MLA-C01 Exam Dumps - AWS Certified Machine Learning Engineer - Associate

Amazon Macie is a fully managed data security and privacy service that uses machine learning to discover and classify sensitive data in Amazon S3. It is purpose-built to identify sensitive data with minimal operational overhead. After identifying the sensitive data, you can use AWS Lambda functions to automate the process of removing or redacting the sensitive data, ensuring efficiency and integration with the hybrid cloud environment. This solution requires the least development effort and aligns with the requirement to handle sensitive data effectively.

The best answers are Parquet, JSON, CSV, and ORC in that order.

Parquet is the strongest choice for complex analytical queries over large structured datasets because it is a columnar format. Columnar storage allows query engines such as Amazon Athena, AWS Glue, and Spark to read only the columns required by the query instead of scanning full rows. AWS documentation states that Apache Parquet and ORC are columnar storage formats optimized for fast retrieval in analytical applications, and that column-level compression can reduce storage space and I/O during query processing. This directly matches the need to filter, aggregate, reduce query response time, and lower storage/query cost.

JSON is correct for semi-structured real-time logs because JSON supports flexible and nested data structures. AWS Glue documentation describes JSON as a format for data structures with consistent shape but flexible contents and notes that it is not row-based or column-based. That makes it appropriate for application logs, event records, and evolving schemas used later for analytics or ML ingestion.

CSV is correct for small spreadsheet exports and occasional human-readable analysis. AWS Glue describes CSV as a minimal, row-based data format. CSV is widely supported by spreadsheet tools and is easy for humans to inspect, but it is not ideal for large-scale analytical performance because it lacks efficient column pruning and rich schema support.

ORC is correct for the Apache Hive read-heavy big data pipeline. ORC is a performance-oriented, column-based format, and it is strongly associated with Hive-based analytics workloads. It provides high compression and efficient reads, making it well suited for structured data in read-heavy big data pipelines.

Predicting apartment prices is a regression problem, where the target variable is continuous. AWS documentation states that classification metrics such as accuracy, AUC, and F1 score are not appropriate for regression tasks.

Mean Absolute Error (MAE) measures the average absolute difference between predicted values and actual values. MAE is easy to interpret because it is expressed in the same units as the target variable (for example, dollars), making it especially useful for business-facing problems like price prediction.

AWS best practices recommend MAE for evaluating regression models when understanding average prediction error magnitude is important and when robustness to outliers is desired.

Therefore, Option D is the correct and AWS-aligned answer.

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.

Option B is correct because Amazon EFS is a managed NFS-based shared file system that supports Linux clients, hybrid access, and file-system semantics such as file locking. AWS documentation states that Amazon EFS provides strong data consistency and file locking, which is an explicit requirement in the question. AWS also states that you can mount EFS file systems on on-premises data center servers when connected to your Amazon VPC through AWS Direct Connect or Site-to-Site VPN, which directly satisfies the hybrid access requirement.

For the SageMaker side, AWS documentation states that SageMaker AI creates a default Amazon EFS volume for Studio domains, and you can also add a custom Amazon EFS file system so domain users can access it in SageMaker Studio. AWS also notes that SageMaker AI Studio notebooks use Amazon EFS as a persistent storage layer. That makes EFS the most natural AWS storage service for a shared file repository that must be accessible from SageMaker notebooks.

The other choices are weaker. Amazon S3 with Mountpoint is object storage access and is not the best answer for a shared NFS-style data store with file locking requirements. FSx for Lustre is high-performance parallel file storage and can support on-premises bursting, but the question explicitly starts from an NFS-based shared data store, and EFS is AWS’s native NFS file service. Amazon EBS cannot be mounted simultaneously across on-premises servers and SageMaker notebooks as a shared, managed hybrid file system. Therefore, the best verified AWS-docs answer is B.

Amazon SageMaker multi-model endpoints (MME) are designed to host multiple models behind a single endpoint, dynamically loading models into memory on demand. AWS documentation explicitly recommends MME as the most cost-effective solution when multiple models share the same ML framework and inference container.

With MME, SageMaker loads models from Amazon S3 only when they are invoked and unloads idle models automatically. This dramatically reduces the number of instances required and avoids paying for always-on resources for infrequently used models.

Multi-container endpoints are intended for inference pipelines or ensembles and require all containers to be loaded at startup, which increases cost. Deploying separate endpoints for each model results in the highest cost due to duplicated infrastructure.

AWS best practices clearly position multi-model endpoints as the optimal choice for reducing inference costs when hosting many models with similar runtime requirements.

Therefore, Option B is the correct and AWS-verified solution.

Shadow testing allows you to send a copy of live production traffic to a shadow variant of the new model while keeping the existing production model unaffected. This enables you to evaluate the performance of the new model in real-time with live data without impacting end users. SageMaker endpoints support this setup by allowing traffic mirroring to the shadow variant, making it an ideal solution for assessing the new model ' s performance.

The ETL process has two critical characteristics: it is long-running (6 hours) and written in R. AWS Lambda is unsuitable because it has a maximum execution time of 15 minutes. AWS Glue primarily supports Spark-based ETL and does not natively support custom R-based workloads.

AWS documentation recommends using Amazon SageMaker Processing Jobs for long-running, custom data processing workloads. Processing jobs allow users to run arbitrary code in custom Docker containers, making them ideal for migrating on-premises ETL jobs written in R.

By building a custom Docker image that includes the R runtime and required libraries and storing it in Amazon ECR, the company can run the ETL job at scale on managed infrastructure without rewriting the code.

SageMaker script mode is intended for training, not ETL. Therefore, SageMaker processing jobs with a custom container are the correct solution.