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To be eligible for the Amazon MLS-C01 Certification Exam, candidates should have a deep understanding of AWS services and features used for machine learning, such as Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, and Amazon Lex. Candidates should also have experience working with different types of data, such as structured, unstructured, and semi-structured data, and be familiar with various machine learning algorithms and techniques, such as regression, clustering, classification, and deep learning.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is designed for professionals who want to validate their expertise in building, training, and deploying machine learning models on the AWS cloud platform. AWS Certified Machine Learning - Specialty certification exam tests candidates on their ability to design and implement machine learning solutions using AWS services such as Amazon SageMaker, AWS Deep Learning AMIs, Amazon Rekognition, and Amazon Comprehend. AWS Certified Machine Learning - Specialty certification is ideal for data scientists, software developers, and machine learning engineers who want to demonstrate their skills in the field of machine learning.

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

NEW QUESTION # 127
A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server- side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?

A. onfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
B. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.
C. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
D. Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset.
Grant permission in the KMS key policy to that role.

Answer: B

Explanation:
https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html

NEW QUESTION # 128
A company is building a new supervised classification model in an AWS environment. The company's data science team notices that the dataset has a large quantity of variables Ail the variables are numeric. The model accuracy for training and validation is low. The model's processing time is affected by high latency The data science team needs to increase the accuracy of the model and decrease the processing.
How it should the data science team do to meet these requirements?

A. Create new features and interaction variables.
B. Use a multiple correspondence analysis (MCA) model
C. Apply normalization on the feature set.
D. Use a principal component analysis (PCA) model.

Answer: D

Explanation:
Explanation
The best way to meet the requirements is to use a principal component analysis (PCA) model, which is a technique that reduces the dimensionality of the dataset by transforming the original variables into a smaller set of new variables, called principal components, that capture most of the variance and information in the data1. This technique has the following advantages:
It can increase the accuracy of the model by removing noise, redundancy, and multicollinearity from the data, and by enhancing the interpretability and generalization of the model23.
It can decrease the processing time of the model by reducing the number of features and the computational complexity of the model, and by improving the convergence and stability of the model45.
It is suitable for numeric variables, as it relies on the covariance or correlation matrix of the data, and it can handle a large quantity of variables, as it can extract the most relevant ones16.
The other options are not effective or appropriate, because they have the following drawbacks:
A: Creating new features and interaction variables can increase the accuracy of the model by capturing more complex and nonlinear relationships in the data, but it can also increase the processing time of the model by adding more features and increasing the computational complexity of the model7. Moreover, it can introduce more noise, redundancy, and multicollinearity in the data, which can degrade the performance and interpretability of the model8.
C: Applying normalization on the feature set can increase the accuracy of the model by scaling the features to a common range and avoiding the dominance of some features over others, but it can also decrease the processing time of the model by reducing the numerical instability and improving the convergence of the model . However, normalization alone is not enough to address the high dimensionality and high latency issues of the dataset, as it does not reduce the number of features or the variance in the data.
D: Using a multiple correspondence analysis (MCA) model is not suitable for numeric variables, as it is a technique that reduces the dimensionality of the dataset by transforming the original categorical variables into a smaller set of new variables, called factors, that capture most of the inertia and information in the data. MCA is similar to PCA, but it is designed for nominal or ordinal variables, not for continuous or interval variables.
References:
1: Principal Component Analysis - Amazon SageMaker
2: How to Use PCA for Data Visualization and Improved Performance in Machine Learning | by Pratik Shukla | Towards Data Science
3: Principal Component Analysis (PCA) for Feature Selection and some of its Pitfalls | by Nagesh Singh Chauhan | Towards Data Science
4: How to Reduce Dimensionality with PCA and Train a Support Vector Machine in Python | by James Briggs | Towards Data Science
5: Dimensionality Reduction and Its Applications | by Aniruddha Bhandari | Towards Data Science
6: Principal Component Analysis (PCA) in Python | by Susan Li | Towards Data Science
7: Feature Engineering for Machine Learning | by Dipanjan (DJ) Sarkar | Towards Data Science
8: Feature Engineering - How to Engineer Features and How to Get Good at It | by Parul Pandey | Towards Data Science
9: [Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs.
Standardization | by Benjamin Obi Tayo Ph.D. | Towards Data Science]
1: [Why, How and When to Scale your Features | by George Seif | Towards Data Science]
2: [Normalization vs Dimensionality Reduction | by Saurabh Annadate | Towards Data Science]
3: [Multiple Correspondence Analysis - Amazon SageMaker]
4: [Multiple Correspondence Analysis (MCA) | by Raul Eulogio | Towards Data Science]

NEW QUESTION # 129
A Machine Learning Specialist is working for an online retailer that wants to run analytics on every customer visit, processed through a machine learning pipeline. The data needs to be ingested by Amazon Kinesis Data Streams at up to 100 transactions per second, and the JSON data blob is 100 KB in size.
What is the MINIMUM number of shards in Kinesis Data Streams the Specialist should use to successfully ingest this data?

A. 1 shards
B. 1,000 shards
C. 10 shards
D. 100 shards

Answer: C

NEW QUESTION # 130
A company is launching a new product and needs to build a mechanism to monitor comments about the company and its new product on social media. The company needs to be able to evaluate the sentiment expressed in social media posts, and visualize trends and configure alarms based on various thresholds.
The company needs to implement this solution quickly, and wants to minimize the infrastructure and data science resources needed to evaluate the messages. The company already has a solution in place to collect posts and store them within an Amazon S3 bucket.
What services should the data science team use to deliver this solution?

A. Trigger an AWS Lambda function when social media posts are added to the S3 bucket. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in an Amazon DynamoDB table. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.
B. Train a model in Amazon SageMaker by using the BlazingText algorithm to detect sentiment in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when posts are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table and in a custom Amazon CloudWatch metric. Use CloudWatch alarms to notify analysts of trends.
C. Train a model in Amazon SageMaker by using the semantic segmentation algorithm to model the semantic content in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when objects are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.
D. Trigger an AWS Lambda function when social media posts are added to the S3 bucket. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in a custom Amazon CloudWatch metric and in S3. Use CloudWatch alarms to notify analysts of trends.

Answer: D

Explanation:
Explanation
The solution that uses Amazon Comprehend and Amazon CloudWatch is the most suitable for the given scenario. Amazon Comprehend is a natural language processing (NLP) service that can analyze text and extract insights such as sentiment, entities, topics, and syntax. Amazon CloudWatch is a monitoring and observability service that can collect and track metrics, create dashboards, and set alarms based on various thresholds. By using these services, the data science team can quickly and easily implement a solution to monitor the sentiment of social media posts without requiring much infrastructure or data science resources.
The solution also meets the requirements of storing the sentiment in both S3 and CloudWatch, and using CloudWatch alarms to notify analysts of trends.
References:
Amazon Comprehend
Amazon CloudWatch

NEW QUESTION # 131
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)

A. Increase feature combinations.
B. Decrease feature combinations.
C. Increase regularization.
D. Decrease regularization.
E. Decrease dropout.
F. Increase dropout.

Answer: B,C,F

Explanation:
Explanation
The problem of poor performance on the test data is a sign of overfitting, which means the model has learned the training data too well and failed to generalize to new and unseen data. To correct this, the Machine Learning Specialist should consider using methods that reduce the complexity of the model and increase its ability to generalize. Some of these methods are:
Increase regularization: Regularization is a technique that adds a penalty term to the loss function of the model, which reduces the magnitude of the model weights and prevents overfitting. There are different types of regularization, such as L1, L2, and elastic net, that apply different penalties to the weights1.
Increase dropout: Dropout is a technique that randomly drops out some units or connections in the neural network during training, which reduces the co-dependency of the units and prevents overfitting. Dropout can be applied to different layers of the network, and the dropout rate can be tuned to control the amount of dropout2.
Decrease feature combinations: Feature combinations are the interactions between different input features that can be used to create new features for the model. However, too many feature combinations can increase the complexity of the model and cause overfitting. Therefore, the Specialist should decrease the number of feature combinations and select only the most relevant and informative ones for the model3.
References:
1: Regularization for Deep Learning - Amazon SageMaker
2: Dropout - Amazon SageMaker
3: Feature Engineering - Amazon SageMaker

NEW QUESTION # 132
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