AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY MOCK EXAMS & AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY DISCOUNT

AWS-Certified-Machine-Learning-Specialty Mock Exams & AWS-Certified-Machine-Learning-Specialty Discount

AWS-Certified-Machine-Learning-Specialty Mock Exams & AWS-Certified-Machine-Learning-Specialty Discount

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To be eligible for AWS-Certified-Machine-Learning-Specialty exam, candidates are expected to have a strong understanding of basic machine learning concepts, programming skills, and experience using AWS services. AWS Certified Machine Learning - Specialty certification is ideal for data scientists, software developers, and IT professionals who want to add machine learning skills to their portfolio and stay competitive in the job market.

The Amazon AWS-Certified-Machine-Learning-Specialty Exam covers a wide range of topics such as data preparation, feature engineering, modeling, evaluation, and deployment. The candidate is expected to have a deep understanding of the AWS infrastructure and services related to machine learning, such as Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, and Amazon Lex, among others.

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Hot AWS-Certified-Machine-Learning-Specialty Mock Exams | Latest AWS-Certified-Machine-Learning-Specialty Discount: AWS Certified Machine Learning - Specialty 100% Pass

This kind of polished approach is beneficial for a commendable grade in the AWS Certified Machine Learning - Specialty (AWS-Certified-Machine-Learning-Specialty) exam. While attempting the exam, take heed of the clock ticking, so that you manage the Amazon AWS-Certified-Machine-Learning-Specialty questions in a time-efficient way. Even if you are completely sure of the correct answer to a question, first eliminate the incorrect ones, so that you may prevent blunders due to human error.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q178-Q183):

NEW QUESTION # 178
A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population.
Which cross-validation strategy should the Data Scientist adopt?

  • A. A k-fold cross-validation strategy with k=5 and 3 repeats
  • B. A stratified k-fold cross-validation strategy with k=5
  • C. A k-fold cross-validation strategy with k=5
  • D. An 80/20 stratified split between training and validation

Answer: B

Explanation:
A stratified k-fold cross-validation strategy is a technique that preserves the class distribution in each fold.
This is important for imbalanced datasets, such as the one in the question, where the disease is seen in only
3% of the population. If a random k-fold cross-validation strategy is used, some folds may have no positive cases or very few, which would lead to poor estimates of the model performance. A stratified k-fold cross- validation strategy ensures that each fold has the same proportion of positive and negative cases as the whole dataset, which makes the evaluation more reliable and robust. A k-fold cross-validation strategy with k=5 and
3 repeats is also a possible option, but it is more computationally expensive and may not be necessary if the stratification is done properly. An 80/20 stratified split between training and validation is another option, but it uses less data for training and validation than k-fold cross-validation, which may result in higher variance and lower accuracy of the estimates. References:
* AWS Machine Learning Specialty Certification Exam Guide
* AWS Machine Learning Training: Model Evaluation
* How to Fix k-Fold Cross-Validation for Imbalanced Classification


NEW QUESTION # 179
A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings.
The company stores audit documents in text format. Auditors have requested help from a data science team to quickly analyze the documents. The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members. Documents that describe adverse events must receive the highest priority.
A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic.
Which algorithms are best suited to this scenario? (Choose two.)

  • A. Linear support vector machine
  • B. Neural topic modeling (NTM)
  • C. Linear regression
  • D. Latent Dirichlet allocation (LDA)
  • E. Random Forest classifier

Answer: B,D

Explanation:
Explanation
The algorithms that are best suited to this scenario are latent Dirichlet allocation (LDA) and neural topic modeling (NTM), as they are both unsupervised learning methods that can discover abstract topics from a collection of text documents. LDA and NTM can provide a list of the top words for each topic, as well as the topic distribution for each document, which can help the auditors assess the relevance and priority of the topic12.
The other options are not suitable because:
Option B: A random forest classifier is a supervised learning method that can perform classification or regression tasks by using an ensemble of decision trees. A random forest classifier is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes3.
Option D: A linear support vector machine is a supervised learning method that can perform classification or regression tasks by using a linear function that separates the data into different classes. A linear support vector machine is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes4.
Option E: A linear regression is a supervised learning method that can perform regression tasks by using a linear function that models the relationship between a dependent variable and one or more independent variables. A linear regression is not suitable for discovering abstract topics from text documents, as it requires labeled data and a continuous output variable5.
References:
1: Latent Dirichlet Allocation
2: Neural Topic Modeling
3: Random Forest Classifier
4: Linear Support Vector Machine
5: Linear Regression


NEW QUESTION # 180
A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test dat a. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.

Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

  • A. Increase the max_depth parameter value.
  • B. Lower the max_depth parameter value.
  • C. Update the objective to binary:logistic.
  • D. Lower the min_child_weight parameter value.

Answer: B

Explanation:
Overfitting occurs when a model performs well on the training data but poorly on the test data. This is often because the model has learned the training data too well and is not able to generalize to new data. To avoid overfitting, the Machine Learning Specialist should lower the max_depth parameter value. This will reduce the complexity of the model and make it less likely to overfit. According to the XGBoost documentation1, the max_depth parameter controls the maximum depth of a tree and lower values can help prevent overfitting. The documentation also suggests other ways to control overfitting, such as adding randomness, using regularization, and using early stopping1. References:
XGBoost Parameters


NEW QUESTION # 181
A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

  • A. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
  • B. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
  • C. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert copyright card numbers.
  • D. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

Answer: D

Explanation:
AWS KMS is a service that provides encryption and key management for data stored in AWS services and applications. AWS KMS can generate and manage encryption keys that are used to encrypt and decrypt data at rest and in transit. AWS KMS can also integrate with other AWS services, such as Amazon S3 and Amazon SageMaker, to enable encryption of data using the keys stored in AWS KMS. Amazon S3 is a service that provides object storage for data in the cloud. Amazon S3 can use AWS KMS to encrypt data at rest using server-side encryption with AWS KMS-managed keys (SSE-KMS). Amazon SageMaker is a service that provides a platform for building, training, and deploying machine learning models. Amazon SageMaker can use AWS KMS to encrypt data at rest on the SageMaker instances and volumes, as well as data in transit between SageMaker and other AWS services. AWS Glue is a service that provides a serverless data integration platform for data preparation and transformation. AWS Glue can use AWS KMS to encrypt data at rest on the Glue Data Catalog and Glue ETL jobs. AWS Glue can also use built-in or custom classifiers to identify and redact sensitive data, such as credit card numbers, from the customer data1234 The other options are not valid or secure ways to encrypt the data and protect the credit card information. Using a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC is not a good practice, as custom encryption algorithms are not recommended for security and may have flaws or vulnerabilities. Using the SageMaker DeepAR algorithm to randomize the credit card numbers is not a good practice, as DeepAR is a forecasting algorithm that is not designed for data anonymization or encryption. Using an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert copyright card numbers is not a good practice, as IAM policies are not meant for data encryption, but for access control and authorization. Amazon Kinesis is a service that provides real-time data streaming and processing, but it does not have the capability to automatically discard or insert data values. Using an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC is not a good practice, as launch configurations are not meant for data encryption, but for specifying the instance type, security group, and user data for the SageMaker instance. Using the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers is not a good practice, as PCA is a dimensionality reduction algorithm that is not designed for data anonymization or encryption.


NEW QUESTION # 182
A company will use Amazon SageMaker to train and host a machine learning (ML) model for a marketing campaign. The majority of data is sensitive customer dat a. The data must be encrypted at rest. The company wants AWS to maintain the root of trust for the master keys and wants encryption key usage to be logged.
Which implementation will meet these requirements?

  • A. Use AWS Security Token Service (AWS STS) to create temporary tokens to encrypt the ML storage volumes, and to encrypt the model artifacts and data in Amazon S3.
  • B. Use customer managed keys in AWS Key Management Service (AWS KMS) to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.
  • C. Use SageMaker built-in transient keys to encrypt the ML data volumes. Enable default encryption for new Amazon Elastic Block Store (Amazon EBS) volumes.
  • D. Use encryption keys that are stored in AWS Cloud HSM to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.

Answer: B


NEW QUESTION # 183
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