2025 REALISTIC PASS4SURE AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY DUMPS PDF - LATEST AWS CERTIFIED MACHINE LEARNING - SPECIALTY DUMPS FILES FREE PDF QUIZ

2025 Realistic Pass4sure AWS-Certified-Machine-Learning-Specialty Dumps Pdf - Latest AWS Certified Machine Learning - Specialty Dumps Files Free PDF Quiz

2025 Realistic Pass4sure AWS-Certified-Machine-Learning-Specialty Dumps Pdf - Latest AWS Certified Machine Learning - Specialty Dumps Files Free PDF Quiz

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The AWS Certified Machine Learning - Specialty certification exam, also known as MLS-C01, is designed to assess the knowledge and skills of professionals in the field of machine learning on the Amazon Web Services (AWS) platform. AWS Certified Machine Learning - Specialty certification exam is intended for individuals who have a deep understanding of machine learning concepts and algorithms, and are proficient in developing, training, and deploying machine learning models using AWS services.

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To take the Amazon MLS-C01 exam, candidates must have a strong background in machine learning concepts, programming languages such as Python, and experience with AWS services. AWS-Certified-Machine-Learning-Specialty Exam consists of multiple-choice and multiple-answer questions and is administered online. Candidates have 170 minutes to complete the exam and must achieve a passing score of 750 out of 1000 points.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q195-Q200):

NEW QUESTION # 195
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist has been asked to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)

  • A. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
  • B. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
  • C. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
  • D. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
  • E. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.

Answer: D,E


NEW QUESTION # 196
A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes Which function will produce the desired output?

  • A. Rectified linear units (ReLU)
  • B. Softmax
  • C. Smooth L1 loss
  • D. Dropout

Answer: B


NEW QUESTION # 197
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

  • A. The output path specifying where on an Amazon S3 bucket the trained model will persist.
  • B. The validation channel identifying the location of validation data on an Amazon S3 bucket.
  • C. Hyperparameters in a JSON array as documented for the algorithm used.
  • D. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
  • E. The training channel identifying the location of training data on an Amazon S3 bucket.
  • F. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.

Answer: A,D,E

Explanation:
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, the common parameters that must be specified are:
The training channel identifying the location of training data on an Amazon S3 bucket. This parameter tells SageMaker where to find the input data for the algorithm and what format it is in. For example, TrainingInputMode: File means that the input data is in files stored in S3.
The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users. This parameter grants SageMaker the necessary permissions to access the S3 buckets, ECR repositories, and other AWS resources needed for the training job. For example, RoleArn: arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-20200303T150948 means that SageMaker will use the specified role to run the training job.
The output path specifying where on an Amazon S3 bucket the trained model will persist. This parameter tells SageMaker where to save the model artifacts, such as the model weights and parameters, after the training job is completed. For example, OutputDataConfig: {S3OutputPath: s3://my-bucket/my-training-job} means that SageMaker will store the model artifacts in the specified S3 location.
The validation channel identifying the location of validation data on an Amazon S3 bucket is an optional parameter that can be used to provide a separate dataset for evaluating the model performance during the training process. This parameter is not required for all algorithms and can be omitted if the validation data is not available or not needed.
The hyperparameters in a JSON array as documented for the algorithm used is another optional parameter that can be used to customize the behavior and performance of the algorithm. This parameter is specific to each algorithm and can be used to tune the model accuracy, speed, complexity, and other aspects. For example, HyperParameters: {num_round: "10", objective: "binary:logistic"} means that the XGBoost algorithm will use 10 boosting rounds and the logistic loss function for binary classification.
The Amazon EC2 instance class specifying whether training will be run using CPU or GPU is not a parameter that is specified when submitting a training job using a built-in algorithm. Instead, this parameter is specified when creating a training instance, which is a containerized environment that runs the training code and algorithm. For example, ResourceConfig: {InstanceType: ml.m5.xlarge, InstanceCount: 1, VolumeSizeInGB: 10} means that SageMaker will use one m5.xlarge instance with 10 GB of storage for the training instance.
References:
Train a Model with Amazon SageMaker
Use Amazon SageMaker Built-in Algorithms or Pre-trained Models
CreateTrainingJob - Amazon SageMaker Service


NEW QUESTION # 198
A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?

  • A. Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.
  • B. Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.
  • C. Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.
  • D. Randomly select 10% of the users. Split off all interaction data from these users for the test set.

Answer: C

Explanation:
https://aws.amazon.com/blogs/machine-learning/building-a-customized-recommender-system-in-amazon-sagemaker/


NEW QUESTION # 199
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs What does the Specialist need to do1?

  • A. Organize the Docker container's file structure to execute on GPU instances.
  • B. Set the GPU flag in the Amazon SageMaker Create TrainingJob request body
  • C. Bundle the NVIDIA drivers with the Docker image
  • D. Build the Docker container to be NVIDIA-Docker compatible

Answer: C


NEW QUESTION # 200
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