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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q168-Q173):

NEW QUESTION # 168
A company is using Amazon SageMaker AI to develop a credit risk assessment model. During model validation, the company finds that the model achieves 82% accuracy on the validation data. However, the model achieved 99% accuracy on the training data. The company needs to address the model accuracy issue before deployment.
Which solution will meet this requirement?

A. Implement dropout layers. Use L1 or L2 regularization. Perform k-fold cross-validation.
B. Use principal component analysis (PCA) to reduce the feature dimensionality. Decrease model layers.
Implement cross-entropy loss functions.
C. Add more dense layers to increase model complexity. Implement batch normalization. Use early stopping during training.
D. Augment the training dataset. Remove duplicate records from the training dataset. Implement stratified sampling.

Answer: A

Explanation:
The large gap between training accuracy (99%) and validation accuracy (82%) is a textbook case of overfitting. The model has learned patterns that fit the training data extremely well but do not generalize to unseen data.
AWS ML best practices recommend regularization techniques to address overfitting. Dropout layers randomly deactivate neurons during training, preventing the network from relying too heavily on specific paths. L1 and L2 regularization penalize large weights, reducing model complexity and improving generalization. k-fold cross-validation provides a more robust evaluation by training and validating the model across multiple data splits.
Option A increases complexity, which would worsen overfitting. Option C mixes valid ideas (dimensionality reduction) with unrelated changes (loss function choice) and is less targeted. Option D focuses on data quality but does not directly address model variance.
Therefore, implementing dropout, regularization, and k-fold cross-validation is the correct solution.

NEW QUESTION # 169
An ML engineer is building an ML model in Amazon SageMaker AI. The ML engineer needs to load historical data directly from Amazon S3, Amazon Athena, and Snowflake into SageMaker AI.
Which solution will meet this requirement?

A. Build a pipeline in SageMaker Pipelines to process the data. Use AWS DataSync to load the processed data into SageMaker AI.
B. Create a feature store in SageMaker Feature Store. Use an Apache Spark connector to Feature Store to access the data.
C. Use SageMaker Data Wrangler to query and import the data.
D. Use AWS Glue DataBrew to import the data into SageMaker AI.

Answer: C

Explanation:
AWS provides Amazon SageMaker Data Wrangler as a native tool for importing, transforming, and analyzing data from multiple sources directly into SageMaker Studio. Data Wrangler supports Amazon S3, Amazon Athena, and Snowflake as built-in data sources through managed connectors.
Using Data Wrangler, ML engineers can query data from Athena using SQL, load structured files from S3, and securely connect to Snowflake without writing custom ingestion code. This approach significantly reduces development effort and aligns with AWS best practices for rapid ML experimentation.
Option A is incorrect because AWS Glue DataBrew is designed for data preparation but does not natively integrate with SageMaker training workflows. Option B introduces unnecessary complexity and is not intended for direct ML data loading. Option C focuses on feature storage, not raw historical data ingestion.
Therefore, SageMaker Data Wrangler is the correct solution.

NEW QUESTION # 170
An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning.
The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain.
Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)

A. The model must be registered in the SageMaker Model Registry.
B. The ML engineer and the Canvas user must be in separate SageMaker domains.
C. The ML engineer must deploy the model to a SageMaker endpoint.
D. The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored.
E. The ML engineer must host the model on AWS Marketplace.

Answer: A,D

Explanation:
The SageMaker Canvas user needs permissions to access the Amazon S3 bucket where the model artifacts are stored to retrieve the model for use in Canvas.
Registering the model in the SageMaker Model Registry allows the model to be tracked and managed within the SageMaker ecosystem. This makes it accessible for tuning and deployment through SageMaker Canvas.
This combination ensures proper access control and integration within SageMaker, enabling the Canvas user to work with the model.

NEW QUESTION # 171
A company wants to migrate ML models from an on-premises environment to Amazon SageMaker AI. The models are based on the PyTorch algorithm. The company needs to reuse its existing custom scripts as much as possible.
Which SageMaker AI feature should the company use?

A. SageMaker AI built-in algorithms
B. SageMaker Canvas
C. SageMaker AI script mode
D. SageMaker JumpStart

Answer: C

Explanation:
SageMaker script mode allows ML engineers to bring existing training scripts written for frameworks such as PyTorch and TensorFlow directly into SageMaker with minimal changes. AWS documentation explicitly states that script mode is designed to support migration of existing ML workloads.
With script mode, the user provides a custom training script, and SageMaker handles infrastructure provisioning, distributed training, logging, and model artifact storage. This makes script mode ideal for companies that want to reuse established codebases without rewriting them.
Built-in algorithms require adopting AWS-provided implementations. SageMaker Canvas is a no-code tool, and JumpStart provides pretrained models and templates but does not focus on custom script reuse.
Therefore, Option D is the correct and AWS-recommended choice.

NEW QUESTION # 172
A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second.
The company needs to implement a scalable solution on AWS to identify anomalous data points.
Which solution will meet these requirements with the LEAST operational overhead?

A. Send real-time data to an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Create an AWS Lambda function to consume the queue messages. Program the Lambda function to start an AWS Glue extract, transform, and load (ETL) job for batch processing and anomaly detection.
B. Ingest real-time data into Apache Kafka on Amazon EC2 instances. Deploy an Amazon SageMaker AI endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
C. Ingest real-time data into Amazon Kinesis Data Streams. Deploy an Amazon SageMaker AI endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
D. Ingest real-time data into Amazon Kinesis Data Streams. Use the built-in RANDOM_CUT_FOREST function in Amazon Managed Service for Apache Flink to process the data streams and to detect data anomalies.

Answer: D

Explanation:
The key requirements are real-time processing, high throughput, and minimal operational overhead. Amazon Kinesis Data Streams is designed for ingesting thousands of events per second with low latency.
For anomaly detection on streaming data, Amazon Managed Service for Apache Flink provides a built-in Random Cut Forest (RCF) function. RCF is an unsupervised anomaly detection algorithm that works well on numerical streaming data and does not require labeled training data.
This fully managed combination eliminates the need to deploy or maintain SageMaker endpoints, EC2 instances, or custom ML pipelines. Options B and C introduce unnecessary infrastructure and model management overhead. Option D is batch-oriented and unsuitable for real-time anomaly detection.
Therefore, using Kinesis Data Streams with Flink's built-in Random Cut Forest is the most scalable and low- overhead solution.

NEW QUESTION # 173
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New Amazon MLA-C01 Exam Practice - Cheap MLA-C01 Dumps

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q168-Q173):

Quiz 2026 MLA-C01: High Pass-Rate New AWS Certified Machine Learning Engineer - Associate Exam Practice

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