CUSTOMIZED MLS-C01 LAB SIMULATION & UPDATED MLS-C01 DEMO

Customized MLS-C01 Lab Simulation & Updated MLS-C01 Demo

Customized MLS-C01 Lab Simulation & Updated MLS-C01 Demo

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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam designed for individuals who are seeking to validate their skills and knowledge in machine learning on the Amazon Web Services (AWS) platform. MLS-C01 exam is aimed at professionals who have experience in designing, implementing, deploying, and maintaining machine learning solutions using AWS services.

Achieving the Amazon MLS-C01 certification demonstrates the candidate's ability to design and implement machine learning solutions on AWS, which is highly valued by employers and clients. AWS Certified Machine Learning - Specialty certification provides an opportunity for professionals to showcase their expertise in machine learning and advance their careers in this rapidly growing field.

Amazon MLS-C01 Exam is designed for individuals who are interested in becoming AWS Certified Machine Learning Specialists. AWS Certified Machine Learning - Specialty certification validates the candidate's ability to design, implement, deploy, and maintain machine learning (ML) solutions for a variety of business applications. MLS-C01 exam covers a broad range of topics, including data preparation, feature engineering, model selection and evaluation, and deployment strategies.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q143-Q148):

NEW QUESTION # 143
An ecommerce company has observed that customers who use the company's website rarely view items that the website recommends to customers. The company wants to recommend items to customers that customers are more likely to want to purchase.
Which solution will meet this requirement in the SHORTEST amount of time?

  • A. Host the company's website on Amazon EC2 Accelerated Computing instances to increase the website response speed.
  • B. Integrate Amazon Personalize into the company's website to provide customers with personalized recommendations.
  • C. Use Amazon SageMaker to train a Neural Collaborative Filtering (NCF) model to make product recommendations.
  • D. Host the company's website on Amazon EC2 GPU-based instances to increase the speed of the website's search tool.

Answer: B

Explanation:
Amazon Personalize is a managed AWS service specifically designed to deliver personalized recommendations with minimal development time. It uses machine learning algorithms tailored for recommendation systems, making it highly suitable for applications where quick integration is essential. By using Amazon Personalize, the company can leverage existing customer data to generate real-time, personalized product recommendations that align better with customer preferences, enhancing the likelihood of customer engagement with recommended items.
Options involving EC2 instances with GPU or accelerated computing primarily enhance computational performance but do not inherently improve recommendation relevance, while Amazon SageMaker would require more development effort to achieve similar results.


NEW QUESTION # 144
A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among
200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.
What type of machine learning model should be used?

  • A. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
  • B. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
  • C. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
  • D. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.

Answer: A

Explanation:
Explanation
Forecasting is a type of machine learning model that predicts future values of a target variable based on historical data and other features. Forecasting is suitable for problems that involve time-series data, such as the number of claims in each category from month to month. Forecasting can handle multiple categories of the target variable, as well as missing or partial information on some features. Therefore, option C is the best choice for the given problem.
Option A is incorrect because classification is a type of machine learning model that assigns a label to an input based on predefined categories. Classification is not suitable for predicting continuous or numerical values, such as the number of claims in each category from month to month. Moreover, classification requires sufficient and complete information on the features that are relevant to the target variable, which is not the case for the given problem. Option B is incorrect because reinforcement learning is a type of machine learning model that learns from its own actions and rewards in an interactive environment. Reinforcement learning is not suitable for problems that involve historical data and do not require an agent to take actions. Option D is incorrect because it combines two different types of machine learning models, which is unnecessary and inefficient. Moreover, classification is not suitable for predicting the number of claims in some categories, as explained in option A.
References:
Forecasting | AWS Solutions for Machine Learning (AI/ML) | AWS Solutions Library Time Series Forecasting Service - Amazon Forecast - Amazon Web Services Amazon Forecast: Guide to Predicting Future Outcomes - Onica Amazon Launches What-If Analyses for Machine Learning Forecasting ...


NEW QUESTION # 145
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
Which model will meet the business requirement?

  • A. Logistic regression
  • B. Principal component analysis (PCA)
  • C. K-means
  • D. Linear regression

Answer: D

Explanation:
Explanation
The best model for predicting housing prices based on a historical dataset with 32 features is linear regression.
Linear regression is a supervised learning algorithm that fits a linear relationship between a dependent variable (housing price) and one or more independent variables (features). Linear regression can handle multiple features and output a continuous value for the housing price. Linear regression can also return the coefficients of the features, which indicate how each feature affects the housing price. Linear regression is suitable for this problem because the outcome of interest is numerical and continuous, and the model needs to capture the linear relationship between the features and the outcome.
References:
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Regression vs Classification in Machine Learning AWS Machine Learning Training - Linear Regression with Amazon SageMaker


NEW QUESTION # 146
A Machine Learning Specialist is working for an online retailer that wants to run analytics on every customer visit, processed through a machine learning pipeline. The data needs to be ingested by Amazon Kinesis Data Streams at up to 100 transactions per second, and the JSON data blob is 100 KB in size.
What is the MINIMUM number of shards in Kinesis Data Streams the Specialist should use to successfully ingest this data?

  • A. 10 shards
  • B. 1,000 shards
  • C. 1 shards
  • D. 100 shards

Answer: C

Explanation:
According to the Amazon Kinesis Data Streams documentation, the maximum size of data blob (the data payload before Base64-encoding) per record is 1 MB. The maximum number of records that can be sent to a shard per second is 1,000. Therefore, the maximum throughput of a shard is 1 MB/sec for input and 2 MB/sec for output. In this case, the input throughput is 100 transactions per second * 100 KB per transaction = 10 MB/sec. Therefore, the minimum number of shards required is 10 MB/sec / 1 MB/sec = 10 shards. However, the question asks for the minimum number of shards in Kinesis Data Streams, not the minimum number of shards per stream. A Kinesis Data Streams account can have multiple streams, each with its own number of shards. Therefore, the minimum number of shards in Kinesis Data Streams is 1, which is the minimum number of shards per stream. References:
Amazon Kinesis Data Streams Terminology and Concepts
Amazon Kinesis Data Streams Limits


NEW QUESTION # 147
During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates What is the MOST likely cause of this issue?

  • A. The class distribution in the dataset is imbalanced
  • B. Dataset shuffling is disabled
  • C. The learning rate is very high
  • D. The batch size is too big

Answer: A


NEW QUESTION # 148
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