AWS Certified Machine Learning Engineer - Associate (MLA-C01) Exam Dumps with Solutions

A fintech startup has deployed a gradient-boosted tree fraud-detection model with 42 numeric features to an Amazon SageMaker real-time endpoint on ml.m5.large (target p95 latency ≤ 80 ms and ~200 RPS), and compliance officers require per-inference feature attributions (for example, amount=$1,275, country=DE, device_age_days=14) to understand why specific transactions are flagged without changing the model code; which solution will provide explanations for predictions from the live endpoint?

In a refinery safety system, an ML engineer must select a binary classifier that detects methane leaks from sensor streams; a missed real leak (false negative) incurs an average incident cost of $120,000 while a false alarm triggers a $300 on-site check and 5 minutes of downtime, so which metric finding should be prioritized the most when choosing the model?

A media analytics company operates over 200 ML models across 12 AWS accounts, where each model is packaged as a Docker image stored in account-local Amazon ECR repositories with semantic version tags. Compliance requires a single, cross-account central catalog in a separate governance account that supports versioned entries, approval status (Approved/Pending/Rejected), lineage metadata, and audit logs, provides read-only discovery to a shared data science account, allows producer accounts to register new versions via CI/CD, and avoids duplicating or migrating container images between accounts; which solution should the company implement?

An online travel platform with 220 data scientists uses Amazon SageMaker Model Registry to track more than 1,200 model package versions that are already organized into 95 model groups, and the scientists are aligned to three business domains—pricing optimization, search relevance, and fraud detection—so an ML platform engineer must implement a solution to organize the existing assets by these domains to improve discoverability at scale without modifying any model artifacts, model package versions, approval statuses, or current model group memberships. Which solution will meet these requirements?

A smart farming startup must build an ML model in Amazon SageMaker that, given 4K drone images (3840x2160) of orchard rows, automatically finds each ripe fruit and returns a class label plus bounding-box coordinates (x_min, y_min, x_max, y_max) for every detection with confidence >= 0.85; which SageMaker algorithm should they use?