2025 VALID MLA-C01 EXAM BIBLE HELP YOU PASS MLA-C01 EASILY

2025 Valid MLA-C01 Exam Bible Help You Pass MLA-C01 Easily

2025 Valid MLA-C01 Exam Bible Help You Pass MLA-C01 Easily

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Tags: MLA-C01 Exam Bible, MLA-C01 Valid Exam Answers, MLA-C01 New Questions, Valid MLA-C01 Mock Exam, Dumps MLA-C01 Guide

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Real Exam Questions & Answers - Amazon MLA-C01 Dump is Ready

If you want to get promotions or high-paying jobs in the Amazon sector, then it is important for you to crack the AWS Certified Machine Learning Engineer - Associate (MLA-C01) certification exam. The Amazon MLA-C01 certification has become the best way to validate your skills and accelerate your tech career. MLA-C01 Exam applicants who are doing jobs or busy with their other matters usually don't have enough time to study for the test.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q23-Q28):

NEW QUESTION # 23
An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.
Which instance purchasing option will meet these requirements MOST cost-effectively?

  • A. Run the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.
  • B. Run the primary node on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.
  • C. Run the primary node, core nodes, and task nodes on On-Demand Instances.
  • D. Run the primary node, core nodes, and task nodes on Spot Instances.

Answer: A

Explanation:
For Amazon EMR, the primary node and core nodes handle the critical functions of the cluster, including data storage (HDFS) and processing. Running them on On-Demand Instances ensures high availability and prevents data loss, as Spot Instances can be interrupted. The task nodes, which handle additionalprocessing but do not store data, can use Spot Instances to reduce costs without compromising the cluster's resilience or data integrity. This configuration balances cost-effectiveness and reliability.


NEW QUESTION # 24
An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to train a model.
Select and order the steps from the following list to create and use the features in Feature Store. Each step should be selected one time. (Select and order three.)
* Access the store to build datasets for training.
* Create a feature group.
* Ingest the records.

Answer:

Explanation:

Explanation:

Step 1: Create a feature group.Step 2: Ingest the records.Step 3: Access the store to build datasets for training.
* Step 1: Create a Feature Group
* Why?A feature group is the foundational unit in SageMaker Feature Store, where features are defined, stored, and organized. Creating a feature group specifies the schema (name, data type) for the features and the primary keys for data identification.
* How?Use the SageMaker Python SDK or AWS CLI to define the feature group by specifying its name, schema, and S3 storage location for offline access.
* Step 2: Ingest the Records
* Why?After creating the feature group, the raw data must be ingested into the Feature Store. This step populates the feature group with data, making it available for both real-time and offline use.
* How?Use the SageMaker SDK or AWS CLI to batch-ingest historical data or stream new records into the feature group. Ensure the records conform to the feature group schema.
* Step 3: Access the Store to Build Datasets for Training
* Why?Once the features are stored, they can be accessed to create training datasets. These datasets combine relevant features into a single format for machine learning model training.
* How?Use the SageMaker Python SDK to query the offline store or retrieve real-time features using the online store API. The offline store is typically used for batch training, while the online store is used for inference.
Order Summary:
* Create a feature group.
* Ingest the records.
* Access the store to build datasets for training.
This process ensures the features are properly managed, ingested, and accessible for model training using Amazon SageMaker Feature Store.


NEW QUESTION # 25
A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.
Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?

  • A. Spot Instances
  • B. Dedicated Instances
  • C. Reserved Instances
  • D. On-Demand Instances

Answer: A

Explanation:
Scenario:The company needs to run a batch job for 90 minutes every weekend over the next 6 months. The processing can handle interruptions, and cost-effectiveness is a priority.
Why Spot Instances?
* Cost-Effective:Spot Instances provide up to 90% savings compared to On-Demand Instances, making them the most cost-effective option for batch processing.
* Interruption Tolerance:Since the processing can tolerate interruptions, Spot Instances are suitable for this workload.
* Batch-Friendly:Spot Instances can be requested for specific durations or automatically re-requested in case of interruptions.
Steps to Implement:
* Create a Spot Instance Request:
* Use the EC2 console or CLI to request Spot Instances with desired instance type and duration.
* Use Auto Scaling:Configure Spot Instances with an Auto Scaling group to handle instance interruptions and ensure job completion.
* Run the Batch Job:Use tools like AWS Batch or custom scripts to manage the processing.
Comparison with Other Options:
* Reserved Instances:Suitable for predictable, continuous workloads, but less cost-effective for a job that runs only once a week.
* On-Demand Instances:More expensive and unnecessary given the tolerance for interruptions.
* Dedicated Instances:Best for isolation and compliance but significantly more costly.
References:
* Amazon EC2 Spot Instances
* Best Practices for Using Spot Instances
* AWS Batch for Spot Instances


NEW QUESTION # 26
A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.
The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.
Which change to the architecture will meet these requirements?

  • A. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue.
  • B. Replace the Firehose stream with an AWS DataSync task. Configure the task with enhanced fan-out consumers.
  • C. Use zero buffering in the Firehose stream. Tune the batch size that is used in the PutRecordBatch operation.
  • D. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds.

Answer: C

Explanation:
Amazon Kinesis Data Firehose allows for near real-time data streaming. Setting thebuffering hintsto zero or a very small value minimizes the buffering delay and ensures that records are delivered to the destination (Amazon OpenSearch Service) as quickly as possible. Additionally, tuning thebatch sizein thePutRecordBatchoperation can further optimize the data ingestion for sub-second latency. This approach minimizes latency while maintaining the operational simplicity of using Firehose.


NEW QUESTION # 27
An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models.
The ML engineer must receive alerts when changes in data quality occur.
Which solution will meet these requirements?

  • A. Deploy the models by using Amazon Elastic Container Service (Amazon ECS) on AWS Fargate. Use Amazon EventBridge to monitor the data quality and to send alerts.
  • B. Deploy the models by using scheduled AWS Glue jobs. Use Amazon CloudWatch alarms to monitor the data quality and to send alerts.
  • C. Deploy the models by using scheduled AWS Batch jobs. Use AWS CloudTrail to monitor the data quality and to send alerts.
  • D. Deploy the models by using Amazon SageMaker batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts.

Answer: D

Explanation:
Amazon SageMaker batch transform is ideal for obtaining inferences from large datasets in an asynchronous manner, as it processes data in batches rather than requiring real-time inputs.
SageMaker Model Monitor allows scheduled monitoring of data quality, detecting shifts in input data characteristics, and generating alerts when changes in data quality occur.
This solution provides a fully managed, efficient way to handle both asynchronous inference and data quality monitoring with minimal operational overhead.


NEW QUESTION # 28
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