100% PASS QUIZ 2025 AMAZON MLS-C01–HIGH PASS-RATE NEW BRAINDUMPS FILES

100% Pass Quiz 2025 Amazon MLS-C01–High Pass-Rate New Braindumps Files

100% Pass Quiz 2025 Amazon MLS-C01–High Pass-Rate New Braindumps Files

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Amazon MLS-C01 Exam covers a variety of topics related to machine learning on AWS. These topics include data engineering, exploratory data analysis, feature engineering, model selection and training, optimization techniques, and deployment and operationalization of machine learning models. MLS-C01 exam also covers important AWS services such as Amazon SageMaker, Amazon S3, Amazon EC2, and Amazon EMR.

The AWS Certified Machine Learning - Specialty exam is one of the most sought-after certifications in the field of machine learning. It is designed for individuals who want to validate their skills and expertise in developing, deploying, and maintaining machine learning solutions. MLS-C01 exam is conducted by Amazon Web Services (AWS) and is recognized globally as a standard for measuring the knowledge and skills of professionals in this field.

The AWS Certified Machine Learning - Specialty certification exam is a professional-level certification that validates the candidate's ability to design, implement, deploy, and maintain machine learning solutions on AWS. AWS Certified Machine Learning - Specialty certification exam is intended for data scientists, software developers, and machine learning practitioners who want to demonstrate their expertise in building and deploying ML solutions on AWS. Passing this certification exam is a valuable credential for professionals seeking to advance their careers in the field of machine learning.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q111-Q116):

NEW QUESTION # 111
A Machine Learning Specialist was given a dataset consisting of unlabeled data The Specialist must create a model that can help the team classify the data into different buckets What model should be used to complete this work?

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

Answer: A


NEW QUESTION # 112
An ecommerce company sends a weekly email newsletter to all of its customers. Management has hired a team of writers to create additional targeted content. A data scientist needs to identify five customer segments based on age, income, and location. The customers' current segmentation is unknown. The data scientist previously built an XGBoost model to predict the likelihood of a customer responding to an email based on age, income, and location.
Why does the XGBoost model NOT meet the current requirements, and how can this be fixed?

  • A. The XGBoost model is a supervised machine learning algorithm. Train a k-Nearest-Neighbors (kNN) model with K = 5 on the same dataset to predict a segment.
  • B. The XGBoost model provides a true/false binary output. Apply principal component analysis (PCA) with five feature dimensions to predict a segment.
  • C. The XGBoost model provides a true/false binary output. Increase the number of classes the XGBoost model predicts to five classes to predict a segment.
  • D. The XGBoost model is a supervised machine learning algorithm. Train a k-means model with K = 5 on the same dataset to predict a segment.

Answer: D

Explanation:
The XGBoost model is a supervised machine learning algorithm, which means it requires labeled data to learn from. The customers' current segmentation is unknown, so there is no label to train the XGBoost model on. Moreover, the XGBoost model is designed for classification or regression tasks, not for clustering.
Clustering is a type of unsupervised machine learning, which means it does not require labeled data.
Clustering algorithms try to find natural groups or clusters in the data based on their similarity or distance. A common clustering algorithm is k-means, which partitions the data into K clusters, where each data point belongs to the cluster with the nearest mean. To meet the current requirements, the data scientist should train a k-means model with K = 5 on the same dataset to predict a segment for each customer. This way, the data scientist can identify five customer segments based on age, income, and location, without needing any labels. References:
* What is XGBoost? - Amazon SageMaker
* What is Clustering? - Amazon SageMaker
* K-Means Algorithm - Amazon SageMaker


NEW QUESTION # 113
A media company is building a computer vision model to analyze images that are on social media. The model consists of CNNs that the company trained by using images that the company stores in Amazon S3. The company used an Amazon SageMaker training job in File mode with a single Amazon EC2 On-Demand Instance.
Every day, the company updates the model by using about 10,000 images that the company has collected in the last 24 hours. The company configures training with only one epoch. The company wants to speed up training and lower costs without the need to make any code changes.
Which solution will meet these requirements?

  • A. Instead Of On-Demand Instances, configure the SageMaker training job to use Spot Instances.Implement model checkpoints.
  • B. Instead of File mode, configure the SageMaker training job to use Pipe mode. Ingest the data from a pipe.
  • C. Instead Of On-Demand Instances, configure the SageMaker training job to use Spot Instances. Make no Other changes.
  • D. Instead Of File mode, configure the SageMaker training job to use FastFile mode with no Other changes.

Answer: C

Explanation:
The solution C will meet the requirements because it uses Amazon SageMaker Spot Instances, which are unused EC2 instances that are available at up to 90% discount compared to On-Demand prices. Amazon SageMaker Spot Instances can speed up training and lower costs by taking advantage of the spare EC2 capacity. The company does not need to make any code changes to use Spot Instances, as it can simply enable the managed spot training option in the SageMaker training job configuration. The company also does not need to implement model checkpoints, as it is using only one epoch for training, which means the model will not resume from a previous state1.
The other options are not suitable because:
* Option A: Configuring the SageMaker training job to use Pipe mode instead of File mode will not speed up training or lower costs significantly. Pipe mode is a data ingestion mode that streams data directly from S3 to the training algorithm, without copying the data to the local storage of the training instance. Pipe mode can reduce the startup time of the training job and the disk space usage, but it does not affect the computation time or the instance price. Moreover, Pipe mode may require some code changes to handle the streaming data, depending on the training algorithm2.
* Option B: Configuring the SageMaker training job to use FastFile mode instead of File mode will not speed up training or lower costs significantly. FastFile mode is a data ingestion mode that copies data from S3 to the local storage of the training instance in parallel with the training process. FastFile mode can reduce the startup time of the training job and the disk space usage, but it does not affect the computation time or the instance price. Moreover, FastFile mode is only available for distributed training jobs that use multiple instances, which is not the case for the company3.
* Option D: Configuring the SageMaker training job to use Spot Instances and implementing model checkpoints will not meet the requirements without the need to make any code changes. Model checkpoints are a feature that allows the training job to save the model state periodically to S3, and resume from the latest checkpoint if the training job is interrupted. Model checkpoints can help to avoid losing the training progress and ensure the model convergence, but they require some code changes to implement the checkpointing logic and the resuming logic4.
1: Managed Spot Training - Amazon SageMaker
2: Pipe Mode - Amazon SageMaker
3: FastFile Mode - Amazon SageMaker
4: Checkpoints - Amazon SageMaker


NEW QUESTION # 114
A company wants to detect credit card fraud. The company has observed that an average of 2% of credit card transactions are fraudulent. A data scientist trains a classifier on a year's worth of credit card transaction data.
The classifier needs to identify the fraudulent transactions. The company wants to accurately capture as many fraudulent transactions as possible.
Which metrics should the data scientist use to optimize the classifier? (Select TWO.)

  • A. False positive rate
  • B. Fl score
  • C. Specificity
  • D. Accuracy
  • E. True positive rate

Answer: B,E

Explanation:
The F1 score is a measure of the harmonic mean of precision and recall, which are both important for fraud detection. Precision is the ratio of true positives to all predicted positives, and recall is the ratio of true positives to all actual positives. A high F1 score indicates that the classifier can correctly identify fraudulent transactions and avoid false negatives. The true positive rate is another name for recall, and it measures the proportion of fraudulent transactions that are correctly detected by the classifier. A high true positive rate means that the classifier can capture as many fraudulent transactions as possible.
References:
* Fraud Detection Using Machine Learning | Implementations | AWS Solutions
* Detect fraudulent transactions using machine learning with Amazon SageMaker | AWS Machine Learning Blog
* 1. Introduction - Reproducible Machine Learning for Credit Card Fraud Detection


NEW QUESTION # 115
A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.
Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Select TWO)

  • A. Use a classification algorithm to run predictions
  • B. Use a regression algorithm to run predictions.
  • C. Use the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named "enrolled" or "not enrolled."
  • D. Use a forecasting algorithm to run predictions.
  • E. Use Amazon SageMaker Ground Truth to sort the data into two groups named "enrolled" or "not enrolled."

Answer: A,E

Explanation:
The data scientist should use Amazon SageMaker Ground Truth to sort the data into two groups named "enrolled" or "not enrolled." This will create a labeled dataset that can be used for supervised learning. The data scientist should then use a classification algorithm to run predictions on the test data. A classification algorithm is a suitable choice for predicting a binary outcome, such as enrollment status, based on the input features, such as academic performance. A classification algorithm will output a probability for each class label and assign the most likely label to each observation.
References:
Use Amazon SageMaker Ground Truth to Label Data
Classification Algorithm in Machine Learning


NEW QUESTION # 116
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