MLS-C01 STUDY TOOL - LATEST MLS-C01 EXAM FEE

MLS-C01 Study Tool - Latest MLS-C01 Exam Fee

MLS-C01 Study Tool - Latest MLS-C01 Exam Fee

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2025 Amazon Marvelous MLS-C01: AWS Certified Machine Learning - Specialty Study Tool

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q180-Q185):

NEW QUESTION # 180
This graph shows the training and validation loss against the epochs for a neural network The network being trained is as follows
* Two dense layers one output neuron
* 100 neurons in each layer
* 100 epochs
* Random initialization of weights
Which technique can be used to improve model performance in terms of accuracy in the validation set?

  • A. Increasing the number of epochs
  • B. Early stopping
  • C. Random initialization of weights with appropriate seed
  • D. Adding another layer with the 100 neurons

Answer: B

Explanation:
Early stopping is a technique that can be used to prevent overfitting and improve model performance on the validation set. Overfitting occurs when the model learns the training data too well and fails to generalize to new and unseen data. This can be seen in the graph, where the training loss keeps decreasing, but the validation loss starts to increase after some point. This means that the model is fitting the noise and patterns in the training data that are not relevant for the validation data. Early stopping is a way of stopping the training process before the model overfits the training data. It works by monitoring the validation loss and stopping the training when the validation loss stops decreasing or starts increasing. This way, the model is saved at the point where it has the best performance on the validation set. Early stopping can also save time and resources by reducing the number of epochs needed for training. References:
* Early Stopping
* How to Stop Training Deep Neural Networks At the Right Time Using Early Stopping


NEW QUESTION # 181
A Data Scientist is working on optimizing a model during the training process by varying multiple parameters. The Data Scientist observes that, during multiple runs with identical parameters, the loss function converges to different, yet stable, values.
What should the Data Scientist do to improve the training process?

  • A. Increase the learning rate. Keep the batch size the same.
  • B. Do not change the learning rate. Increase the batch size.
  • C. Reduce the batch size. Decrease the learning rate.
  • D. Keep the batch size the same. Decrease the learning rate.

Answer: C

Explanation:
It is most likely that the loss function is very curvy and has multiple local minima where the training is getting stuck. Decreasing the batch size would help the Data Scientist stochastically get out of the local minima saddles. Decreasing the learning rate would prevent overshooting the global loss function minimum.


NEW QUESTION # 182
An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

  • A. Listwise deletion
  • B. Multiple imputation
  • C. Last observation carried forward
  • D. Mean substitution

Answer: B

Explanation:
Multiple imputation is a technique that uses machine learning to generate multiple plausible values for each missing value in a dataset, based on the observed data and the relationships among the variables. Multiple imputation preserves the integrity of the dataset by accounting for the uncertainty and variability of the missing data, and avoids the bias and loss of information that may result from other methods, such as listwise deletion, last observation carried forward, or mean substitution. Multiple imputation can improve the accuracy and validity of statistical analysis and machine learning models that use the imputed dataset. References:
* Managing missing values in your target and related datasets with automated imputation support in Amazon Forecast
* Imputation by feature importance (IBFI): A methodology to impute missing data in large datasets
* Multiple Imputation by Chained Equations (MICE) Explained


NEW QUESTION # 183
A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested The company also wants be able to save the results in its data lake for later processing and analysis What is the MOST efficient way to accomplish these tasks'?

  • A. Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.
  • B. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data
  • C. Ingest the data into Apache Spark Streaming using Amazon EMR. and use Spark MLlib with k-means to perform anomaly detection Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake
  • D. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection Then use Kinesis Data Firehose to stream the results to Amazon S3

Answer: B


NEW QUESTION # 184
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. 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.
  • C. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
  • D. Classification month-to-month using supervised learning of the 200 categories based on claim contents.

Answer: A

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 # 185
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