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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is designed to test the knowledge and expertise of professionals in the field of machine learning. MLS-C01 exam is intended for individuals who have a solid understanding of machine learning concepts and practices and are looking to validate their skills and knowledge in this area. MLS-C01 exam is designed to test the candidate's ability to design, implement, and maintain machine learning solutions on the AWS platform.
What Is Audience for AWS Machine Learning Specialty Certification?
The AWS Certified Machine Learning Specialty certificate is intended for programmers, data scientists, and other candidates passionate about machine learning who want to learn how to use the benefits of artificial intelligence capabilities on the AWS platform. The training process necessary for obtaining this certification helps examinees develop the right skills to build, train, and deploy machine learning models using advanced AWS Cloud services. Candidates can achieve this certificate by obtaining the passing score in MLS-C01 Exam. Even though this test doesn't have any mandatory requirements, the vendor recommends that candidates should have previous knowledge of certain topics. A successful applicant is one who has between 1 to 2 years of practical experience in developing, running and architecting ML and deep learning workloads on the AWS Cloud. Also, it would be helpful if the candidate would have prior experience performing basic hyperparameter optimization and know how to follow model-training and operational best practices.
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Keeping in view different preparation styles of Amazon MLS-C01 test applicant PassExamDumps has designed three easy-to-use formats for its product. Each format has a pool of AWS Certified Machine Learning - Specialty (MLS-C01) actual questions which have been compiled under the guidance of thousands of professionals worldwide. Questions in this product will appear in the Amazon MLS-C01 final test.
Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q109-Q114):
NEW QUESTION # 109
A data scientist is training a text classification model by using the Amazon SageMaker built-in BlazingText algorithm. There are 5 classes in the dataset, with 300 samples for category A, 292 samples for category B,
240 samples for category C, 258 samples for category D, and 310 samples for category E.
The data scientist shuffles the data and splits off 10% for testing. After training the model, the data scientist generates confusion matrices for the training and test sets.
What could the data scientist conclude form these results?
Answer: A
Explanation:
A confusion matrix is a matrix that summarizes the performance of a machine learning model on a set of test data. It displays the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) produced by the model on the test data1. For multi-class classification, the matrix shape will be equal to the number of classes i.e for n classes it will be nXn1. The diagonal values represent the number of correct predictions for each class, and the off-diagonal values represent the number of incorrect predictions for each class1.
The BlazingText algorithm is a proprietary machine learning algorithm for forecasting time series using causal convolutional neural networks (CNNs). BlazingText works best with large datasets containing hundreds of time series. It accepts item metadata, and is the only Forecast algorithm that accepts related time series data without future values2.
From the confusion matrices for the training and test sets, we can observe the following:
* The model has a high accuracy on the training set, as most of the diagonal values are high and the off- diagonal values are low. This means that the model is able to learn the patterns and features of the training data well.
* However, the model has a lower accuracy on the test set, as some of the diagonal values are lower and some of the off-diagonal values are higher. This means that the model is not able to generalize well to the unseen data and makes more errors.
* The model has a particularly high error rate for classes B and E on the test set, as the values of M_22 and M_55 are much lower than the values of M_12, M_21, M_15, M_25, M_51, and M_52. This means that the model is confusing classes B and E with other classes more often than it should.
* The model has a relatively low error rate for classes A, C, and D on the test set, as the values of M_11, M_33, and M_44 are high and the values of M_13, M_14, M_23, M_24, M_31, M_32, M_34, M_41, M_42, and M_43 are low. This means that the model is able to distinguish classes A, C, and D from other classes well.
These results indicate that the model is overfitting for classes B and E, meaning that it is memorizing the specific features of these classes in the training data, but failing to capture the general features that are applicable to the test data. Overfitting is a common problem in machine learning, where the model performs well on the training data, but poorly on the test data3. Some possible causes of overfitting are:
* The model is too complex or has too many parameters for the given data. This makes the model flexible enough to fit the noise and outliers in the training data, but reduces its ability to generalize to new data.
* The data is too small or not representative of the population. This makes the model learn from a limited or biased sample of data, but fails to capture the variability and diversity of the population.
* The data is imbalanced or skewed. This makes the model learn from a disproportionate or uneven distribution of data, but fails to account for the minority or rare classes.
Some possible solutions to prevent or reduce overfitting are:
* Simplify the model or use regularization techniques. This reduces the complexity or the number of parameters of the model, and prevents it from fitting the noise and outliers in the data. Regularization techniques, such as L1 or L2 regularization, add a penalty term to the loss function of the model, which shrinks the weights of the model and reduces overfitting3.
* Increase the size or diversity of the data. This provides more information and examples for the model to learn from, and increases its ability to generalize to new data. Data augmentation techniques, such as rotation, flipping, cropping, or noise addition, can generate new data from the existing data by applying some transformations3.
* Balance or resample the data. This adjusts the distribution or the frequency of the data, and ensures that the model learns from all classes equally. Resampling techniques, such as oversampling or undersampling, can create a balanced dataset by increasing or decreasing the number of samples for each class3.
Confusion Matrix in Machine Learning - GeeksforGeeks
BlazingText algorithm - Amazon SageMaker
Overfitting and Underfitting in Machine Learning - GeeksforGeeks
NEW QUESTION # 110
A data engineer is preparing a dataset that a retail company will use to predict the number of visitors to stores.
The data engineer created an Amazon S3 bucket. The engineer subscribed the S3 bucket to an AWS Data Exchange data product for general economic indicators. The data engineer wants to join the economic indicator data to an existing table in Amazon Athena to merge with the business data. All these transformations must finish running in 30-60 minutes.
Which solution will meet these requirements MOST cost-effectively?
Answer: B
Explanation:
Explanation
The most cost-effective solution is to use an S3 event to trigger a Lambda function that uses SageMaker Data Wrangler to merge the data. This solution avoids the need to provision and manage any additional resources, such as Kinesis streams, Firehose delivery streams, Glue jobs, or Redshift clusters. SageMaker Data Wrangler provides a visual interface to import, prepare, transform, and analyze data from various sources, including AWS Data Exchange products. It can also export the data preparation workflow to a Python script that can be executed by a Lambda function. This solution can meet the time requirement of 30-60 minutes, depending on the size and complexity of the data.
References:
Using Amazon S3 Event Notifications
Prepare ML Data with Amazon SageMaker Data Wrangler
AWS Lambda Function
NEW QUESTION # 111
A logistics company needs a forecast model to predict next month's inventory requirements for a single item in
10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.
Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)
Answer: C,D
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/forecast/latest/dg/forecast.dg.pdf
NEW QUESTION # 112
A Machine Learning Specialist is building a supervised model that will evaluate customers' satisfaction with their mobile phone service based on recent usage The model's output should infer whether or not a customer is likely to switch to a competitor in the next 30 days Which of the following modeling techniques should the Specialist use1?
Answer: B
Explanation:
The modeling technique that the Machine Learning Specialist should use is binary classification. Binary classification is a type of supervised learning that predicts whether an input belongs to one of two possible classes. In this case, the input is the customer's recent usage data and the output is whether or not the customer is likely to switch to a competitor in the next 30 days. This is a binary outcome, either yes or no, so binary classification is suitable for this problem. The other options are not appropriate for this problem. Time-series prediction is a type of supervised learning that forecasts future values based on past and present data. Anomaly detection is a type of unsupervised learning that identifies outliers or abnormal patterns in the data. Regression is a type of supervised learning that estimates a continuous numerical value based on the input features. References: Binary Classification, Time Series Prediction, Anomaly Detection, Regression
NEW QUESTION # 113
A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream. As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.
Which next step is MOST likely to improve the data ingestion rate into Amazon S3?
Answer: B
Explanation:
The solution C is the most likely to improve the data ingestion rate into Amazon S3 because it increases the number of shards for the data stream. The number of shards determines the throughput capacity of the data stream, which affects the rate of data ingestion. Each shard can support up to 1 MB per second of data input and 2 MB per second of data output. By increasing the number of shards, the company can increase the data ingestion rate proportionally. The company can use the UpdateShardCount API operation to modify the number of shards in the data stream1.
The other options are not likely to improve the data ingestion rate into Amazon S3 because:
Option A: Increasing the number of S3 prefixes for the delivery stream to write to will not affect the data ingestion rate, as it only changes the way the data is organized in the S3 bucket. The number of S3 prefixes can help to optimize the performance of downstream applications that read the data from S3, but it does not impact the performance of Kinesis Data Firehose2.
Option B: Decreasing the retention period for the data stream will not affect the data ingestion rate, as it only changes the amount of time the data is stored in the data stream. The retention period can help to manage the data availability and durability, but it does not impact the throughput capacity of the data stream3.
Option D: Adding more consumers using the Kinesis Client Library (KCL) will not affect the data ingestion rate, as it only changes the way the data is processed by downstream applications. The consumers can help to scale the data processing and handle failures, but they do not impact the data ingestion into S3 by Kinesis Data Firehose4.
References:
1: Resharding - Amazon Kinesis Data Streams
2: Amazon S3 Prefixes - Amazon Kinesis Data Firehose
3: Data Retention - Amazon Kinesis Data Streams
4: Developing Consumers Using the Kinesis Client Library - Amazon Kinesis Data Streams
NEW QUESTION # 114
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