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MLA-C01 Exam Dumps Collection & Instant MLA-C01 Download

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MLA-C01 Exam Dumps Collection & Instant MLA-C01 Download

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Amazon MLA-C01 Exam Syllabus Topics:
TopicDetails
Topic 1
  • ML Model Development: This section of the exam measures skills of Fraud Examiners and covers choosing and training machine learning models to solve business problems such as fraud detection. It includes selecting algorithms, using built-in or custom models, tuning parameters, and evaluating performance with standard metrics. The domain emphasizes refining models to avoid overfitting and maintaining version control to support ongoing investigations and audit trails.
Topic 2
  • Deployment and Orchestration of ML Workflows: This section of the exam measures skills of Forensic Data Analysts and focuses on deploying machine learning models into production environments. It covers choosing the right infrastructure, managing containers, automating scaling, and orchestrating workflows through CI
  • CD pipelines. Candidates must be able to build and script environments that support consistent deployment and efficient retraining cycles in real-world fraud detection systems.
Topic 3
  • ML Solution Monitoring, Maintenance, and Security: This section of the exam measures skills of Fraud Examiners and assesses the ability to monitor machine learning models, manage infrastructure costs, and apply security best practices. It includes setting up model performance tracking, detecting drift, and using AWS tools for logging and alerts. Candidates are also tested on configuring access controls, auditing environments, and maintaining compliance in sensitive data environments like financial fraud detection.
Topic 4
  • Data Preparation for Machine Learning (ML): This section of the exam measures skills of Forensic Data Analysts and covers collecting, storing, and preparing data for machine learning. It focuses on understanding different data formats, ingestion methods, and AWS tools used to process and transform data. Candidates are expected to clean and engineer features, ensure data integrity, and address biases or compliance issues, which are crucial for preparing high-quality datasets in fraud analysis contexts.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q191-Q196):NEW QUESTION # 191
A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account.
An ML engineer needs to set up an ML pipeline in the primary account to access the S3 bucket in the secondary account. The solution must not require public IPv4 addresses.
Which solution will meet these requirements?
  • A. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create a VPC peering connection between the accounts. Update the VPC route tables to remove the route to 0.0.0.0/0.
  • B. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create an AWS Direct Connect connection and a transit gateway.
    Associate the VPCs from both accounts with the transit gateway. Update the VPC route tables to remove the route to 0.0.0.0/0.
  • C. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account.
    Create an AWS Site-to-Site VPN connection with two encrypted IPsec tunnels between the accounts. Set up interface VPC endpoints for Amazon S3.
  • D. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account.Create an S3 gateway endpoint. Update the S3 bucket policy to allow IAM principals from the primary account. Set up interface VPC endpoints for SageMaker and Amazon Redshift.
Answer: D

NEW QUESTION # 192
A company is training a large language model (LLM) by using on-premises infrastructure. A live conversational engine uses the LLM to help customers find real-time insights in credit card data.
An ML engineer must implement a solution to train and deploy the LLM on Amazon SageMaker.
Which solution will meet these requirements?
  • A. Use SageMaker Notebook Jobs to train the LLM. Deploy the LLM by using SageMaker Asynchronous Inference.
  • B. Use SageMaker with deep learning containers for large model inference to train the LLM. Deploy the LLM by using SageMaker real-time inference.
  • C. Use SageMaker Studio to train the LLM. Deploy the LLM by using SageMaker batch transform.
  • D. Use SageMaker Training Compiler to train the LLM. Deploy the LLM by using SageMaker real- time inference.
Answer: D
Explanation:
SageMaker Training Compiler accelerates training of large models like LLMs by optimizing GPU utilization, making it suitable for efficient large-scale training. For deployment of a live conversational engine that requires real-time responses, the correct choice is a SageMaker real- time inference endpoint. This combination meets both training and deployment requirements effectively.

NEW QUESTION # 193
A company has trained and deployed an ML model by using Amazon SageMaker. The company needs to implement a solution to record and monitor all the API call events for the SageMaker endpoint. The solution also must provide a notification when the number of API call events breaches a threshold.
Which solution will meet these requirements?
  • A. Add the Invocations metric to an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.
  • B. Log all the endpoint invocation API events by using AWS CloudTrail. Use an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.
  • C. Use SageMaker Debugger to track the inferences and to report metrics. Use the tensor_variance built-in rule to provide a notification when the threshold is breached.
  • D. Use SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to provide a notification when the threshold is breached.
Answer: B

NEW QUESTION # 194
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. Ingest real-time data into Apache Kafka on Amazon EC2 instances. Deploy an Amazon SageMaker endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
  • B. 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.
  • C. 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.
  • D. Ingest real-time data into Amazon Kinesis data streams. Deploy an Amazon SageMaker endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
Answer: B

NEW QUESTION # 195
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine.
The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?
  • A. Deploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.
  • B. Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.
  • C. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data.
  • D. Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.
Answer: B
Explanation:
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.

NEW QUESTION # 196
......
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