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>> MLA-C01 Practical Information <<
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NEW QUESTION # 144
A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups.
The company uses a single AWS account and stores all the training data in Amazon S3 buckets.
All ML model training occurs in Amazon SageMaker.
Which solution will provide the ML engineers with the appropriate access?
Answer: A
NEW QUESTION # 145
A company wants to build a real-time analytics application that uses streaming data from social media. An ML engineer must implement a solution that ingests and transforms 5 GB of data each minute. The solution also must load the data into a data store that supports fast queries for the real-time analytics. Which solution will meet these requirements?
Answer: C
Explanation:
Amazon Kinesis Data Streams is designed for high-throughput ingestion of streaming data such as social media feeds. Amazon Managed Service for Apache Flink enables real-time transformations on that data. Amazon DynamoDB provides low-latency reads and writes, making it suitable for fast queries in real-time analytics. This combination fully meets the scale and speed requirements.
NEW QUESTION # 146
A company has an ML model in Amazon SageMaker AI. An ML engineer needs to implement a monitoring solution to automatically detect changes in the input data distribution of model features.
Which solution will meet this requirement with the LEAST operational overhead?
Answer: B
Explanation:
Option A is correct because the requirement is to detect changes in the input data distribution of model features , which is a data quality / data drift monitoring problem. AWS documentation states that Amazon SageMaker Model Monitor uses rules to detect data drift and alerts you when it happens. The documented workflow is to enable data capture, create a baseline from training data, and then run monitoring jobs that compare incoming inference data against that baseline. That directly matches the need to automatically detect changes in feature distributions.
AWS also documents that Model Monitor can emit metrics to Amazon CloudWatch , and those metrics can be used with CloudWatch alarms to notify teams when data quality drifts beyond acceptable thresholds.
That makes Option A the lowest-operational-overhead solution because it uses SageMaker's built-in monitoring capability plus managed alerting, rather than requiring custom drift logic. The inclusion of emit_metrics and CloudWatch alarming is consistent with the SageMaker monitoring pattern for automated notification.
The other options are weaker. Option B is for model quality monitoring, which focuses on prediction performance against ground truth, not shifts in the input feature distribution. Option C uses SageMaker Debugger, which is aimed at training-time debugging and custom rule analysis rather than managed production data drift monitoring. Option D relies on manual log analysis and endpoint performance metrics, which does not directly solve feature-distribution drift detection and adds more operational effort. Therefore, the best AWS-documented answer is A .
NEW QUESTION # 147
A company is building an enterprise AI platform. The company must catalog models for production, manage model versions, and associate metadata such as training metrics with models. The company needs to eliminate the burden of managing different versions of models.
Which solution will meet these requirements?
Answer: C
Explanation:
AWS enterprise ML best practices recommend using Amazon SageMaker Model Registry to manage models throughout their lifecycle. The Model Registry is designed specifically to catalog models, track versions, and associate metadata such as training metrics, approval status, and deployment history.
Model Registry introduces the concept of model groups, which act as logical containers for different versions of the same model. Each model version within a group automatically inherits versioning, metadata tracking, and governance controls. This eliminates the operational burden of manually managing model versions and ensures consistent lineage and traceability across development, testing, and production environments.
Option A is less optimal because manually tagging model versions increases operational complexity and does not take full advantage of the built-in version management features provided by model groups.
Options C and D are incorrect because Amazon ECR is a container image repository, not a model governance or lifecycle management service. Using ECR to manage ML model versions would require custom tooling and manual metadata handling, significantly increasing operational overhead.
By using model groups within SageMaker Model Registry, the company gains a centralized, scalable, and AWS-native solution for enterprise AI governance. This approach directly aligns with AWS documentation for managing model catalogs, version control, and metadata association while minimizing manual intervention.
NEW QUESTION # 148
An ML engineer is tuning an image classification model that shows poor performance on one of two available classes during prediction. Analysis reveals that the images whose class the model performed poorly on represent an extremely small fraction of the whole training dataset.
The ML engineer must improve the model's performance.
Which solution will meet this requirement?
Answer: A
Explanation:
This problem describes severe class imbalance in an image classification task, where the minority class has poor predictive performance. In such cases, accuracy is a misleading metric, because a model can achieve high accuracy by predicting only the majority class. AWS ML best practices recommend using F1 score, which balances precision and recall and is more appropriate for imbalanced classification problems.
To improve performance on the minority image class, image augmentation is the preferred approach.
Augmentation techniques-such as rotation, cropping, flipping, and brightness adjustment-create realistic new training examples while preserving semantic meaning. AWS documentation recommends augmentation for computer vision workloads to improve generalization without collecting new data.
SMOTE (Options C and D) is designed for tabular data, not image data, and generating synthetic pixel-level images using SMOTE is not appropriate or supported in typical computer vision pipelines.
Option A is incorrect because optimizing for accuracy does not address minority-class performance. Option D is incorrect because SMOTE is unsuitable for images.
Therefore, optimizing for F1 score and using image augmentation on the minority class is the correct solution.
NEW QUESTION # 149
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