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Pass Guaranteed Quiz 2026 Accurate MLS-C01: Valid AWS Certified Machine Learning

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Pass Guaranteed Quiz 2026 Accurate MLS-C01: Valid AWS Certified Machine Learning

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q204-Q209):NEW QUESTION # 204
A company provisions Amazon SageMaker notebook instances for its data science team and creates Amazon VPC interface endpoints to ensure communication between the VPC and the notebook instances. All connections to the Amazon SageMaker API are contained entirely and securely using the AWS network.
However, the data science team realizes that individuals outside the VPC can still connect to the notebook instances across the internet.
Which set of actions should the data science team take to fix the issue?
  • A. Add a NAT gateway to the VPC. Convert all of the subnets where the Amazon SageMaker notebook instances are hosted to private subnets. Stop and start all of the notebook instances to reassign only private IP addresses.
  • B. Change the network ACL of the subnet the notebook is hosted in to restrict access to anyone outside the VPC.
  • C. Create an IAM policy that allows the sagemaker:CreatePresignedNotebooklnstanceUrl and sagemaker:
    DescribeNotebooklnstance actions from only the VPC endpoints. Apply this policy to all IAM users, groups, and roles used to access the notebook instances.
  • D. Modify the notebook instances' security group to allow traffic only from the CIDR ranges of the VPC.
    Apply this security group to all of the notebook instances' VPC interfaces.
Answer: D
Explanation:
The issue is that the notebook instances' security group allows inbound traffic from any source IP address, which means that anyone with the authorized URL can access the notebook instances over the internet. To fix this issue, the data science team should modify the security group to allow traffic only from the CIDR ranges of the VPC, which are the IP addresses assigned to the resources within the VPC. This way, only the VPC interface endpoints and the resources within the VPC can communicate with the notebook instances. The data science team should apply this security group to all of the notebook instances' VPC interfaces, which are the network interfaces that connect the notebook instances to the VPC.
The other options are not correct because:
* Option B: Creating an IAM policy that allows the sagemaker:CreatePresignedNotebookInstanceUrl and sagemakerescribeNotebookInstance actions from only the VPC endpoints does not prevent individuals outside the VPC from accessing the notebook instances. These actions are used to generate and retrieve the authorized URL for the notebook instances, but they do not control who can use the URL to access the notebook instances. The URL can still be shared or leaked to unauthorized users, who can then access the notebook instances over the internet.
* Option C: Adding a NAT gateway to the VPC and converting the subnets where the notebook instances are hosted to private subnets does not solve the issue either. A NAT gateway is used to enable outbound internet access from a private subnet, but it does not affect inbound internet access. The notebook instances can still be accessed over the internet if their security group allows inbound traffic from any source IP address. Moreover, stopping and starting the notebook instances to reassign only private IP addresses is not necessary, because the notebook instances already have private IP addresses assigned by the VPC interface endpoints.
* Option D: Changing the network ACL of the subnet the notebook is hosted in to restrict access to anyone outside the VPC is not a good practice, because network ACLs are stateless and apply to the entire subnet. This means that the data science team would have to specify both the inbound and outbound rules for each IP address range that they want to allow or deny. This can be cumbersome and error-prone, especially if the VPC has multiple subnets and resources. It is better to use security groups, which are stateful and apply to individual resources, to control the access to the notebook instances.
Connect to SageMaker Within your VPC - Amazon SageMaker
Security Groups for Your VPC - Amazon Virtual Private Cloud
VPC Interface Endpoints - Amazon Virtual Private Cloud

NEW QUESTION # 205
A data engineer at a bank is evaluating a new tabular dataset that includes customer dat a. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.
Which steps should the data engineer take to address this issue? (Choose two.)
  • A. Apply min-max feature scaling to the dataset.
  • B. Remove a portion of highly correlated features from the dataset.
  • C. Apply principal component analysis (PCA).
  • D. Apply one-hot encoding category-based variables.
  • E. Use a linear-based algorithm to train the model.
Answer: B,C
Explanation:
B) Apply principal component analysis (PCA): PCA is a technique that reduces the dimensionality of a dataset by transforming the original features into a smaller set of new features that capture most of the variance in the data. PCA can help address the issue of multicollinearity, which occurs when some features are highly correlated with each other and can cause problems for some machine learning algorithms. By applying PCA, the data engineer can reduce the number of features and remove the redundancy in the data.
C) Remove a portion of highly correlated features from the dataset: Another way to deal with multicollinearity is to manually remove some of the features that are highly correlated with each other. This can help simplify the model and avoid overfitting. The data engineer can use the correlation matrix to identify the features that have a high correlation coefficient (e.g., above 0.8 or below -0.8) and remove one of them from the dataset. References: = Principal Component Analysis: This is a document from AWS that explains what PCA is, how it works, and how to use it with Amazon SageMaker.
Multicollinearity: This is a document from AWS that describes what multicollinearity is, how to detect it, and how to deal with it.

NEW QUESTION # 206
An ecommerce company has used Amazon SageMaker to deploy a factorization machines (FM) model to suggest products for customers. The company's data science team has developed two new models by using the TensorFlow and PyTorch deep learning frameworks. The company needs to use A/B testing to evaluate the new models against the deployed model.
...required A/B testing setup is as follows:
* Send 70% of traffic to the FM model, 15% of traffic to the TensorFlow model, and 15% of traffic to the Py Torch model.
* For customers who are from Europe, send all traffic to the TensorFlow model
..sh architecture can the company use to implement the required A/B testing setup?
  • A. Create two new SageMaker endpoints for the TensorFlow and PyTorch models in addition to the existing SageMaker endpoint. Create a Network Load Balancer. Create a target group for each endpoint.
    Configure listener rules and add weight to the target groups. To send traffic to the TensorFlow model for customers who are from Europe, create an additional listener rule to forward traffic to the TensorFlow target group.
  • B. Create two production variants for the TensorFlow and PyTorch models. Specify the weight for each production variant in the SageMaker endpoint configuration. Update the existing SageMaker endpoint with the new configuration. To send traffic to the TensorFlow model for customers who are from Europe, set the TargetVariant header in the request to point to the variant name of the TensorFlow model.
  • C. Create two new SageMaker endpoints for the TensorFlow and PyTorch models in addition to the existing SageMaker endpoint. Create an Application Load Balancer Create a target group for each endpoint. Configure listener rules and add weight to the target groups. To send traffic to the TensorFlow model for customers who are from Europe, create an additional listener rule to forward traffic to the TensorFlow target group.
  • D. Create two production variants for the TensorFlow and PyTorch models. Create an auto scaling policy and configure the desired A/B weights to direct traffic to each production variant Update the existing SageMaker endpoint with the auto scaling policy. To send traffic to the TensorFlow model for customers who are from Europe, set the TargetVariant header in the request to point to the variant name of the TensorFlow model.
Answer: B
Explanation:
Explanation
The correct answer is D because it allows the company to use the existing SageMaker endpoint and leverage the built-in functionality of production variants for A/B testing. Production variants can be used to test ML models that have been trained using different training datasets, algorithms, and ML frameworks; test how they perform on different instance types; or a combination of all of the above1. By specifying the weight for each production variant in the endpoint configuration, the company can control how much traffic to send to each variant. By setting the TargetVariant header in the request, the company can invoke a specific variant directly for each request2. This enables the company to implement the required A/B testing setup without creating additional endpoints or load balancers.
References:
1: Production variants - Amazon SageMaker
2: A/B Testing ML models in production using Amazon SageMaker | AWS Machine Learning Blog

NEW QUESTION # 207
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker. The historical training data is stored in Amazon RDS.
Which approach should the Specialist use for training a model using that data?
  • A. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.
  • B. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
  • C. Write a direct connection to the SQL database within the notebook and pull data in
  • D. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
Answer: B

NEW QUESTION # 208
A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format.
During model evaluation, the data scientist discovered that the model recommends certain stopwords such as
"a," "an," and "the" as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords.
What should the data scientist do to meet these requirements?
  • A. Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job.
  • B. Remove the stop words from the blog post data by using the Count Vectorizer function in the scikit- learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.
  • C. Use the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket.
  • D. Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.
Answer: B
Explanation:
The data scientist should remove the stop words from the blog post data by using the Count Vectorizer function in the scikit-learn library, and replace the blog post data in the S3 bucket with the results of the vectorizer. This is because:
* The Count Vectorizer function is a tool that can convert a collection of text documents to a matrix of token counts 1. It also enables the pre-processing of text data prior to generating the vector representation, such as removing accents, converting to lowercase, and filtering out stop words 1. By using this function, the data scientist can remove the stop words such as "a," "an," and "the" from the blog post data, and obtain a numerical representation of the text that can be used as input for the NTM algorithm.
* The NTM algorithm is a neural network-based topic modeling technique that can learn latent topics from a corpus of documents 2. It can be used to recommend tags from blog posts by finding the most probable topics for each document, and ranking the words associated with each topic 3. However, the NTM algorithm does not perform any text pre-processing by itself, so it relies on the quality of the input data. Therefore, the data scientist should replace the blog post data in the S3 bucket with the results of the vectorizer, to ensure that the NTM algorithm does not include the stop words in the tag recommendations.
* The other options are not suitable for the following reasons:
* Option A is not relevant because the Amazon Comprehend entity recognition API operations are used to detect and extract named entities from text, such as people, places, organizations, dates, etc4. This is not the same as removing stop words, which are common words that do not carry much meaning or information. Moreover, removing the detected entities from the blog post data may reduce the quality and diversity of the tag recommendations, as some entities may be relevant and useful as tags.
* Option B is not optimal because the SageMaker built-in principal component analysis (PCA) algorithm is used to reduce the dimensionality of a dataset by finding the most important features that capture the maximum amount of variance in the data 5. This is not the same as removing stop words, which are words that have low variance and high frequency in the data. Moreover, replacing the blog post data in the S3 bucket with the results of the PCA algorithm may not be compatible with the input format expected by the NTM algorithm, which requires a bag-of-words representation of the text 2.
* Option C is not suitable because the SageMaker built-in Object Detection algorithm is used to detect and localize objects in images 6. This is not related to the task of recommending tags from blog posts, which are text documents. Moreover, using the Object Detection algorithm instead of the NTM algorithm would require a different type of input data (images instead of text), and a different type of output data (bounding boxes and labels instead of topics and words).
Neural Topic Model (NTM) Algorithm
Introduction to the Amazon SageMaker Neural Topic Model
Amazon Comprehend - Entity Recognition
sklearn.feature_extraction.text.CountVectorizer
Principal Component Analysis (PCA) Algorithm
Object Detection Algorithm

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......
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