have to be updated accordingly.
Option B: Creating a new custom slot type, adding the unrecognized words to this slot type as enumeration values, and using this slot type for the slot would also not fix the problem, for the same reasons as option A. The Lambda function and the DynamoDB table would not be able to handle the new slot type and its values, and the request would still fail. Furthermore, creating a new slot type would require more effort and time than adding synonyms to the existing slot type.
Option C: Using the AMAZON.SearchQuery built-in slot types for custom searches in the database is not a suitable approach for this use case. The AMAZON.SearchQuery slot type is used to capture free-form user input that corresponds to a search query. However, this slot type does not perform any validation or resolution of the user input, and passes the raw input to the Lambda function. This means that the Lambda function would have to handle the logic of parsing and matching the user input to the DynamoDB table, which would require changing the Lambda code and adding more complexity to the solution.
References:
Custom slot type - Amazon Lex
Using Synonyms - Amazon Lex
Built-in Slot Types - Amazon Lex

NEW QUESTION # 53
A company is building a new supervised classification model in an AWS environment. The company's data science team notices that the dataset has a large quantity of variables Ail the variables are numeric. The model accuracy for training and validation is low. The model's processing time is affected by high latency The data science team needs to increase the accuracy of the model and decrease the processing.
How it should the data science team do to meet these requirements?

A. Create new features and interaction variables.
B. Use a multiple correspondence analysis (MCA) model
C. Use a principal component analysis (PCA) model.
D. Apply normalization on the feature set.

Answer: C

Explanation:
The best way to meet the requirements is to use a principal component analysis (PCA) model, which is a technique that reduces the dimensionality of the dataset by transforming the original variables into a smaller set of new variables, called principal components, that capture most of the variance and information in the data1. This technique has the following advantages:
* It can increase the accuracy of the model by removing noise, redundancy, and multicollinearity from the data, and by enhancing the interpretability and generalization of the model23.
* It can decrease the processing time of the model by reducing the number of features and the computational complexity of the model, and by improving the convergence and stability of the model45.
* It is suitable for numeric variables, as it relies on the covariance or correlation matrix of the data, and it can handle a large quantity of variables, as it can extract the most relevant ones16.
The other options are not effective or appropriate, because they have the following drawbacks:
* A: Creating new features and interaction variables can increase the accuracy of the model by capturing more complex and nonlinear relationships in the data, but it can also increase the processing time of the model by adding more features and increasing the computational complexity of the model7. Moreover, it can introduce more noise, redundancy, and multicollinearity in the data, which can degrade the performance and interpretability of the model8.
* C: Applying normalization on the feature set can increase the accuracy of the model by scaling the features to a common range and avoiding the dominance of some features over others, but it can also decrease the processing time of the model by reducing the numerical instability and improving the convergence of the model . However, normalization alone is not enough to address the high dimensionality and high latency issues of the dataset, as it does not reduce the number of features or the variance in the data.
* D: Using a multiple correspondence analysis (MCA) model is not suitable for numeric variables, as it is a technique that reduces the dimensionality of the dataset by transforming the original categorical variables into a smaller set of new variables, called factors, that capture most of the inertia and information in the data. MCA is similar to PCA, but it is designed for nominal or ordinal variables, not for continuous or interval variables.
References:
* 1: Principal Component Analysis - Amazon SageMaker
* 2: How to Use PCA for Data Visualization and Improved Performance in Machine Learning | by Pratik Shukla | Towards Data Science
* 3: Principal Component Analysis (PCA) for Feature Selection and some of its Pitfalls | by Nagesh Singh Chauhan | Towards Data Science
* 4: How to Reduce Dimensionality with PCA and Train a Support Vector Machine in Python | by James Briggs | Towards Data Science
* 5: Dimensionality Reduction and Its Applications | by Aniruddha Bhandari | Towards Data Science
* 6: Principal Component Analysis (PCA) in Python | by Susan Li | Towards Data Science
* 7: Feature Engineering for Machine Learning | by Dipanjan (DJ) Sarkar | Towards Data Science
* 8: Feature Engineering - How to Engineer Features and How to Get Good at It | by Parul Pandey | Towards Data Science
* : [Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs.
Standardization | by Benjamin Obi Tayo Ph.D. | Towards Data Science]
* : [Why, How and When to Scale your Features | by George Seif | Towards Data Science]
* : [Normalization vs Dimensionality Reduction | by Saurabh Annadate | Towards Data Science]
* : [Multiple Correspondence Analysis - Amazon SageMaker]
* : [Multiple Correspondence Analysis (MCA) | by Raul Eulogio | Towards Data Science]

NEW QUESTION # 54
......

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Authentic Amazon AWS-Certified-Machine-Learning-Specialty Dumps PDF - The Best Way To Pass Exam

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q49-Q54):

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