[2025] New MLA-C01 exam dumps Use Updated Amazon Exam
Verified MLA-C01 Dumps Q&As - MLA-C01 Test Engine with Correct Answers
NEW QUESTION # 39
An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm.
The model classifies transactions as either fraudulent or legitimate.
During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions.
What should the ML engineer do to improve the fraud detection for new transactions?
- A. Increase the value of the max_depth hyperparameter.
- B. Decrease the value of the max_depth hyperparameter.
- C. Increase the learning rate.
- D. Remove some irrelevant features from the training dataset.
Answer: B
Explanation:
A high max_depth value in XGBoost can lead to overfitting, where the model learns the training dataset too well but fails to generalize to new and unseen data. By decreasing the max_depth, the model becomes less complex, reducing overfitting and improving its ability to detect fraud in new transactions. This adjustment helps the model focus on general patterns rather than memorizing specific details in the training data.
NEW QUESTION # 40
A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.
Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?
- A. On-Demand Instances
- B. Reserved Instances
- C. Dedicated Instances
- D. Spot Instances
Answer: D
Explanation:
Scenario:The company needs to run a batch job for 90 minutes every weekend over the next 6 months. The processing can handle interruptions, and cost-effectiveness is a priority.
Why Spot Instances?
* Cost-Effective:Spot Instances provide up to 90% savings compared to On-Demand Instances, making them the most cost-effective option for batch processing.
* Interruption Tolerance:Since the processing can tolerate interruptions, Spot Instances are suitable for this workload.
* Batch-Friendly:Spot Instances can be requested for specific durations or automatically re-requested in case of interruptions.
Steps to Implement:
* Create a Spot Instance Request:
* Use the EC2 console or CLI to request Spot Instances with desired instance type and duration.
* Use Auto Scaling:Configure Spot Instances with an Auto Scaling group to handle instance interruptions and ensure job completion.
* Run the Batch Job:Use tools like AWS Batch or custom scripts to manage the processing.
Comparison with Other Options:
* Reserved Instances:Suitable for predictable, continuous workloads, but less cost-effective for a job that runs only once a week.
* On-Demand Instances:More expensive and unnecessary given the tolerance for interruptions.
* Dedicated Instances:Best for isolation and compliance but significantly more costly.
References:
* Amazon EC2 Spot Instances
* Best Practices for Using Spot Instances
* AWS Batch for Spot Instances
NEW QUESTION # 41
A company is planning to use Amazon SageMaker to make classification ratings that are based on images.
The company has 6 ## of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker.
An ML engineer must make the training data accessible for ML models that are in the SageMaker environment.
Which solution will meet these requirements?
- A. Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
- B. Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system.
- C. Mount the FSx for ONTAP file system as a volume to the SageMaker Instance.
- D. Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
Answer: C
Explanation:
Amazon FSx for NetApp ONTAP allows mounting the file system as a network-attached storage (NAS) volume. Since the FSx for ONTAP file system and SageMaker instance are in the same VPC, you can directly mount the file system to the SageMaker instance. This approach ensures efficient access to the 6 TB of training data without the need to duplicate or transfer the data, meeting the requirements with minimal complexity and operational overhead.
NEW QUESTION # 42
A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ## in size and consists of CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?
- A. Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.
- B. Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.
- C. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.
- D. Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.
Answer: C
Explanation:
Amazon SageMaker Pipelines is designed for creating, automating, and managing end-to-end ML workflows, including complex data preprocessing tasks. It supports handling large datasets and can integrate with custom steps, such as NLP transformations. By combining SageMaker Pipelines with Amazon EventBridge, the entire workflow can be triggered and automated efficiently, meeting the requirements for scalability, automation, and processing complexity.
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 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.
- C. 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.
- D. 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.
Answer: C
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 is using Amazon SageMaker to train a deep learning model that requires distributed training.
After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?
- A. Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.
- B. Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.
- C. Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.
- D. Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.
Answer: D
Explanation:
To minimize communication overhead during distributed training:
1. Same VPC Subnet: Ensures low-latency communication between training instances by keeping the network traffic within a single subnet.
2. Same AWS Region and Availability Zone: Reduces network latency further because cross-AZ communication incurs additional latency and costs.
3. Data in the Same Region and AZ: Ensures that the training data is accessed with minimal latency, improving performance during training.
This configuration optimizes communication efficiency and minimizes overhead.
NEW QUESTION # 45
Case study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.
Which solution will meet these requirements?
- A. Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
- B. Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.
- C. Use AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
- D. Use Amazon Athena to automatically detect the anomalies and to visualize the result.
Answer: B
Explanation:
Amazon SageMaker Data Wrangler is a comprehensive tool that streamlines the process of data preparation and offers built-in capabilities for anomaly detection and visualization.
Key Features of SageMaker Data Wrangler:
* Data Importation: Connects seamlessly to various data sources, including Amazon S3 and on- premises databases, facilitating the aggregation of transaction logs, customer profiles, and MySQL tables.
* Anomaly Detection: Provides built-in analyses to detect anomalies in time series data, enabling the identification of outliers that may indicate fraudulent activities.
* Visualization: Offers a suite of visualization tools, such as histograms and scatter plots, to help understand data distributions and relationships, which are crucial for feature engineering and model development.
Implementation Steps:
* Data Aggregation:
* Import data from Amazon S3 and on-premises MySQL databases into SageMaker Data Wrangler.
* Utilize Data Wrangler's data flow interface to combine and preprocess datasets, ensuring a unified dataset for analysis.
* Anomaly Detection:
* Apply the anomaly detection analysis feature to identify outliers in the dataset.
* Configure parameters such as the anomaly threshold to fine-tune the detection sensitivity.
* Visualization:
* Use built-in visualization tools to create charts and graphs that depict data distributions and highlight anomalies.
* Interpret these visualizations to gain insights into potential fraud patterns and feature interdependencies.
Advantages of Using SageMaker Data Wrangler:
* Integrated Workflow: Combines data preparation, anomaly detection, and visualization within a single interface, streamlining the ML development process.
* Operational Efficiency: Reduces the need for multiple tools and complex integrations, thereby minimizing operational overhead.
* Scalability: Handles large datasets efficiently, making it suitable for extensive transaction logs and customer profiles.
By leveraging SageMaker Data Wrangler, the ML engineer can effectively detect anomalies and visualize results, facilitating the development of a robust fraud detection model.
References:
* Analyze and Visualize - Amazon SageMaker
* Transform Data - Amazon SageMaker
NEW QUESTION # 46
An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning.
The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain.
Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)
- A. The ML engineer and the Canvas user must be in separate SageMaker domains.
- B. The ML engineer must host the model on AWS Marketplace.
- C. The model must be registered in the SageMaker Model Registry.
- D. The ML engineer must deploy the model to a SageMaker endpoint.
- E. The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored.
Answer: C,E
Explanation:
The SageMaker Canvas user needs permissions to access the Amazon S3 bucket where the model artifacts are stored to retrieve the model for use in Canvas.
Registering the model in the SageMaker Model Registry allows the model to be tracked and managed within the SageMaker ecosystem. This makes it accessible for tuning and deployment through SageMaker Canvas.
This combination ensures proper access control and integration within SageMaker, enabling the Canvas user to work with the model.
NEW QUESTION # 47
A company's ML engineer has deployed an ML model for sentiment analysis to an Amazon SageMaker endpoint. The ML engineer needs to explain to company stakeholders how the model makes predictions.
Which solution will provide an explanation for the model's predictions?
- A. Show the distribution of inferences from A/# testing in Amazon CloudWatch.
- B. Add a shadow endpoint. Analyze prediction differences on samples.
- C. Use SageMaker Model Monitor on the deployed model.
- D. Use SageMaker Clarify on the deployed model.
Answer: D
Explanation:
SageMaker Clarify is designed to provide explainability for ML models. It can analyze feature importance and explain how input features influence the model's predictions. By using Clarify with the deployed SageMaker model, the ML engineer can generate insights and present them to stakeholders to explain the sentiment analysis predictions effectively.
NEW QUESTION # 48
A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs reach a specific threshold.
Which solution will meet these requirements?
- A. Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Budgets to send an alert when the threshold is reached.
- B. Add resource tagging by editing each user's IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.
- C. Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.
- D. Add resource tagging by editing each user's IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.
Answer: A
Explanation:
Adding resource tagging to the SageMaker user profile enables tracking and monitoring of costs associated with specific SageMaker resources.
AWS Budgets allows setting thresholds and automated alerts for costs and usage, making it the ideal service to notify the ML engineer when compute costs reach a specified limit.
This solution is efficient and integrates seamlessly with SageMaker and AWS cost management tools.
NEW QUESTION # 49
A company has developed a new ML model. The company requires online model validation on 10% of the traffic before the company fully releases the model in production. The company uses an Amazon SageMaker endpoint behind an Application Load Balancer (ALB) to serve the model.
Which solution will set up the required online validation with the LEAST operational overhead?
- A. Create a new SageMaker endpoint. Use production variants to add the new model to the new endpoint.
Monitor the number of invocations by using Amazon CloudWatch. - B. Use production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 0.1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.
- C. Configure the ALB to route 10% of the traffic to the new model at the existing SageMaker endpoint.Monitor the number of invocations by using AWS CloudTrail.
- D. Use production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.
Answer: B
Explanation:
Scenario:The company wants to perform online validation of a new ML model on 10% of the traffic before fully deploying the model in production. The setup must have minimal operational overhead.
Why Use SageMaker Production Variants?
* Built-In Traffic Splitting:Amazon SageMaker endpoints support production variants, allowing multiple models to run on a single endpoint. You can direct a percentage of incoming traffic to each variant by adjusting the variant weights.
* Ease of Management:Using production variants eliminates the need for additional infrastructure like separate endpoints or custom ALB configurations.
* Monitoring with CloudWatch:SageMaker automatically integrates with CloudWatch, enabling real- time monitoring of model performance and invocation metrics.
Steps to Implement:
* Deploy the New Model as a Production Variant:
* Update the existing SageMaker endpoint to include the new model as a production variant. This can be done via the SageMaker console, CLI, or SDK.
Example SDK Code:
import boto3
sm_client = boto3.client('sagemaker')
response = sm_client.update_endpoint_weights_and_capacities(
EndpointName='existing-endpoint-name',
DesiredWeightsAndCapacities=[
{'VariantName': 'current-model', 'DesiredWeight': 0.9},
{'VariantName': 'new-model', 'DesiredWeight': 0.1}
]
)
* Set the Variant Weight:
* Assign a weight of 0.1 to the new model and 0.9 to the existing model. This ensures 10% of traffic goes to the new model while the remaining 90% continues to use the current model.
* Monitor the Performance:
* Use Amazon CloudWatch metrics, such as InvocationCount and ModelLatency, to monitor the traffic and performance of each variant.
* Validate the Results:
* Analyze the performance of the new model based on metrics like accuracy, latency, and failure rates.
Why Not the Other Options?
* Option B:Setting the weight to 1 directs all traffic to the new model, which does not meet the requirement of splitting traffic for validation.
* Option C:Creating a new endpoint introduces additional operational overhead for traffic routing and monitoring, which is unnecessary given SageMaker's built-in production variant capability.
* Option D:Configuring the ALB to route traffic requires manual setup and lacks SageMaker's seamless variant monitoring and traffic splitting features.
Conclusion:Using production variants with a weight of 0.1 for the new model on the existing SageMaker endpoint provides the required traffic split for online validation with minimal operational overhead.
References:
* Amazon SageMaker Endpoints
* SageMaker Production Variants
* Monitoring SageMaker Endpoints with CloudWatch
NEW QUESTION # 50
A company has trained and deployed an ML model by using Amazon SageMaker. The company needs to implement a solution to record and monitor all the API call events for the SageMaker endpoint. The solution also must provide a notification when the number of API call events breaches a threshold.
Use SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to provide a notification when the threshold is breached.
Which solution will meet these requirements?
- A. Use SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to provide a notification when the threshold is breached.
- B. Log all the endpoint invocation API events by using AWS CloudTrail. Use an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.
- C. Use SageMaker Debugger to track the inferences and to report metrics. Use the tensor_variance built-in rule to provide a notification when the threshold is breached.
- D. Add the Invocations metric to an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.
Answer: D
Explanation:
Amazon SageMaker automatically tracks theInvocationsmetric, which represents the number of API calls made to the endpoint, inAmazon CloudWatch. By adding this metric to a CloudWatch dashboard, you can monitor the endpoint's activity in real-time. Setting up aCloudWatch alarmallows the system to send notifications whenever the API call events exceed the defined threshold, meeting both the monitoring and notification requirements efficiently.
NEW QUESTION # 51
An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.
The ML engineer needs a scalable solution that minimizes costs when the model is not in use. The solution also must maintain the model's capacity to respond to requests during times of peak usage.
Which solution will meet these requirements?
- A. Deploy the model to an Amazon SageMaker endpoint. Deploy multiple copies of the model to the endpoint. Create an Application Load Balancer to route traffic between the different copies of the model at the endpoint.
- B. Deploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.
- C. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Set a static number of tasks to handle requests during times of peak usage.
- D. Create AWS Lambda functions that have fixed concurrency to host the model. Configure the Lambda functions to automatically scale based on the number of requests to the model.
Answer: B
NEW QUESTION # 52
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference data to the model for predictions.
Which solution will meet this requirement?
- A. Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.
- B. Apply statistics from a well-known dataset to normalize the production samples.
- C. Calculate a new set of min-max normalization statistics from each production sample. Use these values to normalize all the production samples.
- D. Calculate a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all the production samples.
Answer: A
Explanation:
To ensure consistency between training and inference, themin-max normalization statistics (min and max values)calculated during training must be retained and applied to normalize production inference data. Using the same statistics ensures that the model receives data in the same scale and distribution as it did during training, avoiding discrepancies that could degrade model performance. Calculating new statistics from production data would lead to inconsistent normalization and affect predictions.
NEW QUESTION # 53
A company stores time-series data about user clicks in an Amazon S3 bucket. The raw data consists of millions of rows of user activity every day. ML engineers access the data to develop their ML models.
The ML engineers need to generate daily reports and analyze click trends over the past 3 days by using Amazon Athena. The company must retain the data for 30 days before archiving the data.
Which solution will provide the HIGHEST performance for data retrieval?
- A. Put each day's time-series data into its own S3 bucket. Use S3 Lifecycle policies to archive S3 buckets that hold data that is older than 30 days to S3 Glacier Flexible Retrieval.
- B. Organize the time-series data into partitions by date prefix in the S3 bucket. Apply S3 Lifecycle policies to archive partitions that are older than 30 days to S3 Glacier Flexible Retrieval.
- C. Create AWS Lambda functions to copy the time-series data into separate S3 buckets. Apply S3 Lifecycle policies to archive data that is older than 30 days to S3 Glacier Flexible Retrieval.
- D. Keep all the time-series data without partitioning in the S3 bucket. Manually move data that is older than 30 days to separate S3 buckets.
Answer: B
Explanation:
Partitioning the time-series data by date prefix in the S3 bucket significantly improves query performance in Amazon Athena by reducing the amount of data that needs to be scanned during queries. This allows the ML engineers to efficiently analyze trends over specific time periods, such as the past 3 days. Applying S3 Lifecycle policies to archive partitions older than 30 days to S3 Glacier FlexibleRetrieval ensures cost- effective data retention and storage management while maintaining high performance for recent data retrieval.
NEW QUESTION # 54
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 # 55
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. Random search
- B. Hyperbaric!
- C. Grid search
- D. Bayesian optimization
Answer: B
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 # 56
An ML engineer is building a generative AI application on Amazon Bedrock by using large language models (LLMs).
Select the correct generative AI term from the following list for each description. Each term should be selected one time or not at all. (Select three.)
* Embedding
* Retrieval Augmented Generation (RAG)
* Temperature
* Token
Answer:
Explanation:
Explanation:
* Text representation of basic units of data processed by LLMs:Token
* High-dimensional vectors that contain the semantic meaning of text:Embedding
* Enrichment of information from additional data sources to improve a generated response:
Retrieval Augmented Generation (RAG)
Comprehensive Detailed Explanation
* Token:
* Description: A token represents the smallest unit of text (e.g., a word or part of a word) that an LLM processes. For example, "running" might be split into two tokens: "run" and "ing."
* Why?Tokens are the fundamental building blocks for LLM input and output processing, ensuring that the model can understand and generate text efficiently.
* Embedding:
* Description: High-dimensional vectors that encode the semantic meaning of text. These vectors are representations of words, sentences, or even paragraphs in a way that reflects their relationships and meaning.
* Why?Embeddings are essential for enabling similarity search, clustering, or any task requiring semantic understanding. They allow the model to "understand" text contextually.
* Retrieval Augmented Generation (RAG):
* Description: A technique where information is enriched or retrieved from external data sources (e.g., knowledge bases or document stores) to improve the accuracy and relevance of a model's generated responses.
* Why?RAG enhances the generative capabilities of LLMs by grounding their responses in factual and up-to-date information, reducing hallucinations in generated text.
By matching these terms to their respective descriptions, the ML engineer can effectively leverage these concepts to build robust and contextually aware generative AI applications on Amazon Bedrock.
NEW QUESTION # 57
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. Create a SageMaker real-time inference endpoint. Configure auto scaling. Configure the endpoint to present the existing model.
- B. 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.
- 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: A
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 # 58
A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models.
Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)
* Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.
* Store the resulting data back in Amazon S3.
* Use Amazon Athena to infer the schemas and available columns.
* Use AWS Glue crawlers to infer the schemas and available columns.
* Use AWS Glue DataBrew for data cleaning and feature engineering.
Answer:
Explanation:
Explanation:
Step 1: Use AWS Glue crawlers to infer the schemas and available columns.Step 2: Use AWS Glue DataBrew for data cleaning and feature engineering.Step 3: Store the resulting data back in Amazon S3.
* Step 1: Use AWS Glue Crawlers to Infer Schemas and Available Columns
* Why?The data is stored in .csv files with unlabeled columns, and Glue Crawlers can scan the raw data in Amazon S3 to automatically infer the schema, including available columns, data types, and any missing or incomplete entries.
* How?Configure AWS Glue Crawlers to point to the S3 bucket containing the .csv files, and run the crawler to extract metadata. The crawler creates a schema in the AWS Glue Data Catalog, which can then be used for subsequent transformations.
* Step 2: Use AWS Glue DataBrew for Data Cleaning and Feature Engineering
* Why?Glue DataBrew is a visual data preparation tool that allows for comprehensive cleaning and transformation of data. It supports imputation of missing values, renaming columns, feature engineering, and more without requiring extensive coding.
* How?Use Glue DataBrew to connect to the inferred schema from Step 1 and perform data cleaning and feature engineering tasks like filling in missing rows/columns, renaming unlabeled columns, and creating derived features.
* Step 3: Store the Resulting Data Back in Amazon S3
* Why?After cleaning and preparing the data, it needs to be saved back to Amazon S3 so that it can be used for training machine learning models.
* How?Configure Glue DataBrew to export the cleaned data to a specific S3 bucket location. This ensures the processed data is readily accessible for ML workflows.
Order Summary:
* Use AWS Glue crawlers to infer schemas and available columns.
* Use AWS Glue DataBrew for data cleaning and feature engineering.
* Store the resulting data back in Amazon S3.
This workflow ensures that the data is prepared efficiently for ML model training while leveraging AWS services for automation and scalability.
NEW QUESTION # 59
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