VALID AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY EXAM QUESTIONS AND ANSWERS - HOW TO PREPARE FOR AMAZON AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY: AWS CERTIFIED MACHINE LEARNING - SPECIALTY

Valid AWS-Certified-Machine-Learning-Specialty Exam Questions And Answers - How to Prepare for Amazon AWS-Certified-Machine-Learning-Specialty: AWS Certified Machine Learning - Specialty

Valid AWS-Certified-Machine-Learning-Specialty Exam Questions And Answers - How to Prepare for Amazon AWS-Certified-Machine-Learning-Specialty: AWS Certified Machine Learning - Specialty

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Pattern of the Exam

MLS-C01 test contains questions in the form of multiple choice (with only one correct option) and multiple answer (more than 1 correct response). The candidates will get 180 minutes to finish the exam. Also, they need to pay $300 for registration. They can also choose preferred language from the options such as English, Simplified Chinese, Korean, and Japanese. Finally, one has the opportunity of taking the exam online or in a testing center. Topics covered include ML operations as well as implementation, exploratory data analysis, data engineering, and modeling.

The AWS Certified Machine Learning - Specialty certification exam consists of 65 multiple-choice and multiple-response questions, and candidates are given 3 hours to complete the exam. AWS-Certified-Machine-Learning-Specialty Exam is available online and can be taken from anywhere in the world. To prepare for the exam, candidates can take advantage of various resources provided by AWS, including online training courses, practice exams, and study guides.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q40-Q45):

NEW QUESTION # 40
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. Random Cut Forest (RCF)
  • C. Seq2seq
  • 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 # 41
A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings.
The company stores audit documents in text format. Auditors have requested help from a data science team to quickly analyze the documents. The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members. Documents that describe adverse events must receive the highest priority.
A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic.
Which algorithms are best suited to this scenario? (Choose two.)

  • A. Latent Dirichlet allocation (LDA)
  • B. Linear support vector machine
  • C. Neural topic modeling (NTM)
  • D. Random Forest classifier
  • E. Linear regression

Answer: A,C

Explanation:
Explanation
The algorithms that are best suited to this scenario are latent Dirichlet allocation (LDA) and neural topic modeling (NTM), as they are both unsupervised learning methods that can discover abstract topics from a collection of text documents. LDA and NTM can provide a list of the top words for each topic, as well as the topic distribution for each document, which can help the auditors assess the relevance and priority of the topic12.
The other options are not suitable because:
Option B: A random forest classifier is a supervised learning method that can perform classification or regression tasks by using an ensemble of decision trees. A random forest classifier is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes3.
Option D: A linear support vector machine is a supervised learning method that can perform classification or regression tasks by using a linear function that separates the data into different classes. A linear support vector machine is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes4.
Option E: A linear regression is a supervised learning method that can perform regression tasks by using a linear function that models the relationship between a dependent variable and one or more independent variables. A linear regression is not suitable for discovering abstract topics from text documents, as it requires labeled data and a continuous output variable5.
References:
1: Latent Dirichlet Allocation
2: Neural Topic Modeling
3: Random Forest Classifier
4: Linear Support Vector Machine
5: Linear Regression


NEW QUESTION # 42
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. Use the AMAZON.SearchQuery built-in slot types for custom searches in the database.
  • B. 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.
  • C. Add the unrecognized words as synonyms in the custom slot type.
  • D. Add the unrecognized words in the enumeration values list as new values in the slot type.

Answer: A


NEW QUESTION # 43
A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing (NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers.
The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset.
Which solution for text extraction and entity detection will require the LEAST amount of effort?

  • A. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
  • B. Extract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
  • C. Extract text from receipt images by using Amazon Textract. Use the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities.
  • D. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities.

Answer: B

Explanation:
The best solution for text extraction and entity detection with the least amount of effort is to use Amazon Textract and Amazon Comprehend. These services are:
Amazon Textract for text extraction from receipt images. Amazon Textract is a machine learning service that can automatically extract text and data from scanned documents. It can handle different structures and formats of documents, such as PDF, TIFF, PNG, and JPEG, without any preprocessing steps. It can also extract key-value pairs and tables from documents1 Amazon Comprehend for entity detection and custom entity detection. Amazon Comprehend is a natural language processing service that can identify entities, such as dates, locations, and notes, from unstructured text. It can also detect custom entities, such as receipt numbers, by using a custom entity recognizer that can be trained with a small amount of labeled data2 The other options are not suitable because they either require more effort for text extraction, entity detection, or custom entity detection. For example:
Option A uses the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities. BlazingText is a supervised learning algorithm that can perform text classification and word2vec. It requires users to provide a large amount of labeled data, preprocess the data into a specific format, and tune the hyperparameters of the model3 Option B uses a deep learning OCR model from the AWS Marketplace and a NER deep learning model for text extraction and entity detection. These models are pre-trained and may not be suitable for the specific use case of receipt processing. They also require users to deploy and manage the models on Amazon SageMaker or Amazon EC2 instances4 Option D uses a deep learning OCR model from the AWS Marketplace for text extraction. This model has the same drawbacks as option B. It also requires users to integrate the model output with Amazon Comprehend for entity detection and custom entity detection.
References:
1: Amazon Textract - Extract text and data from documents
2: Amazon Comprehend - Natural Language Processing (NLP) and Machine Learning (ML)
3: BlazingText - Amazon SageMaker
4: AWS Marketplace: OCR


NEW QUESTION # 44
A company offers an online shopping service to its customers. The company wants to enhance the site's security by requesting additional information when customers access the site from locations that are different from their normal location. The company wants to update the process to call a machine learning (ML) model to determine when additional information should be requested.
The company has several terabytes of data from its existing ecommerce web servers containing the source IP addresses for each request made to the web server. For authenticated requests, the records also contain the login name of the requesting user.
Which approach should an ML specialist take to implement the new security feature in the web application?

  • A. Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the factorization machines (FM) algorithm.
  • B. Use Amazon SageMaker to train a model using the Object2Vec algorithm. Schedule updates and retraining of the model using new log data nightly.
  • C. Use Amazon SageMaker to train a model using the IP Insights algorithm. Schedule updates and retraining of the model using new log data nightly.
  • D. Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the IP Insights algorithm.

Answer: C

Explanation:
Explanation
The IP Insights algorithm is designed to capture associations between entities and IP addresses, and can be used to identify anomalous IP usage patterns. The algorithm can learn from historical data that contains pairs of entities and IP addresses, and can return a score that indicates how likely the pair is to occur. The company can use this algorithm to train a model that can detect when a customer is accessing the site from a different location than usual, and request additional information accordingly. The company can also schedule updates and retraining of the model using new log data nightly to keep the model up to date with the latest IP usage patterns.
The other options are not suitable for this use case because:
Option A: The factorization machines (FM) algorithm is a general-purpose supervised learning algorithm that can be used for both classification and regression tasks. However, it is not optimized for capturing associations between entities and IP addresses, and would require labeling each record as either a successful or failed access attempt, which is a costly and time-consuming process.
Option C: The IP Insights algorithm is a good choice for this use case, but it does not require labeling each record as either a successful or failed access attempt. The algorithm is unsupervised and can learn from the historical data without labels. Labeling the data would be unnecessary and wasteful.
Option D: The Object2Vec algorithm is a general-purpose neural embedding algorithm that can learn low-dimensional dense embeddings of high-dimensional objects. However, it is not designed to capture associations between entities and IP addresses, and would require a different input format than the one provided by the company. The Object2Vec algorithm expects pairs of objects and their relationship labels or scores as inputs, while the company has data containing the source IP addresses and the login names of the requesting users.
References:
IP Insights - Amazon SageMaker
Factorization Machines Algorithm - Amazon SageMaker
Object2Vec Algorithm - Amazon SageMaker


NEW QUESTION # 45
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