DUMPS MLS-C01 DOWNLOAD - FREE MLS-C01 BRAIN DUMPS

Dumps MLS-C01 Download - Free MLS-C01 Brain Dumps

Dumps MLS-C01 Download - Free MLS-C01 Brain Dumps

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q27-Q32):

NEW QUESTION # 27
An agricultural company is interested in using machine learning to detect specific types of weeds in a 100-acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10 * 10 grids. The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks.
The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints. The model will perform real-time inferencing using the images captured by the cameras.
Which approach should a Machine Learning Specialist take to obtain accurate predictions?

  • A. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.
  • B. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.
  • C. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
  • D. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.

Answer: A


NEW QUESTION # 28
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.
What technique should be used to convert this column to binary values.

  • A. One-hot encoding
  • B. Tokenization
  • C. Binarization
  • D. Normalization transformation

Answer: A


NEW QUESTION # 29
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information which model would have the HIGHEST accuracy?

  • A. Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))
  • B. Support vector machine (SVM) with non-linear kernel
  • C. Logistic regression
  • D. Single perceptron with tanh activation function

Answer: C


NEW QUESTION # 30
A global financial company is using machine learning to automate its loan approval process. The company has a dataset of customer information. The dataset contains some categorical fields, such as customer location by city and housing status. The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents.
The company's data scientists are using a gradient boosting regression model to infer the credit score for each customer. The model has a training accuracy of 99% and a testing accuracy of 75%. The data scientists want to improve the model's testing accuracy.
Which process will improve the testing accuracy the MOST?

  • A. Use tokenization of the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Remove the outliers in the data by using the z-score.
  • B. Use a one-hot encoder for the categorical fields in the dataset. Perform standardization on the financial fields in the dataset. Apply L1 regularization to the data.
  • C. Use a label encoder for the categorical fields in the dataset. Perform L1 regularization on the financial fields in the dataset. Apply L2 regularization to the data.
  • D. Use a logarithm transformation on the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Use imputation to populate missing values in the dataset.

Answer: B

Explanation:
The question is about improving the testing accuracy of a gradient boosting regression model. The testing accuracy is much lower than the training accuracy, which indicates that the model is overfitting the training data. To reduce overfitting, the following steps are recommended:
Use a one-hot encoder for the categorical fields in the dataset. This will create binary features for each category and avoid imposing an ordinal relationship among them. This can help the model learn the patterns better and generalize to unseen data.
Perform standardization on the financial fields in the dataset. This will scale the features to have zero mean and unit variance, which can improve the convergence and performance of the model. This can also help the model handle features with different units and ranges.
Apply L1 regularization to the data. This will add a penalty term to the loss function that is proportional to the absolute value of the coefficients. This can help the model reduce the complexity and select the most relevant features by shrinking the coefficients of less important features to zero.
References:
1: AWS Machine Learning Specialty Exam Guide
2: AWS Machine Learning Specialty Course
3: AWS Machine Learning Blog


NEW QUESTION # 31
A company wants to predict stock market price trends. The company stores stock market data each business day in Amazon S3 in Apache Parquet format. The company stores 20 GB of data each day for each stock code.
A data engineer must use Apache Spark to perform batch preprocessing data transformations quickly so the company can complete prediction jobs before the stock market opens the next day. The company plans to track more stock market codes and needs a way to scale the preprocessing data transformations.
Which AWS service or feature will meet these requirements with the LEAST development effort over time?

  • A. AWS Lambda
  • B. AWS Glue jobs
  • C. Amazon Athena
  • D. Amazon EMR cluster

Answer: B

Explanation:
AWS Glue jobs is the AWS service or feature that will meet the requirements with the least development effort over time. AWS Glue jobs is a fully managed service that enables data engineers to run Apache Spark applications on a serverless Spark environment. AWS Glue jobs can perform batch preprocessing data transformations on large datasets stored in Amazon S3, such as converting data formats, filtering data, joining data, and aggregating dat a. AWS Glue jobs can also scale the Spark environment automatically based on the data volume and processing needs, without requiring any infrastructure provisioning or management. AWS Glue jobs can reduce the development effort and time by providing a graphical interface to create and monitor Spark applications, as well as a code generation feature that can generate Scala or Python code based on the data sources and targets. AWS Glue jobs can also integrate with other AWS services, such as Amazon Athena, Amazon EMR, and Amazon SageMaker, to enable further data analysis and machine learning tasks1.
The other options are either more complex or less scalable than AWS Glue jobs. Amazon EMR cluster is a managed service that enables data engineers to run Apache Spark applications on a cluster of Amazon EC2 instances. However, Amazon EMR cluster requires more development effort and time than AWS Glue jobs, as it involves setting up, configuring, and managing the cluster, as well as writing and deploying the Spark code. Amazon EMR cluster also does not scale automatically, but requires manual or scheduled resizing of the cluster based on the data volume and processing needs2. Amazon Athena is a serverless interactive query service that enables data engineers to analyze data stored in Amazon S3 using standard SQL. However, Amazon Athena is not suitable for performing complex data transformations, such as joining data from multiple sources, aggregating data, or applying custom logic. Amazon Athena is also not designed for running Spark applications, but only supports SQL queries3. AWS Lambda is a serverless compute service that enables data engineers to run code without provisioning or managing servers. However, AWS Lambda is not optimized for running Spark applications, as it has limitations on the execution time, memory size, and concurrency of the functions. AWS Lambda is also not integrated with Amazon S3, and requires additional steps to read and write data from S3 buckets.
References:
1: AWS Glue - Fully Managed ETL Service - Amazon Web Services
2: Amazon EMR - Amazon Web Services
3: Amazon Athena - Interactive SQL Queries for Data in Amazon S3
[4]: AWS Lambda - Serverless Compute - Amazon Web Services


NEW QUESTION # 32
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