Amazon MLA-C01 Exam Preparation Guide and PDF Download [Q30-Q47]

Amazon MLA-C01 Exam Preparation Guide and PDF Download

Verified & Correct MLA-C01 Practice Test Reliable Source Apr 12, 2025 Updated

NEW QUESTION # 30
A company has an ML model that needs to run one time each night to predict stock values. The model input is
3 MB of data that is collected during the current day. The model produces the predictions for the next day.
The prediction process takes less than 1 minute to finish running.
How should the company deploy the model on Amazon SageMaker to meet these requirements?

• A. Use a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.
• B. Use an asynchronous inference endpoint. Set the InitialInstanceCount parameter to 0.
• C. Use a multi-model serverless endpoint. Enable caching.
• D. Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.

Answer: D

Explanation:
A serverless inference endpoint in Amazon SageMaker is ideal for use cases where the model is invoked infrequently, such as running one time each night. It eliminates the cost of idle resources when the model is not in use. Setting the MaxConcurrency parameter to 1 ensures cost-efficiency while supporting the required single nightly invocation. This solution minimizes costs and matches the requirement to process a small amount of data quickly.

NEW QUESTION # 31
A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.
The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.
How should the company deploy the model into production to meet these requirements?

• A. Install SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Deploy the model in Amazon EKS. Set horizontal pod auto scaling to scale replicas based on the memory metric.
• B. Create a SageMaker real-time inference endpoint. Configure auto scaling. Configure the endpoint to present the existing model.
• C. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster. Use ECS scheduled scaling that is based on the CPU of the ECS cluster.
• D. Use Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences. Use the ALBRequestCountPerTarget metric as the metric for auto scaling.

Answer: B

Explanation:
Amazon SageMaker real-time inference endpoints are designed to provide low-latency predictions in production environments. They offer built-in auto scaling to handle unpredictable bursts of requests, ensuring high availability and responsiveness. This approach is fully managed, reduces operational complexity, and is optimized for the range of request sizes (1 KB to 3 MB) specified in the requirements.

NEW QUESTION # 32
An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to train a model.
Select and order the steps from the following list to create and use the features in Feature Store. Each step should be selected one time. (Select and order three.)
* Access the store to build datasets for training.
* Create a feature group.
* Ingest the records.

Answer:

Explanation:

Explanation:

Step 1: Create a feature group.Step 2: Ingest the records.Step 3: Access the store to build datasets for training.
* Step 1: Create a Feature Group
* Why?A feature group is the foundational unit in SageMaker Feature Store, where features are defined, stored, and organized. Creating a feature group specifies the schema (name, data type) for the features and the primary keys for data identification.
* How?Use the SageMaker Python SDK or AWS CLI to define the feature group by specifying its name, schema, and S3 storage location for offline access.
* Step 2: Ingest the Records
* Why?After creating the feature group, the raw data must be ingested into the Feature Store. This step populates the feature group with data, making it available for both real-time and offline use.
* How?Use the SageMaker SDK or AWS CLI to batch-ingest historical data or stream new records into the feature group. Ensure the records conform to the feature group schema.
* Step 3: Access the Store to Build Datasets for Training
* Why?Once the features are stored, they can be accessed to create training datasets. These datasets combine relevant features into a single format for machine learning model training.
* How?Use the SageMaker Python SDK to query the offline store or retrieve real-time features using the online store API. The offline store is typically used for batch training, while the online store is used for inference.
Order Summary:
* Create a feature group.
* Ingest the records.
* Access the store to build datasets for training.
This process ensures the features are properly managed, ingested, and accessible for model training using Amazon SageMaker Feature Store.

NEW QUESTION # 33
A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts.
An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources.
Which solution will meet these requirements with the LEAST development effort?

• A. Add cost allocation tags to the resources. Activate the tags in AWS Billing and Cost Management.
• B. Run AWS Compute Optimizer.
• C. Check AWS CloudTrail event history for the creation of the resources.
• D. Create code to evaluate each instance's memory and compute usage.

Answer: B

Explanation:
AWS Compute Optimizer analyzes the resource usage of Amazon EC2 instances, ECS services, Lambda functions, and Amazon EBS volumes. It provides actionable recommendations to optimize resource utilization and reduce costs, such as resizing instances, moving workloads to Spot Instances, or changing volume types. This solution requires the least development effort because Compute Optimizer is a managed service that automatically generates insights and recommendations based on historical usage data.

NEW QUESTION # 34
An ML engineer needs to use Amazon SageMaker to fine-tune a large language model (LLM) for text summarization. The ML engineer must follow a low-code no-code (LCNC) approach.
Which solution will meet these requirements?

• A. Use SageMaker Autopilot to fine-tune an LLM that is deployed by a custom API endpoint.
• B. Use SageMaker Autopilot to fine-tune an LLM that is deployed on Amazon EC2 instances.
• C. Use SageMaker Autopilot to fine-tune an LLM that is deployed by SageMaker JumpStart.
• D. Use SageMaker Studio to fine-tune an LLM that is deployed on Amazon EC2 instances.

Answer: C

Explanation:
SageMaker JumpStart provides access to pre-trained models, including large language models (LLMs), which can be easily deployed and fine-tuned with a low-code/no-code (LCNC) approach. Using SageMaker Autopilot with JumpStart simplifies the fine-tuning process by automating model optimization and reducing the need for extensive coding, making it the ideal solution for this requirement.

NEW QUESTION # 35
A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models.
When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance.
The company needs to centralize management of the team's permissions.
Which solution will meet this requirement?

• A. Create a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the AdministratorAccess AWS managed IAM policy to the role. Associate the role with the group.Associate the group with each notebook instance that the team uses.
• B. Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.
• C. Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.
• D. Create a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user.
  Configure each notebook instance to use the IAM user.

Answer: C

Explanation:
Managing permissions for multiple Amazon SageMaker notebook instances can become complex when handled individually. To centralize and streamline permission management, AWS recommends creating a single IAM role with the necessary permissions and attaching this role to each notebook instance used by the data science team.
Steps to Implement the Solution:
* Create a Single IAM Role with Necessary Permissions:
* Define an IAM role that encompasses all permissions required by the data scientists for their tasks. This includes permissions for SageMaker operations and any other AWS services they interact with.
* AWS provides managed policies like AmazonSageMakerFullAccess that can be attached to the role to grant comprehensive SageMaker permissions.(IAM Policies for SageMaker)
* Attach the IAM Role to Each Notebook Instance:
* When creating or updating a SageMaker notebook instance, specify the IAM role created in the previous step. This ensures that all notebook instances operate under a consistent set of permissions.
* In the SageMaker console, during the notebook instance setup, you can choose an existing IAM role to associate with the instance.(Creating SageMaker Workspaces) Benefits of This Approach:
* Centralized Permission Management:By using a single IAM role, you simplify the process of updating permissions. Changes to the role's policies automatically propagate to all associated notebook instances, ensuring consistent access control.
* Adherence to Best Practices:AWS recommends using IAM roles to manage permissions for applications running on services like SageMaker. This approach avoids the need to manage individual user permissions separately.(IAM Best Practices for SageMaker) Alternative Options and Their Drawbacks:
* Option B:Creating a single IAM group and adding data scientists to it does not directly associate the group with notebook instances. IAM groups are used to manage user permissions, not to assign roles to AWS resources like notebook instances.
* Option C:Using a single IAM user with the AdministratorAccess policy is not recommended due to security risks associated with granting broad permissions and the challenges in managing shared user credentials.
* Option D:Associating an IAM group with a role and then with notebook instances is not a valid approach, as IAM groups cannot be directly associated with AWS resources.
Conclusion:Option A is the most effective solution to centralize and manage permissions for SageMaker notebook instances, aligning with AWS best practices for IAM role management.
References:
* AWS Documentation: IAM Policies for SageMaker
* AWS Documentation: Creating SageMaker Workspaces
* AWS Documentation: IAM Best Practices for SageMaker

NEW QUESTION # 36
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.
Which metric finding should the ML engineer prioritize the MOST when choosing the model?

• A. Low precision
• B. Low recall
• C. High precision
• D. High recall

Answer: D

Explanation:
Recall measures the ability of a model to correctly identify all positive cases (true positives) out of all actual positives, minimizing false negatives. Since the cost of false negatives is much higher than falsepositives in this scenario, the ML engineer should prioritize models with high recall to reduce the likelihood of missing positive cases.

NEW QUESTION # 37
A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset.
Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?

• A. Grid search
• B. Random search
• C. Hyperbaric!
• D. Bayesian optimization

Answer: C

Explanation:
Hyperband is a hyperparameter tuning strategy designed to minimize computation time by adaptively allocating resources to promising configurations and terminating underperforming ones early. It efficiently balances exploration and exploitation, making it ideal for large datasets and deep learning models where training can be computationally expensive.

NEW QUESTION # 38
A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.
During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases significantly.
What could be the reason for the reduced F1 score?

• A. Concept drift occurred in the underlying customer data that was used for predictions.
• B. Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.
• C. The model was not sufficiently complex to capture all the patterns in the original baseline data.
• D. The original baseline data had a data quality issue of missing values.

Answer: A

Explanation:
* Problem Description:
* The F1 score, which is a balance of precision and recall, has decreased significantly. This indicates the model's predictions are no longer aligned with the real-world data distribution.
* Why Concept Drift?
* Concept driftoccurs when the statistical properties of the target variable or features change over time. For example, customer behaviors or subscription cancellation patterns may have shifted, leading to reduced model accuracy.
* Signs of Concept Drift:
* Deviation in performance metrics (e.g., F1 score) over time.
* Declining prediction accuracy for certain groups or scenarios.
* Solution:
* Monitor for drift using tools like SageMaker Model Monitor.
* Regularly retrain the model with updated data to account for the drift.
* Why Not Other Options?:
* B: Model complexity is unrelated if the model initially performed well.
* C: Data quality issues would have been detected during baseline analysis.
* D: Incorrect ground truth labels would have resulted in a consistently poor baseline.
Conclusion: The decrease in F1 score is most likely due toconcept driftin the customer data, requiring retraining of the model with new data.

NEW QUESTION # 39
A company wants to improve the sustainability of its ML operations.
Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

• A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
• B. Use AWS Trainium instances for training.
• C. Deploy models by using AWS Lambda functions.
• D. Use Amazon SageMaker Ground Truth for data labeling.
• E. Use PyTorch or TensorFlow with the distributed training option.

Answer: A,B

Explanation:
SageMaker Debuggercan identify when a training job is not converging or is stuck in a non-productive state.
By stopping these jobs early, unnecessary energy and computational resources are conserved, improving sustainability.
AWS Trainiuminstances are purpose-built for ML training and are optimized for energy efficiency and cost- effectiveness. They use less energy per training task compared to general-purpose instances, making them a sustainable choice.

NEW QUESTION # 40
A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker domain.
Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address.
Which update to the network configuration will meet this requirement?

• A. Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located.
• B. Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
• C. Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.
• D. Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.

Answer: A

Explanation:
Network ACLs (Access Control Lists) operate at the subnet level and allow for rules to explicitly deny traffic from specific IP addresses. By creating an inbound rule in the network ACL to deny traffic from the suspicious IP address, the company can block traffic to the Amazon SageMaker domain from that IP. This approach works because network ACLs are evaluated before traffic reaches the security groups, making them effective for blocking traffic at the subnet level.

NEW QUESTION # 41
An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers. The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML.
Which solution will meet these requirements?

• A. Use Amazon SageMaker Ground Truth to import the datasets and to consolidate them into a single data frame. Use the human-in-the-loop capability to prepare the data.
• B. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon SageMaker data labeling to prepare the data.
• C. Use Amazon SageMaker Data Wrangler to import the datasets and to consolidate them into a single data frame. Use the cleansing and enrichment functionalities to prepare the data.
• D. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon Q Developer to generate code snippets that will prepare the data.

Answer: C

Explanation:
Amazon SageMakerData Wranglerprovides a comprehensive solution for importing, consolidating, and preparing datasets for ML. It offers tools to handle missing values, duplicates, and outliers through its built- incleansingandenrichmentfunctionalities, allowing the ML engineer to efficiently prepare the data in a single environment with minimal manual effort.

NEW QUESTION # 42
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine- grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?

• A. Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
• B. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
• C. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
• D. Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.

Answer: C

Explanation:
SageMaker Pipelines provides a directed acyclic graph (DAG) view for managing and visualizing ML workflows with fine-grained control. It integrates seamlessly with SageMaker Studio, offering an intuitive interface for workflow orchestration.
SageMaker ML Lineage Tracking keeps a running history of experiments and tracks the lineage of datasets, models, and training jobs. This feature supports model governance, auditing, and compliance verification requirements.

NEW QUESTION # 43
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.
Which solution will meet these requirements?

• A. Use an Amazon Textract asynchronous job to ingest the documents from the S3 bucket. Query Amazon Textract to perform the semantic searches.
• B. Use the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra. Query Amazon Kendra to perform the semantic searches.
• C. Use a custom Amazon SageMaker notebook to run a custom script to generate embeddings. Use SageMaker Feature Store to store the embeddings. Use SQL queries to perform the semantic searches.
• D. Use an AWS Batch job to process the files and generate embeddings. Use AWS Glue to store the embeddings. Use SQL queries to perform the semantic searches.

Answer: B

Explanation:
Amazon Kendrais an AI-powered search service designed for semantic search use cases. It allows ingestion of documents from an Amazon S3 bucket using theAmazon Kendra S3 connector. Once the documents are ingested, Kendra enables semantic searches with its built-in capabilities, removing the need to manually generate embeddings or manage a vector database. This approach is efficient, requires minimal operational effort, and meets the requirements for a Retrieval Augmented Generation (RAG) application.

NEW QUESTION # 44
An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.
Which solution will meet these requirements?

• A. Use Amazon Redshift ML to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
• B. Use Amazon Data Firehose to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
• C. Use AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
• D. Use Amazon Athena to create the data ingestion pipelines. Use an Amazon SageMaker notebook to create the model deployment pipelines.

Answer: C

Explanation:
AWS Glue is a serverless data integration service that is well-suited for creating data ingestion pipelines, especially when raw data is stored in Amazon S3. It can clean, transform, and catalog data, making it accessible for downstream ML tasks.
Amazon SageMaker Studio Classic provides a comprehensive environment for building, training, and deploying ML models. It includes built-in tools and capabilities to create efficient model deployment pipelines with minimal setup.
This combination ensures seamless integration of data ingestion and ML model deployment with minimal operational overhead.

NEW QUESTION # 45
A company needs to host a custom ML model to perform forecast analysis. The forecast analysis will occur with predictable and sustained load during the same 2-hour period every day.
Multiple invocations during the analysis period will require quick responses. The company needs AWS to manage the underlying infrastructure and any auto scaling activities.
Which solution will meet these requirements?

• A. Schedule an Amazon SageMaker batch transform job by using AWS Lambda.
• B. Use Amazon SageMaker Serverless Inference with provisioned concurrency.
• C. Configure an Auto Scaling group of Amazon EC2 instances to use scheduled scaling.
• D. Run the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster on Amazon EC2 with pod auto scaling.

Answer: B

Explanation:
SageMaker Serverless Inference is ideal for workloads with predictable, intermittent demand. By enabling provisioned concurrency, the model can handle multiple invocations quickly during the high-demand 2-hour period. AWS manages the underlying infrastructure and scaling, ensuring the solution meets performance requirements with minimal operational overhead. This approach is cost-effective since it scales down when not in use.

NEW QUESTION # 46
A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.
A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.
Which solution will meet these requirements with the LEAST implementation effort?

• A. Configure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.
• B. Unload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to anonymize the data.Share the dataset with the data scientist.
• C. Create a materialized view with masking logic on top of the database. Grant the necessary read permissions to the data scientist.
• D. Unload the Amazon Redshift data to Amazon S3. Use Amazon Athena to create schema-on-read with masking logic. Share the view with the data scientist.

Answer: A

Explanation:
Dynamic data maskingallows you to control how sensitive data is presented to users at query time, without modifying or storing transformed versions of the source data. Amazon Redshift supports dynamic data masking, which can be implemented with minimal effort. This solution ensures that the data scientistcan access the required information while sensitive data remains protected, meeting the requirements efficiently and with the least implementation effort.

NEW QUESTION # 47
......

Pass Amazon MLA-C01 exam Dumps 100 Pass Guarantee With Latest Demo: https://examsites.premiumvcedump.com/Amazon/valid-MLA-C01-premium-vce-exam-dumps.html