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最新的 AWS Certified Associate MLA-C01 免費考試真題 (Q39-Q44):

問題 #39
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?

A. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
B. Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
C. Create a model group for each category. Move the existing models into these category model groups.
D. Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.

答案：B

解題說明：
Using custom tags allows you to organize and categorize models in the SageMaker Model Registry without altering their existing groupings or affecting the integrity of the model artifacts. Tags are a lightweight and scalable way to improve model discoverability at scale, enabling the data scientists to filter and identify models by category (e.g., computer vision, NLP, speech recognition). This approach meets the requirements efficiently without introducing structural changes to the existing model registry setup.

問題 #40
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 AWS DataSync task. Configure the task with enhanced fan-out consumers.
B. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue.
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.

答案：C

解題說明：
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.

問題 #41
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company is experimenting with consecutive training jobs.
How can the company MINIMIZE infrastructure startup times for these jobs?

A. Use SageMaker managed warm pools.
B. Use SageMaker Training Compiler.
C. Use the SageMaker distributed data parallelism (SMDDP) library.
D. Use Managed Spot Training.

答案：A

解題說明：
When running consecutive training jobs in Amazon SageMaker, infrastructure provisioning can introduce latency, as each job typically requires the allocation and setup of compute resources. To minimize this startup time and enhance efficiency, Amazon SageMaker offersManaged Warm Pools.
Key Features of Managed Warm Pools:
* Reduced Latency: Reusing existing infrastructure significantly reduces startup time for training jobs.
* Configurable Retention Period: Allows retention of resources after training jobs complete, defined by the KeepAlivePeriodInSeconds parameter.
* Automatic Matching: Subsequent jobs with matching configurations (e.g., instance type) can reuse retained infrastructure.
Implementation Steps:
* Request Warm Pool Quota Increase: Increase the default resource quota for warm pools through AWS Service Quotas.
* Configure Training Jobs:
* Set KeepAlivePeriodInSeconds for the first training job to retain resources.
* Ensure subsequent jobs match the retained pool's configuration to enable reuse.
* Monitor Warm Pool Usage: Track warm pool status through the SageMaker console or API to confirm resource reuse.
Considerations:
* Billing: Resources in warm pools are billable during the retention period.
* Matching Requirements: Jobs must have consistent configurations to use warm pools effectively.
Alternative Options:
* Managed Spot Training: Reduces costs by using spare capacity but doesn't address startup latency.
* SageMaker Training Compiler: Optimizes training time but not infrastructure setup.
* SageMaker Distributed Data Parallelism Library: Enhances training efficiency but doesn't reduce setup time.
By usingManaged Warm Pools, the company can significantly reduce startup latency for consecutive training jobs, ensuring faster experimentation cycles with minimal operational overhead.
References:
* AWS Documentation: Managed Warm Pools
* AWS Blog: Reduce ML Model Training Job Startup Time

問題 #42
An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed- circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.
The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.
Which solution will improve the model's accuracy in the LEAST amount of time?

A. Recreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.
B. Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.
C. Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.
D. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.

答案：D

解題說明：
The model is underperforming in production due to variations in image quality from different cameras. Using the corrupt image transform with the impulse noise option in SageMaker Data Wrangler simulates real-world noise and variations in the training dataset. This approach helps the model become more robust to inconsistencies in image quality, improving its accuracy in production without the need to collect and process new data, thereby saving time.

問題 #43
An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers. The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML.
Which solution will meet these requirements?

A. Use Amazon SageMaker Data Wrangler to import the datasets and to consolidate them into a single data frame. Use the cleansing and enrichment functionalities to prepare the data.
B. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon SageMaker data labeling to prepare the data.
C. Use Amazon SageMaker Ground Truth to import the datasets and to consolidate them into a single data frame. Use the human-in-the-loop capability to prepare the data.
D. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon Q Developer to generate code snippets that will prepare the data.

答案：A

解題說明：
Amazon SageMakerData Wranglerprovides a comprehensive solution for importing, consolidating, and preparing datasets for ML. It offers tools to handle missing values, duplicates, and outliers through its built- incleansingandenrichmentfunctionalities, allowing the ML engineer to efficiently prepare the data in a single environment with minimal manual effort.

問題 #44
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