Learn Ruqyah

Lily Cooper Lily Cooper

0 Course Enrolled • 0 Course Completed

Biography

AWS-Certified-Machine-Learning-Specialty Best Study Material - Study AWS-Certified-Machine-Learning-Specialty Group

P.S. Free & New AWS-Certified-Machine-Learning-Specialty dumps are available on Google Drive shared by VerifiedDumps: https://drive.google.com/open?id=1Ds4DYib_ZZo8T4JDcBEpMQZ0rFOLTI4u

Amazon AWS-Certified-Machine-Learning-Specialty certifications are thought to be the best way to get good jobs in the high-demanding market. There is a large range of AWS-Certified-Machine-Learning-Specialty certifications that can help you improve your professional worth and make your dreams come true. Our AWS Certified Machine Learning - Specialty AWS-Certified-Machine-Learning-Specialty Certification Practice materials provide you with a wonderful opportunity to get your dream certification with confidence and ensure your success by your first attempt.

AWS Certified Machine Learning Specialty Exam topics

Candidates must know the exam topics before they start of preparation. Because it will really help them in hitting the core. Our AWS Certified Machine Learning Specialty exam dumps will include the following topics:

Domain 4: Machine Learning Implementation and Operations 20%
Domain 2: Exploratory Data Analysis 24%
Domain 3: Modeling 36%
Domain 1: Data Engineering 20%

The AWS Certified Machine Learning - Specialty certification exam is a challenging and rigorous exam that requires a significant amount of preparation and study. To prepare for the exam, candidates can take advantage of a wide range of resources, including online courses, study guides, and practice exams. They can also attend training courses and workshops offered by AWS or other training providers.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam offered by Amazon Web Services for individuals who are interested in demonstrating their proficiency in machine learning on the AWS platform. AWS-Certified-Machine-Learning-Specialty Exam is designed for professionals who have a strong understanding of machine learning concepts and the ability to design, implement, and maintain machine learning solutions on AWS.

>> AWS-Certified-Machine-Learning-Specialty Best Study Material <<

Latest Amazon AWS-Certified-Machine-Learning-Specialty Practice test Material in Three Different Formats

To attempt the Amazon AWS-Certified-Machine-Learning-Specialty exam optimally and ace it on the first attempt, proper exam planning is crucial. Since the Amazon AWS-Certified-Machine-Learning-Specialty exam demands a lot of time and effort, we designed the AWS Certified Machine Learning - Specialty (AWS-Certified-Machine-Learning-Specialty) exam dumps in such a way that you won't have to go through sleepless study nights or disturb your schedule. Before starting the Amazon AWS-Certified-Machine-Learning-Specialty Preparation, plan the amount of time you will allot to each topic, determine the topics that demand more effort and prioritize the components that possess more weightage in the Amazon AWS-Certified-Machine-Learning-Specialty exam.

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

NEW QUESTION # 80
A company's data scientist has trained a new machine learning model that performs better on test data than the company's existing model performs in the production environment. The data scientist wants to replace the existing model that runs on an Amazon SageMaker endpoint in the production environment. However, the company is concerned that the new model might not work well on the production environment data.
The data scientist needs to perform A/B testing in the production environment to evaluate whether the new model performs well on production environment data.
Which combination of steps must the data scientist take to perform the A/B testing? (Choose two.)

A. Deploy the new model to the existing endpoint.
B. Create a new endpoint configuration that includes two target variants that point to different endpoints.
C. Update the existing endpoint to use the new endpoint configuration.
D. Create a new endpoint configuration that includes a production variant for each of the two models.
E. Update the existing endpoint to activate the new model.

Answer: C,D

Explanation:
Explanation
The combination of steps that the data scientist must take to perform the A/B testing are to create a new endpoint configuration that includes a production variant for each of the two models, and update the existing endpoint to use the new endpoint configuration. This approach will allow the data scientist to deploy both models on the same endpoint and split the inference traffic between them based on a specified distribution.
Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to quickly build, train, and deploy machine learning models. Amazon SageMaker supports A/B testing on machine learning models by allowing the data scientist to run multiple production variants on an endpoint. A production variant is a version of a model that is deployed on an endpoint. Each production variant has a name, a machine learning model, an instance type, an initial instance count, and an initial weight. The initial weight determines the percentage of inference requests that the variant will handle. For example, if there are two variants with weights of 0.5 and 0.5, each variant will handle 50% of the requests. The data scientist can use production variants to test models that have been trained using different training datasets, algorithms, and machine learning frameworks; test how they perform on different instance types; or a combination of all of the above1.
To perform A/B testing on machine learning models, the data scientist needs to create a new endpoint configuration that includes a production variant for each of the two models. An endpoint configuration is a collection of settings that define the properties of an endpoint, such as the name, the production variants, and the data capture configuration. The data scientist can use the Amazon SageMaker console, the AWS CLI, or the AWS SDKs to create a new endpoint configuration. The data scientist needs to specify the name, model name, instance type, initial instance count, and initial variant weight for each production variant in the endpoint configuration2.
After creating the new endpoint configuration, the data scientist needs to update the existing endpoint to use the new endpoint configuration. Updating an endpoint is the process of deploying a new endpoint configuration to an existing endpoint. Updating an endpoint does not affect the availability or scalability of the endpoint, as Amazon SageMaker creates a new endpoint instance with the new configuration and switches the DNS record to point to the new instance when it is ready. The data scientist can use the Amazon SageMaker console, the AWS CLI, or the AWS SDKs to update an endpoint. The data scientist needs to specify the name of the endpoint and the name of the new endpoint configuration to update the endpoint3.
The other options are either incorrect or unnecessary. Creating a new endpoint configuration that includes two target variants that point to different endpoints is not possible, as target variants are only used to invoke a specific variant on an endpoint, not to define an endpoint configuration. Deploying the new model to the existing endpoint would replace the existing model, not run it side-by-side with the new model. Updating the existing endpoint to activate the new model is not a valid operation, as there is no activation parameter for an endpoint.
References:
1: A/B Testing ML models in production using Amazon SageMaker | AWS Machine Learning Blog
2: Create an Endpoint Configuration - Amazon SageMaker
3: Update an Endpoint - Amazon SageMaker

NEW QUESTION # 81
A wildlife research company has a set of images of lions and cheetahs. The company created a dataset of the images. The company labeled each image with a binary label that indicates whether an image contains a lion or cheetah. The company wants to train a model to identify whether new images contain a lion or cheetah.
.... Dh Amazon SageMaker algorithm will meet this requirement?

A. XGBoost
B. Semantic segmentation - MXNet
C. Image Classification - TensorFlow
D. Object Detection - TensorFlow

Answer: C

Explanation:
The best Amazon SageMaker algorithm for this task is Image Classification - TensorFlow. This algorithm is a supervised learning algorithm that supports transfer learning with many pretrained models from the TensorFlow Hub. Transfer learning allows the company to fine-tune one of the available pretrained models on their own dataset, even if a large amount of image data is not available. The image classification algorithm takes an image as input and outputs a probability for each provided class label. The company can choose from a variety of models, such as MobileNet, ResNet, or Inception, depending on their accuracy and speed requirements. The algorithm also supports distributed training, data augmentation, and hyperparameter tuning.
Image Classification - TensorFlow - Amazon SageMaker
Amazon SageMaker Provides New Built-in TensorFlow Image Classification Algorithm Image Classification with ResNet :: Amazon SageMaker Workshop Image classification on Amazon SageMaker | by Julien Simon - Medium

NEW QUESTION # 82
This graph shows the training and validation loss against the epochs for a neural network The network being trained is as follows
* Two dense layers one output neuron
* 100 neurons in each layer
* 100 epochs
* Random initialization of weights

Which technique can be used to improve model performance in terms of accuracy in the validation set?

A. Random initialization of weights with appropriate seed
B. Early stopping
C. Adding another layer with the 100 neurons
D. Increasing the number of epochs

Answer: C

NEW QUESTION # 83
A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS). The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.
How should the data scientist meet these requirements?

A. Run a SageMaker training job with an EFS file system as the data source.
B. Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.
C. Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.
D. Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.

Answer: D

NEW QUESTION # 84
A media company is building a computer vision model to analyze images that are on social media. The model consists of CNNs that the company trained by using images that the company stores in Amazon S3. The company used an Amazon SageMaker training job in File mode with a single Amazon EC2 On-Demand Instance.
Every day, the company updates the model by using about 10,000 images that the company has collected in the last 24 hours. The company configures training with only one epoch. The company wants to speed up training and lower costs without the need to make any code changes.
Which solution will meet these requirements?

A. Instead Of File mode, configure the SageMaker training job to use FastFile mode with no Other changes.
B. Instead Of On-Demand Instances, configure the SageMaker training job to use Spot Instances. Make no Other changes.
C. Instead Of On-Demand Instances, configure the SageMaker training job to use Spot Instances.Implement model checkpoints.
D. Instead of File mode, configure the SageMaker training job to use Pipe mode. Ingest the data from a pipe.

Answer: B

Explanation:
Explanation
The solution C will meet the requirements because it uses Amazon SageMaker Spot Instances, which are unused EC2 instances that are available at up to 90% discount compared to On-Demand prices. Amazon SageMaker Spot Instances can speed up training and lower costs by taking advantage of the spare EC2 capacity. The company does not need to make any code changes to use Spot Instances, as it can simply enable the managed spot training option in the SageMaker training job configuration. The company also does not need to implement model checkpoints, as it is using only one epoch for training, which means the model will not resume from a previous state1.
The other options are not suitable because:
Option A: Configuring the SageMaker training job to use Pipe mode instead of File mode will not speed up training or lower costs significantly. Pipe mode is a data ingestion mode that streams data directly from S3 to the training algorithm, without copying the data to the local storage of the training instance.
Pipe mode can reduce the startup time of the training job and the disk space usage, but it does not affect the computation time or the instance price. Moreover, Pipe mode may require some code changes to handle the streaming data, depending on the training algorithm2.
Option B: Configuring the SageMaker training job to use FastFile mode instead of File mode will not speed up training or lower costs significantly. FastFile mode is a data ingestion mode that copies data from S3 to the local storage of the training instance in parallel with the training process. FastFile mode can reduce the startup time of the training job and the disk space usage, but it does not affect the computation time or the instance price. Moreover, FastFile mode is only available for distributed training jobs that use multiple instances, which is not the case for the company3.
Option D: Configuring the SageMaker training job to use Spot Instances and implementing model checkpoints will not meet the requirements without the need to make any code changes. Model checkpoints are a feature that allows the training job to save the model state periodically to S3, and resume from the latest checkpoint if the training job is interrupted. Model checkpoints can help to avoid losing the training progress and ensure the model convergence, but they require some code changes to implement the checkpointing logic and the resuming logic4.
References:
1: Managed Spot Training - Amazon SageMaker
2: Pipe Mode - Amazon SageMaker
3: FastFile Mode - Amazon SageMaker
4: Checkpoints - Amazon SageMaker

NEW QUESTION # 85
......

The AWS Certified Machine Learning - Specialty certification provides beginners and professionals with multiple great career opportunities. The Amazon Exam AWS-Certified-Machine-Learning-Specialty examination is one of the most demanding Amazon tests. There are multiple benefits you can get after cracking the AWS-Certified-Machine-Learning-Specialty test. The top-listed benefits include skill verification, high-paying jobs, bonuses, and promotions in your current organizations. All these benefits of earning the AWS-Certified-Machine-Learning-Specialty certificate help you level up your career in the tech sector.

Study AWS-Certified-Machine-Learning-Specialty Group: https://www.verifieddumps.com/AWS-Certified-Machine-Learning-Specialty-valid-exam-braindumps.html

P.S. Free 2025 Amazon AWS-Certified-Machine-Learning-Specialty dumps are available on Google Drive shared by VerifiedDumps: https://drive.google.com/open?id=1Ds4DYib_ZZo8T4JDcBEpMQZ0rFOLTI4u