Question # 1
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. The model was not sufficiently complex to capture all the patterns in the original
baseline data. | | C. The original baseline data had a data quality issue of missing values. | | D. Incorrect ground truth labels were provided to Model Monitor during the calculation of
the baseline. |
A. Concept drift occurred in the underlying customer data that was used for predictions.
Explanation:
Problem Description:
Why Concept Drift?
Signs of Concept Drift:
Solution:
Why Not Other Options?:
Conclusion: The decrease in F1 score is most likely due toconcept driftin the customer
data, requiring retraining of the model with new data.
Question # 2
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. 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. | | B. 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. | | C. 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. | | D. 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. |
A. 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.
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:
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:
Monitor the Performance:
Validate the Results:
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.
Question # 3
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 Amazon SageMaker Ground Truth for data labeling. | | C. Deploy models by using AWS Lambda functions. | | D. Use AWS Trainium instances for training. | | E. Use PyTorch or TensorFlow with the distributed training option. |
A. Use Amazon SageMaker Debugger to stop training jobs when non-converging
conditions are detected.
D. Use AWS Trainium instances for training.
Explanation:
SageMaker Debuggercan identify when a training job is not converging or is stuck in a nonproductive
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.
Question # 4
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. Create a materialized view with masking logic on top of the database. Grant the
necessary read permissions to the data scientist. | | C. 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. | | D. Unload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to
anonymize the data. Share the dataset with the data scientist. |
A. Configure dynamic data masking policies to control how sensitive data is shared with the
data scientist at query time.
Explanation:
Dynamic data masking allows 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 scientist can access the required information while
sensitive data remains protected, meeting the requirements efficiently and with the least
implementation effort.
Question # 5
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. Create code to evaluate each instance's memory and compute usage. | | B. Add cost allocation tags to the resources. Activate the tags in AWS Billing and Cost
Management. | | C. Check AWS CloudTrail event history for the creation of the resources. | | D. Run AWS Compute Optimizer. |
D. Run AWS Compute Optimizer.
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.
Question # 6
An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model.
The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a
file format that minimizes processing time for the data.
Which file format will meet these requirements? | | A. CSV files compressed with Snappy | | B. JSON objects in JSONL format | | C. JSON files compressed with gzip | | D. Apache Parquet files |
D. Apache Parquet files
Explanation:
Apache Parquet is a columnar storage file format optimized for complex and large
datasets. It provides efficient reading and processing by accessing only the required
columns, which reduces I/O and speeds up data handling. This makes it ideal for use with
Amazon SageMaker Canvas, where minimizing processing time is important for training ML
models. Parquet is also compatible with S3 and widely supported in data analytics and ML
workflows.
Question # 7
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
 |
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