100% PASS 2025 AMAZON MLS-C01 UPDATED NEW BRAINDUMPS EBOOK

100% Pass 2025 Amazon MLS-C01 Updated New Braindumps Ebook

100% Pass 2025 Amazon MLS-C01 Updated New Braindumps Ebook

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The AWS Certified Machine Learning - Specialty exam (MLS-C01) is a certification offered by Amazon Web Services (AWS) for individuals who want to validate their expertise in machine learning on the AWS cloud. AWS Certified Machine Learning - Specialty certification is designed to validate a candidate's understanding of the core concepts and best practices of machine learning implementation on AWS, including data preparation and cleaning, feature engineering, model development, and deployment.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Certification Exam is a comprehensive and challenging test designed for individuals who want to demonstrate their expertise in machine learning on the Amazon Web Services (AWS) platform. MLS-C01 Exam validates your knowledge and skills in designing, implementing, and maintaining machine learning solutions using AWS services. AWS Certified Machine Learning - Specialty certification is suitable for professionals who have experience in data science, software engineering, and cloud computing.

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Topics in AWS Certified Machine Learning - Specialty

The following will be discussed in AMAZON MLS-C01 Practice Exam and AMAZON MLS-C01 practice exams:

  • Modeling
  • Data Engineering
  • Exploratory Data Analysis
  • Machine Learning Implementation and Operations

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q12-Q17):

NEW QUESTION # 12
A data science team is planning to build a natural language processing (NLP) application. The application's text preprocessing stage will include part-of-speech tagging and key phase extraction. The preprocessed text will be input to a custom classification algorithm that the data science team has already written and trained using Apache MXNet.
Which solution can the team build MOST quickly to meet these requirements?

  • A. Use Amazon Comprehend for the part-of-speech tagging, key phase extraction, and classification tasks.
  • B. Use an NLP library in Amazon SageMaker for the part-of-speech tagging. Use Amazon Comprehend for the key phase extraction. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.
  • C. Use Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.
  • D. Use Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use Amazon SageMaker built-in Latent Dirichlet Allocation (LDA) algorithm to build the custom classifier.

Answer: B


NEW QUESTION # 13
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?

  • A. Apply the Pearson correlation coefficient
  • B. Create a new feature space using principal component analysis (PCA)
  • C. Perform one-hot encoding on highly correlated features
  • D. Use matrix multiplication on highly correlated features.

Answer: D


NEW QUESTION # 14
A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

  • A. 0
  • B. 1
  • C. 2
  • D. 3

Answer: C


NEW QUESTION # 15
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. Amazon SageMaker BlazingText allow mode
  • D. Natural Language Toolkit (NLTK) stemming and stop word removal

Answer: A


NEW QUESTION # 16
A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

  • A. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
  • B. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
  • C. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.
  • D. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.

Answer: C

Explanation:
The best way to ensure that required packages are automatically available on the notebook instance for the data scientist to use is to create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance. A lifecycle configuration is a shell script that runs when you create or start a notebook instance. You can use a lifecycle configuration to customize the notebook instance by installing libraries, changing environment variables, or downloading datasets. You can also use a lifecycle configuration to automate the installation of custom Python packages that are not natively available on Amazon SageMaker.
Option A is incorrect because installing AWS Systems Manager Agent on the underlying Amazon EC2 instance and using Systems Manager Automation to execute the package installation commands is not a recommended way to customize the notebook instance. Systems Manager Automation is a feature that lets you safely automate common and repetitive IT operations and tasks across AWS resources. However, using Systems Manager Automation would require additional permissions and configurations, and it would not guarantee that the packages are installed before the notebook instance is ready to use.
Option B is incorrect because creating a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and placing the file under the /etc/init directory of each Amazon SageMaker notebook instance is not a valid way to customize the notebook instance. The /etc/init directory is used to store scripts that are executed during the boot process of the operating system, not the Jupyter notebook application. Moreover, a Jupyter notebook file is not a shell script that can be executed by the operating system.
Option C is incorrect because using the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook is not an automatic way to customize the notebook instance. This option would require the data scientist to manually run the conda commands every time they create or start a new notebook instance. This would not be efficient or convenient for the data scientist.
Customize a notebook instance using a lifecycle configuration script - Amazon SageMaker AWS Systems Manager Automation - AWS Systems Manager Conda environments - Amazon SageMaker


NEW QUESTION # 17
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