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AWS-Certified-Machine-Learning-Specialty Exam Questions Vce & Downloadable AWS-Certified-Machine-Learning-Specialty PDF
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The Amazon AWS-Certified-Machine-Learning-Specialty exam consists of 65 multiple-choice and multiple-response questions, and candidates are given 180 minutes to complete the exam. AWS-Certified-Machine-Learning-Specialty exam fee is $300, and candidates must achieve a passing score of 750 out of 1000 to earn their certification.
The AWS-Certified-Machine-Learning-Specialty exam consists of 65 multiple-choice and multiple-response questions, and candidates have 180 minutes to complete the exam. AWS-Certified-Machine-Learning-Specialty exam covers a wide range of topics, including data preparation, feature engineering, model selection and evaluation, and deployment and implementation.
Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is a valuable credential for professionals who want to specialize in machine learning. AWS-Certified-Machine-Learning-Specialty Exam Tests the candidate's ability to design, implement, and maintain machine learning solutions on the AWS platform. To prepare for the exam, candidates should have a solid understanding of machine learning algorithms, data modeling, and cloud computing concepts, and should take advantage of the training and certification resources available through AWS.
Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q194-Q199):
NEW QUESTION # 194
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII).
The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?
- A. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
- B. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance
- C. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
- D. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
Answer: C
NEW QUESTION # 195
A company uses camera images of the tops of items displayed on store shelves to determine which items were removed and which ones still remain. After several hours of data labeling, the company has a total of
1,000 hand-labeled images covering 10 distinct items. The training results were poor.
Which machine learning approach fulfills the company's long-term needs?
- A. Reduce the number of distinct items from 10 to 2, build the model, and iterate
- B. Convert the images to grayscale and retrain the model
- C. Augment training data for each item using image variants like inversions and translations, build the model, and iterate.
- D. Attach different colored labels to each item, take the images again, and build the model
Answer: C
Explanation:
Data augmentation is a technique that can increase the size and diversity of the training data by applying various transformations to the original images, such as inversions, translations, rotations, scaling, cropping, flipping, and color variations. Data augmentation can help improve the performance and generalization of image classification models by reducing overfitting and introducing more variability to the data. Data augmentation is especially useful when the original data is limited or imbalanced, as in the case of the company's problem. By augmenting the training data for each item using image variants, the company can build a more robust and accurate model that can recognize the items on the store shelves from different angles, positions, and lighting conditions. The company can also iterate on the model by adding more data or fine-tuning the hyperparameters to achieve better results.
References:
Build high performing image classification models using Amazon SageMaker JumpStart The Effectiveness of Data Augmentation in Image Classification using Deep Learning Data augmentation for improving deep learning in image classification problem Class-Adaptive Data Augmentation for Image Classification
NEW QUESTION # 196
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker.
The historical training data is stored in Amazon RDS.
Which approach should the Specialist use for training a model using that data?
- A. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.
- B. Write a direct connection to the SQL database within the notebook and pull data in
- C. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
- D. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
Answer: C
NEW QUESTION # 197
A Machine Learning Specialist has built a model using Amazon SageMaker built-in algorithms and is not getting expected accurate results The Specialist wants to use hyperparameter optimization to increase the model's accuracy Which method is the MOST repeatable and requires the LEAST amount of effort to achieve this?
- A. Launch multiple training jobs in parallel with different hyperparameters
- B. Create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter
- C. Create a hyperparameter tuning job and set the accuracy as an objective metric.
- D. Create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters
Answer: C
Explanation:
A hyperparameter tuning job is a feature of Amazon SageMaker that allows automatically finding the best combination of hyperparameters for a machine learning model. Hyperparameters are high-level parameters that influence the learning process and the performance of the model, such as the learning rate, the number of layers, the regularization factor, etc. A hyperparameter tuning job works by launching multiple training jobs with different hyperparameters, evaluating the results using an objective metric, and choosing the next set of hyperparameters to try based on a search strategy. The objective metric is a measure of the quality of the model, such as accuracy, precision, recall, etc. The search strategy is a method of exploring the hyperparameter space, such as random search, grid search, or Bayesian optimization.
Among the four options, option C is the most repeatable and requires the least amount of effort to use hyperparameter optimization to increase the model's accuracy. This option involves the following steps:
Create a hyperparameter tuning job: Amazon SageMaker provides an easy-to-use interface for creating a hyperparameter tuning job, either through the AWS Management Console, the AWS CLI, or the AWS SDKs. To create a hyperparameter tuning job, the Machine Learning Specialist needs to specify the following information:
The name and type of the algorithm to use, either a built-in algorithm or a custom algorithm.
The ranges and types of the hyperparameters to tune, such as categorical, continuous, or integer.
The name and type of the objective metric to optimize, such as accuracy, and whether to maximize or minimize it.
The resource limits for the tuning job, such as the maximum number of training jobs and the maximum parallel training jobs.
The input data channels and the output data location for the training jobs.
The configuration of the training instances, such as the instance type, the instance count, the volume size, etc.
Set the accuracy as an objective metric: To use accuracy as an objective metric, the Machine Learning Specialist needs to ensure that the training algorithm writes the accuracy value to a file called metric_definitions in JSON format and prints it to stdout or stderr. For example, the file can contain the following content:
This means that the training algorithm prints a line like this:
Amazon SageMaker reads the accuracy value from the line and uses it to evaluate and compare the training jobs.
The other options are not as repeatable and require more effort than option C for the following reasons:
Option A: This option requires manually launching multiple training jobs in parallel with different hyperparameters, which can be tedious and error-prone. It also requires manually monitoring and comparing the results of the training jobs, which can be time-consuming and subjective.
Option B: This option requires writing code to create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters, which can be complex and challenging. It also requires maintaining and updating the list of hyperparameters, which can be inefficient and suboptimal.
Option D: This option requires writing code to create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter, which can be unreliable and unpredictable. It also requires defining and implementing a stopping criterion, which can be arbitrary and inconsistent.
References:
Automatic Model Tuning - Amazon SageMaker
Define Metrics to Monitor Model Performance
NEW QUESTION # 198
A machine learning specialist is developing a proof of concept for government users whose primary concern is security. The specialist is using Amazon SageMaker to train a convolutional neural network (CNN) model for a photo classifier application. The specialist wants to protect the data so that it cannot be accessed and transferred to a remote host by malicious code accidentally installed on the training container.
Which action will provide the MOST secure protection?
- A. Encrypt the training and validation dataset.
- B. Encrypt the weights of the CNN model.
- C. Remove Amazon S3 access permissions from the SageMaker execution role.
- D. Enable network isolation for training jobs.
Answer: D
Explanation:
Explanation
The most secure action to protect the data from being accessed and transferred to a remote host by malicious code accidentally installed on the training container is to enable network isolation for training jobs. Network isolation is a feature that allows you to run training and inference containers in internet-free mode, which blocks any outbound network calls from the containers, even to other AWS services such as Amazon S3.
Additionally, no AWS credentials are made available to the container runtime environment. This way, you can prevent unauthorized access to your data and resources by malicious code or users. You can enable network isolation by setting the EnableNetworkIsolation parameter to True when you call CreateTrainingJob, CreateHyperParameterTuningJob, or CreateModel.
References:
Run Training and Inference Containers in Internet-Free Mode - Amazon SageMaker
NEW QUESTION # 199
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