100% PASS 2025 THE BEST AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY: HIGH AWS CERTIFIED MACHINE LEARNING - SPECIALTY PASSING SCORE

100% Pass 2025 The Best AWS-Certified-Machine-Learning-Specialty: High AWS Certified Machine Learning - Specialty Passing Score

100% Pass 2025 The Best AWS-Certified-Machine-Learning-Specialty: High AWS Certified Machine Learning - Specialty Passing Score

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Passing the AWS-Certified-Machine-Learning-Specialty Exam demonstrates an individual’s ability to design and implement machine learning solutions on the AWS platform. AWS Certified Machine Learning - Specialty certification is highly valued by employers and can lead to higher-paying job opportunities. Additionally, certified individuals can join the AWS Certified Community, which provides access to resources and networking opportunities with other certified professionals.

Achieving the Amazon MLS-C01 certification is an excellent way for professionals to demonstrate their expertise in machine learning and to advance their careers. It is also a valuable credential for organizations that are looking to hire skilled professionals in the field of machine learning. By becoming certified in Amazon MLS-C01, candidates can show their dedication to staying current with the latest trends and technologies in the rapidly evolving field of machine learning.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q80-Q85):

NEW QUESTION # 80
A company is creating an application to identify, count, and classify animal images that are uploaded to the company's website. The company is using the Amazon SageMaker image classification algorithm with an ImageNetV2 convolutional neural network (CNN). The solution works well for most animal images but does not recognize many animal species that are less common.
The company obtains 10,000 labeled images of less common animal species and stores the images in Amazon S3. A machine learning (ML) engineer needs to incorporate the images into the model by using Pipe mode in SageMaker.
Which combination of steps should the ML engineer take to train the model? (Choose two.)

  • A. Use an Inception model that is available with the SageMaker image classification algorithm.
  • B. Use a ResNet model. Initiate full training mode by initializing the network with random weights.
  • C. Create a .lst file that contains a list of image files and corresponding class labels. Upload the .lst file to Amazon S3.
  • D. Use an augmented manifest file in JSON Lines format.
  • E. Initiate transfer learning. Train the model by using the images of less common species.

Answer: C,E

Explanation:
Explanation
The combination of steps that the ML engineer should take to train the model are to create a .lst file that contains a list of image files and corresponding class labels, upload the .lst file to Amazon S3, and initiate transfer learning by training the model using the images of less common species. This approach will allow the ML engineer to leverage the existing ImageNetV2 CNN model and fine-tune it with the new data using Pipe mode in SageMaker.
A .lst file is a text file that contains a list of image files and corresponding class labels, separated by tabs. The
.lst file format is required for using the SageMaker image classification algorithm with Pipe mode. Pipe mode is a feature of SageMaker that enables streaming data directly from Amazon S3 to the training instances, without downloading the data first. Pipe mode can reduce the startup time, improve the I/O throughput, and enable training on large datasets that exceed the disk size limit. To use Pipe mode, the ML engineer needs to upload the .lst file to Amazon S3 and specify the S3 path as the input data channel for the training job1.
Transfer learning is a technique that enables reusing a pre-trained model for a new task by fine-tuning the model parameters with new data. Transfer learning can save time and computational resources, as well as improve the performance of the model, especially when the new task is similar to the original task. The SageMaker image classification algorithm supports transfer learning by allowing the ML engineer to specify the number of output classes and the number of layers to be retrained. The ML engineer can use the existing ImageNetV2 CNN model, which is trained on 1,000 classes of common objects, and fine-tune it with the new data of less common animal species, which is a similar task2.
The other options are either less effective or not supported by the SageMaker image classification algorithm.
Using a ResNet model and initiating full training mode would require training the model from scratch, which would take more time and resources than transfer learning. Using an Inception model is not possible, as the SageMaker image classification algorithm only supports ResNet and ImageNetV2 models. Using an augmented manifest file in JSON Lines format is not compatible with Pipe mode, as Pipe mode only supports
.lst files for image classification1.
References:
1: Using Pipe input mode for Amazon SageMaker algorithms | AWS Machine Learning Blog
2: Image Classification Algorithm - Amazon SageMaker


NEW QUESTION # 81
A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.
What combination of services should the team use to build a custom algorithm in Amazon SageMaker?
(Choose two.)

  • A. Amazon S3
  • B. AWS CodeStar
  • C. AWS Secrets Manager
  • D. Amazon ECR
  • E. Amazon ECS

Answer: A,D

Explanation:
* The Machine Learning team wants to use its own training algorithm on Amazon SageMaker, and submit both its own algorithm code and algorithm-specific parameters. The best combination of services to build a custom algorithm in Amazon SageMaker are Amazon ECR and Amazon S3.
* Amazon ECR is a fully managed container registry service that allows you to store, manage, and deploy Docker container images. You can use Amazon ECR to create a Docker image that contains your training algorithm code and any dependencies or libraries that it requires. You can also use Amazon ECR to push, pull, and manage your Docker images securely and reliably.
* Amazon S3 is a durable, scalable, and secure object storage service that can store any amount and type of data. You can use Amazon S3 to store your training data, model artifacts, and algorithm-specific parameters. You can also use Amazon S3 to access your data and parameters from your training algorithm code, and to write your model output to a specified location.
* Therefore, the Machine Learning team can use the following steps to build a custom algorithm in Amazon SageMaker:
* Write the training algorithm code in Python, using the Amazon SageMaker Python SDK or the Amazon SageMaker Containers library to interact with the Amazon SageMaker service. The code should be able to read the input data and parameters from Amazon S3, and write the model output to Amazon S3.
* Create a Dockerfile that defines the base image, the dependencies, the environment variables, and the commands to run the training algorithm code. The Dockerfile should also expose the ports that Amazon SageMaker uses to communicate with the container.
* Build the Docker image using the Dockerfile, and tag it with a meaningful name and version.
* Push the Docker image to Amazon ECR, and note the registry path of the image.
* Upload the training data, model artifacts, and algorithm-specific parameters to Amazon S3, and note the S3 URIs of the objects.
* Create an Amazon SageMaker training job, using the Amazon SageMaker Python SDK or the AWS CLI. Specify the registry path of the Docker image, the S3 URIs of the input and output data, the algorithm-specific parameters, and other configuration options, such as the instance type, the number of instances, the IAM role, and the hyperparameters.
* Monitor the status and logs of the training job, and retrieve the model output from Amazon S3.
Use Your Own Training Algorithms
Amazon ECR - Amazon Web Services
Amazon S3 - Amazon Web Services


NEW QUESTION # 82
A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.
Which action will resolve the problem?

  • A. Adjust hyperparameters related to the attention mechanism.
  • B. Choose a different weight initialization type.
  • C. Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.
  • D. Change preprocessing to use n-grams.

Answer: C


NEW QUESTION # 83
A data scientist uses Amazon SageMaker Data Wrangler to define and perform transformations and feature engineering on historical data. The data scientist saves the transformations to SageMaker Feature Store.
The historical data is periodically uploaded to an Amazon S3 bucket. The data scientist needs to transform the new historic data and add it to the online feature store The data scientist needs to prepare the .....historic data for training and inference by using native integrations.
Which solution will meet these requirements with the LEAST development effort?

  • A. Use AWS Lambda to run a predefined SageMaker pipeline to perform the transformations on each new dataset that arrives in the S3 bucket.
  • B. Use Apache Airflow to orchestrate a set of predefined transformations on each new dataset that arrives in the S3 bucket.
  • C. Run an AWS Step Functions step and a predefined SageMaker pipeline to perform the transformations on each new dalaset that arrives in the S3 bucket
  • D. Configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket.

Answer: D

Explanation:
The best solution is to configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket. This solution requires the least development effort because it leverages the native integration between EventBridge and SageMaker Pipelines, which allows you to trigger a pipeline execution based on an event rule. EventBridge can monitor the S3 bucket for new data uploads and invoke the pipeline that contains the same transformations and feature engineering steps that were defined in SageMaker Data Wrangler. The pipeline can then ingest the transformed data into the online feature store for training and inference.
The other solutions are less optimal because they require more development effort and additional services.
Using AWS Lambda or AWS Step Functions would require writing custom code to invoke the SageMaker pipeline and handle any errors or retries. Using Apache Airflow would require setting up and maintaining an Airflow server and DAGs, as well as integrating with the SageMaker API.
Amazon EventBridge and Amazon SageMaker Pipelines integration
Create a pipeline using a JSON specification
Ingest data into a feature group


NEW QUESTION # 84
A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake.
Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream. As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.
Which next step is MOST likely to improve the data ingestion rate into Amazon S3?

  • A. Decrease the retention period for the data stream.
  • B. Add more consumers using the Kinesis Client Library (KCL).
  • C. Increase the number of shards for the data stream.
  • D. Increase the number of S3 prefixes for the delivery stream to write to.

Answer: C

Explanation:
Explanation
The solution C is the most likely to improve the data ingestion rate into Amazon S3 because it increases the number of shards for the data stream. The number of shards determines the throughput capacity of the data stream, which affects the rate of data ingestion. Each shard can support up to 1 MB per second of data input and 2 MB per second of data output. By increasing the number of shards, the company can increase the data ingestion rate proportionally. The company can use the UpdateShardCount API operation to modify the number of shards in the data stream1.
The other options are not likely to improve the data ingestion rate into Amazon S3 because:
Option A: Increasing the number of S3 prefixes for the delivery stream to write to will not affect the data ingestion rate, as it only changes the way the data is organized in the S3 bucket. The number of S3 prefixes can help to optimize the performance of downstream applications that read the data from S3, but it does not impact the performance of Kinesis Data Firehose2.
Option B: Decreasing the retention period for the data stream will not affect the data ingestion rate, as it only changes the amount of time the data is stored in the data stream. The retention period can help to manage the data availability and durability, but it does not impact the throughput capacity of the data stream3.
Option D: Adding more consumers using the Kinesis Client Library (KCL) will not affect the data ingestion rate, as it only changes the way the data is processed by downstream applications. The consumers can help to scale the data processing and handle failures, but they do not impact the data ingestion into S3 by Kinesis Data Firehose4.
References:
1: Resharding - Amazon Kinesis Data Streams
2: Amazon S3 Prefixes - Amazon Kinesis Data Firehose
3: Data Retention - Amazon Kinesis Data Streams
4: Developing Consumers Using the Kinesis Client Library - Amazon Kinesis Data Streams


NEW QUESTION # 85
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