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The Amazon MLS-C01 Exam consists of multiple-choice questions and is designed to test an individual's understanding of machine learning concepts and their ability to apply these concepts in real-world scenarios. MLS-C01 exam covers a wide range of topics, including data preparation, feature engineering, model training and evaluation, and model deployment. Candidates are also expected to have a strong understanding of AWS services such as Amazon SageMaker, Amazon S3, and AWS Lambda.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q261-Q266):

NEW QUESTION # 261
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 files in Amazon S3.
B. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
C. Store datasets as global tables in Amazon DynamoDB.
D. Store datasets as tables in a multi-node Amazon Redshift cluster.

Answer: A

Explanation:
The best storage scheme for this scenario is to store datasets as files in Amazon S3. Amazon S3 is a scalable, cost-effective, and durable object storage service that can store any amount and type of data. Amazon S3 also supports querying data using SQL with Amazon Athena, a serverless interactive query service that can analyze data directly in S3. This way, the Data Science team can easily explore and analyze their datasets without having to load them into a database or a compute instance.
The other options are not as suitable for this scenario because:
* Storing datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance would limit the scalability and availability of the data, as EBS volumes are only accessible within a single availability zone and have a maximum size of 16 TiB. Also, EBS volumes are more expensive than S3 buckets and require provisioning and managing EC2 instances.
* Storing datasets as tables in a multi-node Amazon Redshift cluster would incur higher costs and complexity than using S3 and Athena. Amazon Redshift is a data warehouse service that is optimized for analytical queries over structured or semi-structured data. However, it requires setting up and maintaining a cluster of nodes, loading data into tables, and choosing the right distribution and sort keys for optimal performance. Moreover, Amazon Redshift charges for both storage and compute, while S3 and Athena only charge for the amount of data stored and scanned, respectively.
* Storing datasets as global tables in Amazon DynamoDB would not be feasible for large amounts of data, as DynamoDB is a key-value and document database service that is designed for fast and consistent performance at any scale. However, DynamoDB has a limit of 400 KB per item and 25 GB per partition key value, which may not be enough for storing large datasets. Also, DynamoDB does not support SQL queries natively, and would require using a service like Amazon EMR or AWS Glue to run SQL queries over DynamoDB data.
Amazon S3 - Cloud Object Storage
Amazon Athena - Interactive SQL Queries for Data in Amazon S3
Amazon EBS - Amazon Elastic Block Store (EBS)
Amazon Redshift - Data Warehouse Solution - AWS
Amazon DynamoDB - NoSQL Cloud Database Service

NEW QUESTION # 262
A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model's accuracy. The learning rate parameter is specified in the following HPO configuration:
During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.
Which solution provides the MOST accurate result?

A. Modify the HPO configuration as follows:
Select the most accurate hyperparameter configuration form this training job.
B. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job:
[0.01, 0.1]
[0.001, 0.01]
[0.0001, 0.001]
Select the most accurate hyperparameter configuration form these three HPO jobs.
C. Modify the HPO configuration as follows:
Select the most accurate hyperparameter configuration form this HPO job.
D. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. Divide the number of training jobs for each HPO job by three:
[0.01, 0.1]
[0.001, 0.01]
[0.0001, 0.001]
Select the most accurate hyperparameter configuration form these three HPO jobs.

Answer: A

Explanation:
The solution C modifies the HPO configuration to use a logarithmic scale for the learning rate parameter. This means that the values of the learning rate are sampled from a log-uniform distribution, which gives more weight to smaller values. This can help to explore the lower end of the range more evenly and find the optimal learning rate more efficiently. The other solutions either use a linear scale, which may not sample enough values from the lower end, or divide the range into sub-intervals, which may miss some combinations of hyperparameters. References:
* How Hyperparameter Tuning Works - Amazon SageMaker
* Tuning Hyperparameters - Amazon SageMaker

NEW QUESTION # 263
An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images Which of the following should be used to resolve this issue? (Select TWO)

A. Add vanishing gradient to the model
B. Add L2 regularization to the model
C. Use gradient checking in the model
D. Perform data augmentation on the training data
E. Make the neural network architecture complex.

Answer: B,D

NEW QUESTION # 264
Example Corp has an annual sale event from October to December. The company has sequential sales data from the past 15 years and wants to use Amazon ML to predict the sales for this year's upcoming event. Which method should Example Corp use to split the data into a training dataset and evaluation dataset?

A. Perform custom cross-validation on the data
B. Pre-split the data before uploading to Amazon S3
C. Have Amazon ML split the data sequentially.
D. Have Amazon ML split the data randomly.

Answer: C

Explanation:
A sequential split is a method of splitting data into training and evaluation datasets while preserving the order of the data records. This method is useful when the data has a temporal or sequential structure, and the order of the data matters for the prediction task. For example, if the data contains sales data for different months or years, and the goal is to predict the sales for the next month or year, a sequential split can ensure that the training data comes from the earlier period and the evaluation data comes from the later period. This can help avoid data leakage, which occurs when the training data contains information from the future that is not available at the time of prediction. A sequential split can also help evaluate the model performance on the most recent data, which may be more relevant and representative of the future data.
In this question, Example Corp has sequential sales data from the past 15 years and wants to use Amazon ML to predict the sales for this year's upcoming annual sale event. A sequential split is the most appropriate method for splitting the data, as it can preserve the order of the data and prevent data leakage. For example, Example Corp can use the data from the first 14 years as the training dataset, and the data from the last year as the evaluation dataset. This way, the model can learn from the historical data and be tested on the most recent data.
Amazon ML provides an option to split the data sequentially when creating the training and evaluation datasources. To use this option, Example Corp can specify the percentage of the data to use for training and evaluation, and Amazon ML will use the first part of the data for training and the remaining part of the data for evaluation. For more information, see Splitting Your Data - Amazon Machine Learning.

NEW QUESTION # 265
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.
What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

A. L1 norm
B. Root Mean Square Error (RM&)
C. Misclassification rate
D. Receiver operating characteristic (ROC) curve

Answer: D

NEW QUESTION # 266
......

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Dan Shaw Dan Shaw

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