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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q122-Q127):

NEW QUESTION # 122
A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.
The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.
Which change to the architecture will meet these requirements?

A. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds.
B. Replace the Firehose stream with an AWS DataSync task. Configure the task with enhanced fan-out consumers.
C. Use zero buffering in the Firehose stream. Tune the batch size that is used in the PutRecordBatch operation.
D. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue.

Answer: C

Explanation:
The primary requirement in this scenario is achieving sub-second latency for a real-time analytics dashboard powered by Amazon OpenSearch Service. The current architecture uses Amazon Data Firehose, which buffers incoming records based on time or size before delivering them to the destination. A buffer interval of
60 seconds introduces unavoidable latency, making it unsuitable for near-real-time or sub-second use cases.
According to AWS documentation, reducing or eliminating buffering in Firehose is the correct approach when low-latency ingestion is required. Setting the Firehose buffer interval to zero seconds forces Firehose to deliver records as soon as they are received. Additionally, tuning the PutRecordBatch batch size allows efficient ingestion while minimizing delivery delay. This configuration is explicitly recommended for latency- sensitive analytics pipelines.
Option B is incorrect because AWS DataSync is designed for batch-oriented data transfers between storage systems, not real-time streaming. Enhanced fan-out consumers are a feature of Amazon Kinesis Data Streams, not DataSync, making this option invalid.
Option C directly contradicts the requirement. Increasing the buffer interval from 60 seconds to 120 seconds would further increase latency and degrade real-time performance.
Option D is also incorrect because Amazon SQS is a message queueing service, not a streaming ingestion service optimized for indexing data into OpenSearch with minimal latency. Using SQS would add additional processing layers and would not inherently provide sub-second ingestion into OpenSearch.
Therefore, using zero buffering in the Firehose stream and tuning the PutRecordBatch batch size is the only change that aligns with AWS best practices for achieving sub-second latency in real-time analytics pipelines.

NEW QUESTION # 123
A medical company needs to store clinical data. The data includes personally identifiable information (PII) and protected health information (PHI).
An ML engineer needs to implement a solution to ensure that the PII and PHI are not used to train ML models.
Which solution will meet these requirements?

A. Store the clinical data in Amazon S3 buckets. Use AWS Glue DataBrew to mask the PII and PHI before the data is used for model training.
B. Create an AWS Lambda function to encrypt the PII and PHI. Program the Lambda function to save the encrypted data to an Amazon S3 bucket for model training.
C. Upload the clinical data to an Amazon Redshift database. Use built-in SQL stored procedures to automatically classify and mask the PII and PHI before the data is used for model training.
D. Use Amazon Comprehend to detect and mask the PII before the data is used for model training.
Use Amazon Comprehend Medical to detect and mask the PHI before the data is used for model training.

Answer: D

NEW QUESTION # 124
A company has an ML model that is deployed to an Amazon SageMaker endpoint for real-time inference. The company needs to deploy a new model. The company must compare the new model's performance to the currently deployed model's performance before shifting all traffic to the new model. Which solution will meet these requirements with the LEAST operational effort?

A. Deploy the new model to a separate endpoint. Manually split traffic between the two endpoints.
B. Deploy the new model as a shadow variant on the same endpoint as the current model. Route a portion of live traffic to the shadow model for evaluation.
C. Deploy the new model to a separate endpoint. Use Amazon CloudFront to distribute traffic between the two endpoints.
D. Use AWS Lambda functions with custom logic to route traffic between the current model and the new model.

Answer: B

Explanation:
SageMaker supports shadow variant deployments, which allow a new model to run alongside the current one on the same endpoint. A portion of live traffic is mirrored to the shadow model for evaluation, while only the current model's output is returned to users. This provides the required comparison with minimal operational effort, avoiding the need for custom traffic-splitting solutions.

NEW QUESTION # 125
A manufacturing company uses an ML model to determine whether products meet a standard for quality. The model produces an output of "Passed" or "Failed." Robots separate the products into the two categories by using the model to analyze photos on the assembly line.
Which metrics should the company use to evaluate the model's performance? (Choose two.)

A. Root mean square error (RMSE) and mean absolute percentage error (MAPE)
B. Perplexity
C. Accuracy and F1 score
D. Precision and recall
E. Bilingual Evaluation Understudy (BLEU) score

Answer: C,D

NEW QUESTION # 126
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 AI compute costs reach a specific threshold.
Which solution will meet these requirements?

A. Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.
B. Add resource tagging by editing each user's IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.
C. Add resource tagging by editing each user's IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.
D. Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain. Configure AWS Budgets to send an alert when the threshold is reached.

Answer: D

Explanation:
AWS best practices for cost governance recommend using resource tagging combined with AWS Budgets to track and alert on service-level spending. By adding tags at the SageMaker Studio user profile level, all compute resources launched by users inherit those tags automatically.
AWS Budgets supports threshold-based alerts, unlike AWS Cost Explorer, which is primarily used for historical analysis and visualization. Budgets can trigger notifications via email or Amazon SNS when spending exceeds defined limits.
IAM profiles are unrelated to cost tracking, making options C and D invalid. Therefore, tagging SageMaker user profiles and using AWS Budgets is the correct solution.

NEW QUESTION # 127
......

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