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MLS-C01 Reliable Exam Labs, Reliable MLS-C01 Exam Papers

MLS-C01 Reliable Exam Labs, Reliable MLS-C01 Exam Papers

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AWS Certified Machine Learning - Specialty Exam, also known as AWS-Certified-Machine-Learning-Specialty, is a certification exam offered by Amazon Web Services. MLS-C01 Exam is designed for individuals who want to validate their expertise in designing, implementing, and deploying machine learning solutions using AWS services.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q252-Q257):

NEW QUESTION # 252
A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.
How should the Data Science team configure the notebook instance placement to meet these requirements?

  • A. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use 1AM policies to grant access to Amazon S3 and Amazon SageMaker.
  • B. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
  • C. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker
  • D. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.

Answer: B

Explanation:
Explanation
To configure the notebook instance placement to meet the requirements, the Data Science team should associate the Amazon SageMaker notebook with a private subnet in a VPC. A VPC is a virtual network that is logically isolated from other networks in AWS. A private subnet is a subnet that has no internet gateway attached to it, and therefore cannot communicate with the internet. By placing the notebook instance in a private subnet, the team can ensure that it stays within a secured VPC with no internet access.
However, to access data stored in Amazon S3 buckets and other AWS services, the team needs to ensure that the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it. A VPC endpoint is a gateway that enables private connections between the VPC and supported AWS services. A VPC endpoint does not require an internet gateway, a NAT device, or a VPN connection, and ensures that the traffic between the VPC and the AWS service does not leave the AWS network. By using VPC endpoints, the team can access Amazon S3 and Amazon SageMaker from the notebook instance without compromising data privacy or security.
References:
1: What Is Amazon VPC? - Amazon Virtual Private Cloud
2: Subnet Routing - Amazon Virtual Private Cloud
3: VPC Endpoints - Amazon Virtual Private Cloud


NEW QUESTION # 253
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen Which combination of algorithms would provide the appropriate insights? (Select TWO )

  • A. The Random Cut Forest (RCF) algorithm
  • B. The principal component analysis (PCA) algorithm
  • C. The Latent Dirichlet Allocation (LDA) algorithm
  • D. The k-means algorithm
  • E. The factorization machines (FM) algorithm

Answer: B,D

Explanation:
* The agency wants to analyze the census data for population segmentation, which is a type of unsupervised learning problem that aims to group similar data points together based on their attributes.
The agency can use a combination of algorithms that can perform dimensionality reduction and clustering on the data to achieve this goal.
* Dimensionality reduction is a technique that reduces the number of features or variables in a dataset while preserving the essential information and relationships. Dimensionality reduction can help improve the efficiency and performance of clustering algorithms, as well as facilitate data visualization and interpretation. One of the most common algorithms for dimensionality reduction is principal component analysis (PCA), which transforms the original features into a new set of orthogonal features called principal components that capture the maximum variance in the data. PCA can help reduce the noise and redundancy in the data and reveal the underlying structure and patterns.
* Clustering is a technique that partitions the data into groups or clusters based on their similarity or distance. Clustering can help discover the natural segments or categories in the data and understand their characteristics and differences. One of the most popular algorithms for clustering is k-means, which assigns each data point to one of k clusters based on the nearest mean or centroid. K-means can handle large and high-dimensional datasets and produce compact and spherical clusters.
* Therefore, the combination of algorithms that would provide the appropriate insights for population segmentation are PCA and k-means. The agency can use PCA to reduce the dimensionality of the census data from 500 features to a smaller number of principal components that capture most of the variation in the data. Then, the agency can use k-means to cluster the data based on the principal components and identify the segments of the population that share similar characteristics.
References:
* Amazon SageMaker Principal Component Analysis (PCA)
* Amazon SageMaker K-Means Algorithm


NEW QUESTION # 254
A company processes millions of orders every day. The company uses Amazon DynamoDB tables to store order information. When customers submit new orders, the new orders are immediately added to the DynamoDB tables. New orders arrive in the DynamoDB tables continuously.
A data scientist must build a peak-time prediction solution. The data scientist must also create an Amazon OuickSight dashboard to display near real-lime order insights. The data scientist needs to build a solution that will give QuickSight access to the data as soon as new order information arrives.
Which solution will meet these requirements with the LEAST delay between when a new order is processed and when QuickSight can access the new order information?

  • A. Use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3.
    Configure OuickSight to access the data in Amazon S3.
  • B. Use an API call from OuickSight to access the data that is in Amazon DynamoDB directly
  • C. Use Amazon Kinesis Data Firehose to export the data from Amazon DynamoDB to Amazon S3.Configure OuickSight to access the data in Amazon S3.
  • D. Use AWS Glue to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.

Answer: A

Explanation:
The best solution for this scenario is to use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3, and then configure QuickSight to access the data in Amazon S3. This solution has the following advantages:
* It allows near real-time data ingestion from DynamoDB to S3 using Kinesis Data Streams, which can capture and process data continuously and at scale1.
* It enables QuickSight to access the data in S3 using the Athena connector, which supports federated queries to multiple data sources, including Kinesis Data Streams2.
* It avoids the need to create and manage a Lambda function or a Glue crawler, which are required for the other solutions.
The other solutions have the following drawbacks:
* Using AWS Glue to export the data from DynamoDB to S3 introduces additional latency and complexity, as Glue is a batch-oriented service that requires scheduling and configuration3.
* Using an API call from QuickSight to access the data in DynamoDB directly is not possible, as QuickSight does not support direct querying of DynamoDB4.
* Using Kinesis Data Firehose to export the data from DynamoDB to S3 is less efficient and flexible than using Kinesis Data Streams, as Firehose does not support custom data processing or transformation, and has a minimum buffer interval of 60 seconds5.
References:
* 1: Amazon Kinesis Data Streams - Amazon Web Services
* 2: Visualize Amazon DynamoDB insights in Amazon QuickSight using the Amazon Athena DynamoDB connector and AWS Glue | AWS Big Data Blog
* 3: AWS Glue - Amazon Web Services
* 4: Visualising your Amazon DynamoDB data with Amazon QuickSight - DEV Community
* 5: Amazon Kinesis Data Firehose - Amazon Web Services


NEW QUESTION # 255
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 should be periodically retrained using the original training data plus new data as product inventory changes.
  • B. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
  • C. The model needs to be completely re-engineered because it is unable to handle product inventory changes.
  • D. The model's hyperparameters should be periodically updated to prevent drift.

Answer: A


NEW QUESTION # 256
A machine learning engineer is building a bird classification model. The engineer randomly separates a dataset into a training dataset and a validation dataset. During the training phase, the model achieves very high accuracy. However, the model did not generalize well during validation of the validation dataset. The engineer realizes that the original dataset was imbalanced.
What should the engineer do to improve the validation accuracy of the model?

  • A. Acquire additional data about the majority classes in the original dataset.
  • B. Use a smaller, randomly sampled version of the training dataset.
  • C. Perform stratified sampling on the original dataset.
  • D. Perform systematic sampling on the original dataset.

Answer: C

Explanation:
Stratified sampling is a technique that preserves the class distribution of the original dataset when creating a smaller or split dataset. This means that the proportion of examples from each class in the original dataset is maintained in the smaller or split dataset. Stratified sampling can help improve the validation accuracy of the model by ensuring that the validation dataset is representative of the original dataset and not biased towards any class. This can reduce the variance and overfitting of the model and increase its generalization ability.
Stratified sampling can be applied to both oversampling and undersampling methods, depending on whether the goal is to increase or decrease the size of the dataset.
The other options are not effective ways to improve the validation accuracy of the model. Acquiring additional data about the majority classes in the original dataset will only increase the imbalance and make the model more biased towards the majority classes. Using a smaller, randomly sampled version of the training dataset will not guarantee that the class distribution is preserved and may result in losing important information from the minority classes. Performing systematic sampling on the original dataset will also not ensure that the class distribution is preserved and may introduce sampling bias if the original dataset is ordered or grouped by class.
References:
*Stratified Sampling for Imbalanced Datasets
*Imbalanced Data
*Tour of Data Sampling Methods for Imbalanced Classification


NEW QUESTION # 257
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