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Recommended Experience

Before registering for the AWS Certified Machine Learning – Specialty exam, the applicant should ensure meeting some prerequisites as stated by the vendor. First, working experience of 1-2 years in running ML workloads as well as their development and architecting on AWS cloud is a must for the candidate. Moreover, it is recommended to have practical skills in executing the hyperparameter optimization, deep learning and ML frameworks, and operational and model-training best practices for AWS Machine learning.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam that is designed to validate the skills and knowledge of individuals who want to work with machine learning technologies on the AWS platform. MLS-C01 exam is intended for individuals who have a deep understanding of machine learning concepts, as well as experience working with AWS services and tools. MLS-C01 Exam measures the extent to which candidates can design, deploy, and maintain machine learning solutions on AWS.

Earning the AWS Certified Machine Learning - Specialty certification is beneficial for professionals in the field of machine learning. AWS Certified Machine Learning - Specialty certification validates their skills and knowledge in designing and deploying machine learning solutions using AWS services. It also demonstrates their proficiency in using AWS tools and services to build and maintain machine learning models. AWS Certified Machine Learning - Specialty certification can help professionals advance their careers and increase their earning potential.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q127-Q132):

NEW QUESTION # 127
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is
99.1%, but the Data Scientist needs to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false negative predictions by the model? (Choose two.)

A. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
B. Change the XGBoost eval_metric parameter to optimize based on Root Mean Square Error (RMSE).
C. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
D. Change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC).
E. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

Answer: D,E

Explanation:
The Data Scientist should increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights and change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC). This will help reduce the number of false negative predictions by the model.
The scale_pos_weight parameter controls the balance of positive and negative weights in the XGBoost algorithm. It is useful for imbalanced classification problems, such as fraud detection, where the number of positive examples (fraudulent transactions) is much smaller than the number of negative examples (non- fraudulent transactions). By increasing the scale_pos_weight parameter, the Data Scientist can assign more weight to the positive class and make the model more sensitive to detecting fraudulent transactions.
The eval_metric parameter specifies the metric that is used to measure the performance of the model during training and validation. The default metric for binary classification problems is the error rate, which is the fraction of incorrect predictions. However, the error rate is not a good metric for imbalanced classification problems, because it does not take into account the cost of different types of errors. For example, in fraud detection, a false negative (failing to detect a fraudulent transaction) is more costly than a false positive (flagging a non-fraudulent transaction as fraudulent). Therefore, the Data Scientist should use a metric that reflects the trade-off between the true positive rate (TPR) and the false positive rate (FPR), such as the Area Under the ROC Curve (AUC). The AUC is a measure of how well the model can distinguish between the positive and negative classes, regardless of the classification threshold. A higher AUC means that the model can achieve a higher TPR with a lower FPR, which is desirable for fraud detection.
References:
* XGBoost Parameters - Amazon Machine Learning
* Using XGBoost with Amazon SageMaker - AWS Machine Learning Blog

NEW QUESTION # 128
A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day, the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.
Which storage scheme is MOST adapted to this scenario?

A. Store datasets as global tables in Amazon DynamoDB.
B. Store datasets as tables in a multi-node Amazon Redshift cluster.
C. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
D. Store datasets as files in Amazon S3.

Answer: D

NEW QUESTION # 129
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes.
What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

A. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.
B. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
C. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
D. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.

Answer: C

Explanation:
Explanation

NEW QUESTION # 130
A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.
Which solution will meet these requirements?

A. Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.
B. Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.
C. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.
D. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.

Answer: A

NEW QUESTION # 131
A Machine Learning Specialist wants to determine the appropriate SageMakerVariant Invocations Per Instance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS As this is the first deployment, the Specialist intends to set the invocation safety factor to 0 5 Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the sageMakervariantinvocationsPerinstance setting?

A. 0
B. 2,400
C. 1
D. 2

Answer: C

NEW QUESTION # 132
......

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