MLS-C01 COMPLETE EXAM DUMPS & EXAMCOLLECTION MLS-C01 QUESTIONS ANSWERS

MLS-C01 Complete Exam Dumps & Examcollection MLS-C01 Questions Answers

MLS-C01 Complete Exam Dumps & Examcollection MLS-C01 Questions Answers

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To become certified, candidates must pass the MLS-C01 Exam. MLS-C01 exam is available in multiple languages and can be taken at a testing center or online through a proctored exam. Candidates who pass the exam will receive the AWS Certified Machine Learning - Specialty certification, which is valid for three years.

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The AWS Certified Machine Learning - Specialty exam consists of multiple-choice and multiple-response questions. MLS-C01 Exam is designed to test the candidates' knowledge of AWS machine learning services, algorithms, and data science tools. Candidates are required to demonstrate their ability to design, implement, and maintain machine learning solutions using AWS services such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q72-Q77):

NEW QUESTION # 72
A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population.
Which cross-validation strategy should the Data Scientist adopt?

  • A. A k-fold cross-validation strategy with k=5
  • B. A k-fold cross-validation strategy with k=5 and 3 repeats
  • C. A stratified k-fold cross-validation strategy with k=5
  • D. An 80/20 stratified split between training and validation

Answer: C

Explanation:
Explanation
A stratified k-fold cross-validation strategy is a technique that preserves the class distribution in each fold.
This is important for imbalanced datasets, such as the one in the question, where the disease is seen in only 3% of the population. If a random k-fold cross-validation strategy is used, some folds may have no positive cases or very few, which would lead to poor estimates of the model performance. A stratified k-fold cross-validation strategy ensures that each fold has the same proportion of positive and negative cases as the whole dataset, which makes the evaluation more reliable and robust. A k-fold cross-validation strategy with k=5 and 3 repeats is also a possible option, but it is more computationally expensive and may not be necessary if the stratification is done properly. An 80/20 stratified split between training and validation is another option, but it uses less data for training and validation than k-fold cross-validation, which may result in higher variance and lower accuracy of the estimates. References:
AWS Machine Learning Specialty Certification Exam Guide
AWS Machine Learning Training: Model Evaluation
How to Fix k-Fold Cross-Validation for Imbalanced Classification


NEW QUESTION # 73
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?

  • A. Search for duplicate accounts in the AWS Glue Data Catalog.
  • B. Create a FindMatches machine learning transform in AWS Glue.
  • C. Execute the built-in FindDuplicates Amazon Athena query.
  • D. Create an AWS Glue crawler to infer duplicate accounts in the source data.

Answer: B

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html


NEW QUESTION # 74
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:
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 # 75
Each morning, a data scientist at a rental car company creates insights about the previous day's rental car reservation demands. The company needs to automate this process by streaming the data to Amazon S3 in near real time. The solution must detect high-demand rental cars at each of the company's locations. The solution also must create a visualization dashboard that automatically refreshes with the most recent data.
Which solution will meet these requirements with the LEAST development time?

  • A. Use Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.
  • B. Use Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker.
    Visualize the data in Amazon QuickSight.
  • C. Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker.
    Visualize the data in Amazon QuickSight.
  • D. Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.

Answer: D

Explanation:
Explanation
The solution that will meet the requirements with the least development time is to use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3, detect high-demand outliers by using Amazon QuickSight ML Insights, and visualize the data in QuickSight. This solution does not require any custom development or ML domain expertise, as it leverages the built-in features of QuickSight ML Insights to automatically run anomaly detection and generate insights on the streaming data. QuickSight ML Insights can also create a visualization dashboard that automatically refreshes with the most recent data, and allows the data scientist to explore the outliers and their key drivers. References:
1: Simplify and automate anomaly detection in streaming data with Amazon Lookout for Metrics | AWS Machine Learning Blog
2: Detecting outliers with ML-powered anomaly detection - Amazon QuickSight
3: Real-time Outlier Detection Over Streaming Data - IEEE Xplore
4: Towards a deep learning-based outlier detection ... - Journal of Big Data


NEW QUESTION # 76
A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago Which method should the Specialist try to improve model performance?

  • A. The model's hyperparameters should be periodically updated to prevent drift
  • B. The model needs to be completely re-engineered because it is unable to handle product inventory changes
  • C. The model should be periodically retrained using the original training data plus new data as product inventory changes
  • D. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes

Answer: C

Explanation:
The problem that the Machine Learning Specialist is facing is likely due to concept drift, which is a phenomenon where the statistical properties of the target variable change over time, making the model less accurate and relevant. Concept drift can occur due to various reasons, such as changes in customer preferences, market trends, product inventory, seasonality, etc. In this case, the product recommendations model may have become outdated as the product inventory changed over time, making the recommendations less appealing to the customers. To address this issue, the model should be periodically retrained using the original training data plus new data as product inventory changes. This way, the model can learn from the latest data and adapt to the changing customer behavior and preferences. Retraining the model from scratch using the original data while adding a regularization term may not be sufficient, as it does not account for the new data. Updating the model's hyperparameters may not help either, as it does not address the underlying data distribution change. Re-engineering the model completely may not be necessary, as the model may still be valid and useful with periodic retraining.
References:
Concept Drift - Amazon SageMaker
Detecting and Handling Concept Drift - Amazon SageMaker
Machine Learning Concepts - Amazon Machine Learning


NEW QUESTION # 77
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2025 Latest ITCertMagic MLS-C01 PDF Dumps and MLS-C01 Exam Engine Free Share: https://drive.google.com/open?id=1BriGeew8KJEI8XNvH7HoqlwujVSztaCX

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