Questions AWS-Certified-Machine-Learning-Specialty Exam, AWS-Certified-Machine-Learning-Specialty Valid Test Dumps
Questions AWS-Certified-Machine-Learning-Specialty Exam, AWS-Certified-Machine-Learning-Specialty Valid Test Dumps
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By taking a AWS-Certified-Machine-Learning-Specialty practice exam, you can find out what you're good at. AWS-Certified-Machine-Learning-Specialty exam preparation software is the best way to prepare for your AWS-Certified-Machine-Learning-Specialty certification exam. With the AWS-Certified-Machine-Learning-Specialty list of questions, you can brush up on your skills and knowledge. With PassCollection, you'll access a lot of AWS-Certified-Machine-Learning-Specialty Practice Questions, detailed explanations, and personalized feedback. And because it's all online, you can study anywhere, anytime. The AWS Certified Machine Learning - Specialty (AWS-Certified-Machine-Learning-Specialty) practice exam consists of questions from a pool of questions.
Amazon AWS Certified Machine Learning Specialty Exam is intended for individuals who have a strong understanding of data analytics and machine learning, as well as experience working with AWS architectures. AWS-Certified-Machine-Learning-Specialty exam requires knowledge of how to navigate AWS services and tools, including Amazon Sagemaker, Amazon Comprehend, and Amazon Rekognition. Candidates should also have experience using programming languages like Python and have a deep understanding of the fundamentals of machine learning.
Amazon MLS-C01 exam covers a wide range of topics, including data preparation, feature engineering, model training, evaluation and deployment, and machine learning implementation and operations. AWS-Certified-Machine-Learning-Specialty Exam also covers AWS services such as Amazon SageMaker, AWS Deep Learning AMIs, Amazon S3, Amazon EMR, and Amazon Redshift. AWS-Certified-Machine-Learning-Specialty exam is designed to assess the candidate's ability to use these services to build, train, and deploy ML models.
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Excel in the Certification Exam With Real Amazon AWS-Certified-Machine-Learning-Specialty Questions
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The AWS Certified Machine Learning - Specialty Exam is a rigorous and comprehensive certification program that requires a significant amount of preparation and study. To prepare for the exam, candidates should have a strong foundation in mathematics, statistics, and programming, as well as experience with machine learning frameworks such as TensorFlow and PyTorch. Additionally, candidates should be familiar with the AWS platform and its various services and tools, including Amazon SageMaker, Amazon EMR, and Amazon Rekognition. Overall, the AWS Certified Machine Learning - Specialty Exam is an excellent opportunity for individuals to demonstrate their expertise and advance their careers in the field of machine learning.
Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q100-Q105):
NEW QUESTION # 100
A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet.
Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)
- A. Modify the ACL on the endpoint network interface to restrict access to the instances.
- B. Add a SageMaker Runtime VPC endpoint interface to the VPC.
- C. Modify the security group on the endpoint network interface to restrict access to the instances.
- D. Add a VPC endpoint policy to allow access to the IAM users.
- E. Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.
Answer: B,C
Explanation:
To secure calls to the Amazon SageMaker Service API, the ML specialist should take the following steps:
Modify the security group on the endpoint network interface to restrict access to the instances. This will allow the ML specialist to control which instances in the VPC can communicate with the VPC interface endpoint for the Amazon SageMaker Service API. The security group can specify inbound and outbound rules based on the instance IDs, IP addresses, or CIDR blocks1.
Add a SageMaker Runtime VPC endpoint interface to the VPC. This will allow the ML specialist to invoke the SageMaker endpoints from within the VPC without using the public internet. The SageMaker Runtime VPC endpoint interface connects the VPC directly to the SageMaker Runtime using AWS PrivateLink2.
The other options are not as effective or necessary as the steps above. Adding a VPC endpoint policy to allow access to the IAM users is not required, as the IAM users can already access the Amazon SageMaker Service API through the VPC interface endpoint. Modifying the users' IAM policy to allow access to Amazon SageMaker Service API calls only is not sufficient, as it does not prevent unauthorized instances from accessing the VPC interface endpoint. Modifying the ACL on the endpoint network interface to restrict access to the instances is not possible, as network ACLs are associated with subnets, not network interfaces3.
References:
Security groups for your VPC - Amazon Virtual Private Cloud
Connect to SageMaker Within your VPC - Amazon SageMaker
Network ACLs - Amazon Virtual Private Cloud
NEW QUESTION # 101
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset Which tool should be used to improve the validation accuracy?
- A. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers
- B. Amazon Comprehend syntax analysts and entity detection
- C. Natural Language Toolkit (NLTK) stemming and stop word removal
- D. Amazon SageMaker BlazingText allow mode
Answer: A
Explanation:
Explanation
Term frequency-inverse document frequency (TF-IDF) is a technique that assigns a weight to each word in a document based on how important it is to the meaning of the document. The term frequency (TF) measures how often a word appears in a document, while the inverse document frequency (IDF) measures how rare a word is across a collection of documents. The TF-IDF weight is the product of the TF and IDF values, and it is high for words that are frequent in a specific document but rare in the overall corpus. TF-IDF can help improve the validation accuracy of a sentiment analysis model by reducing the impact of common words that have little or no sentiment value, such as "the", "a", "and", etc. Scikit-learn is a popular Python library for machine learning that provides a TF-IDF vectorizer class that can transform a collection of text documents into a matrix of TF-IDF features. By using this tool, the Data Scientist can create a more informative and discriminative feature representation for the sentiment analysis task.
References:
TfidfVectorizer - scikit-learn
Text feature extraction - scikit-learn
TF-IDF for Beginners | by Jana Schmidt | Towards Data Science
Sentiment Analysis: Concept, Analysis and Applications | by Susan Li | Towards Data Science
NEW QUESTION # 102
A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last
24 hours of the data
Which type of data repository is the MOST cost-effective solution?
- A. An Amazon RDS database with hourly table partitions
- B. An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes
- C. An Amazon S3 data lake with hourly object prefixes
- D. An Amazon EBS-backed Amazon EC2 instance with hourly directories
Answer: C
Explanation:
Explanation
An Amazon S3 data lake is a cost-effective solution for storing and analyzing large amounts of time-based training data for a new model. Amazon S3 is a highly scalable, durable, and secure object storage service that can store any amount of data in any format. Amazon S3 also offers low-cost storage classes, such as S3 Standard-IA and S3 One Zone-IA, that can reduce the storage costs for infrequently accessed data. By using hourly object prefixes, the Machine Learning Specialist can organize the data into logical partitions based on the time of ingestion. This can enable efficient data access and management, as well as support incremental updates and deletes. The Specialist can also use Amazon S3 lifecycle policies to automatically transition the data to lower-cost storage classes or delete the data after a certain period of time. This way, the Specialist can always train on the last 24 hours of the data and optimize the storage costs.
References:
What is a data lake? - Amazon Web Services
Amazon S3 Storage Classes - Amazon Simple Storage Service
Managing your storage lifecycle - Amazon Simple Storage Service
Best Practices Design Patterns: Optimizing Amazon S3 Performance
NEW QUESTION # 103
A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket.
A Machine Learning Specialist wants to use SQL to run queries on this data.
Which solution requires the LEAST effort to be able to query this data?
- A. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.
- B. Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
- C. Use AWS Glue to catalogue the data and Amazon Athena to run queries.
- D. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
Answer: C
NEW QUESTION # 104
An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images Which of the following should be used to resolve this issue? (Select TWO)
- A. Add L2 regularization to the model
- B. Use gradient checking in the model
- C. Make the neural network architecture complex.
- D. Perform data augmentation on the training data
- E. Add vanishing gradient to the model
Answer: D,E
NEW QUESTION # 105
......
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