David Lee David Lee's Profile Page

David Lee David Lee

0 Course Enrolled • 0 Course Completed

Biography

Amazon MLS-C01 Study Dumps - Latest MLS-C01 Dumps Ppt

BTW, DOWNLOAD part of TestSimulate MLS-C01 dumps from Cloud Storage: https://drive.google.com/open?id=1J4O7bSUgBf6_YeTXnfs4oIQ7wgZoTcdg

You deserve this opportunity to win and try to make some difference in your life if you want to attend the MLS-C01 exam and get the certification by the help of our MLS-C01 practice braindumps. As we all know, all companies will pay more attention on the staffs who have more certifications which is a symbol of better understanding and efficiency on the job. Our MLS-C01 Study Materials have the high pass rate as 98% to 100%, hope you can use it fully and pass the exam smoothly.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam offered by Amazon Web Services (AWS) for individuals who want to prove their expertise in machine learning on the AWS platform. MLS-C01 exam is designed to test the knowledge and skills of candidates in various aspects of machine learning, including data preparation, model training, deployment, and monitoring. MLS-C01 Exam is intended for professionals who have a strong understanding of AWS services and machine learning concepts, and who have experience working with these technologies.

>> Amazon MLS-C01 Study Dumps <<

Latest Amazon MLS-C01 Dumps Ppt, MLS-C01 New Soft Simulations

By adhering to the principle of “quality first, customer foremost”, and “mutual development and benefit”, our company will provide first class service for our customers. The exam prepare materials of TestSimulate is high quality and high pass rate, it is completed by our experts who have a good understanding of Real MLS-C01 Exam and have many years of experience writing study materials. They know very well what candidates really need most when they prepare for the MLS-C01 exam. They also understand the real exam situation very well. We will let you know what a real exam is like.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q301-Q306):

NEW QUESTION # 301
An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored for offline analysis.
What approach would be the MOST effective to perform near-real time defect detection?

A. Use Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry outApache Spark ML k-means clustering to determine anomalies.
B. Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest(RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for furtheranalysis.
C. Use AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from withinAWS IoT Analytics to carry out analysis for anomalies.
D. Use Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random CutForest (RCF) algorithm to determine anomalies.

Answer: B

Explanation:
The company wants to perform near-real time defect detection on a time-series of 200 performance metrics, and store all the data for offline analysis. The best approach for this scenario is to use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.
Amazon Kinesis Data Firehose is a service that can capture, transform, and deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk. Kinesis Data Firehose can handle any amount and frequency of data, and automatically scale to match the throughput.
Kinesis Data Firehose can also compress, encrypt, and batch the data before delivering it to the destination, reducing the storage cost and enhancing the security.
Amazon Kinesis Data Analytics is a service that can analyze streaming data in real time using SQL or Apache Flink applications. Kinesis Data Analytics can use built-in functions and algorithms to perform various analytics tasks, such as aggregations, joins, filters, windows, and anomaly detection. One of the built-in algorithms that Kinesis Data Analytics supports is Random Cut Forest (RCF), which is a supervised learning algorithm for forecasting scalar time series using recurrent neural networks. RCF can detect anomalies in streaming data by assigning an anomaly score to each data point, based on how distant it is from the rest of the data. RCF can handle multiple related time series, such as the performance metrics of the aircraft engine, and learn a global model that captures the common patterns and trends across the time series.
Therefore, the company can use the following architecture to build the near-real time defect detection solution:
Use Amazon Kinesis Data Firehose for ingestion: The company can use Kinesis Data Firehose to capture the streaming data from the aircraft engine testing, and deliver it to two destinations: Amazon S3 and Amazon Kinesis Data Analytics. The company can configure the Kinesis Data Firehose delivery stream to specify the source, the buffer size and interval, the compression and encryption options, the error handling and retry logic, and the destination details.
Use Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection: The company can use Kinesis Data Analytics to create a SQL application that can read the streaming data from the Kinesis Data Firehose delivery stream, and apply the RCF algorithm to detect anomalies. The company can use the RANDOM_CUT_FOREST or RANDOM_CUT_FOREST_WITH_EXPLANATION functions to compute the anomaly scores and attributions for each data point, and use the WHERE clause to filter out the normal data points. The company can also use the CURSOR function to specify the input stream, and the PUMP function to write the output stream to another destination, such as Amazon Kinesis Data Streams or AWS Lambda.
Use Kinesis Data Firehose to store data in Amazon S3 for further analysis: The company can use Kinesis Data Firehose to store the raw and processed data in Amazon S3 for offline analysis. The company can use the S3 destination of the Kinesis Data Firehose delivery stream to store the raw data, and use another Kinesis Data Firehose delivery stream to store the output of the Kinesis Data Analytics application. The company can also use AWS Glue or Amazon Athena to catalog, query, and analyze the data in Amazon S3.
What Is Amazon Kinesis Data Firehose?
What Is Amazon Kinesis Data Analytics for SQL Applications?
DeepAR Forecasting Algorithm - Amazon SageMaker

NEW QUESTION # 302
A Machine Learning Specialist was given a dataset consisting of unlabeled data The Specialist must create a model that can help the team classify the data into different buckets What model should be used to complete this work?

A. BlazingText
B. Random Cut Forest (RCF)
C. K-means clustering
D. XGBoost

Answer: C

Explanation:
K-means clustering is a machine learning technique that can be used to classify unlabeled data into different groups based on their similarity. It is an unsupervised learning method, which means it does not require any prior knowledge or labels for the data. K-means clustering works by randomly assigning data points to a number of clusters, then iteratively updating the cluster centers and reassigning the data points until the clusters are stable. The result is a partition of the data into distinct and homogeneous groups. K-means clustering can be useful for exploratory data analysis, data compression, anomaly detection, and feature extraction. References:
K-Means Clustering: A tutorial on how to use K-means clustering with Amazon SageMaker.
Unsupervised Learning: A video that explains the concept and applications of unsupervised learning.

NEW QUESTION # 303
A company needs to deploy a chatbot to answer common questions from customers. The chatbot must base its answers on company documentation.
Which solution will meet these requirements with the LEAST development effort?

A. Index company documents by using Amazon OpenSearch Service. Integrate the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation to answer customer questions.
B. Train a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents. Deploy the model as a real-time Amazon SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.
C. Index company documents by using Amazon Kendra. Integrate the chatbot with Amazon Kendra by using the Amazon Kendra Query API operation to answer customer questions.
D. Train an Amazon SageMaker BlazingText model based on past customer questions and company documents. Deploy the model as a real-time SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.

Answer: C

Explanation:
The solution A will meet the requirements with the least development effort because it uses Amazon Kendra, which is a highly accurate and easy to use intelligent search service powered by machine learning. Amazon Kendra can index company documents from various sources and formats, such as PDF, HTML, Word, and more. Amazon Kendra can also integrate with chatbots by using the Amazon Kendra Query API operation, which can understand natural language questions and provide relevant answers from the indexed documents. Amazon Kendra can also provide additional information, such as document excerpts, links, and FAQs, to enhance the chatbot experience1.
The other options are not suitable because:
Option B: Training a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents, deploying the model as a real-time Amazon SageMaker endpoint, and integrating the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation will incur more development effort than using Amazon Kendra. The company will have to write the code for the BiDAF network, which is a complex deep learning model for question answering. The company will also have to manage the SageMaker endpoint, the model artifact, and the inference logic2.
Option C: Training an Amazon SageMaker BlazingText model based on past customer questions and company documents, deploying the model as a real-time SageMaker endpoint, and integrating the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation will incur more development effort than using Amazon Kendra. The company will have to write the code for the BlazingText model, which is a fast and scalable text classification and word embedding algorithm. The company will also have to manage the SageMaker endpoint, the model artifact, and the inference logic3.
Option D: Indexing company documents by using Amazon OpenSearch Service and integrating the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation will not meet the requirements effectively. Amazon OpenSearch Service is a fully managed service that provides fast and scalable search and analytics capabilities. However, it is not designed for natural language question answering, and it may not provide accurate or relevant answers for the chatbot. Moreover, the k-NN Query API operation is used to find the most similar documents or vectors based on a distance function, not to find the best answers based on a natural language query4.
References:
1: Amazon Kendra
2: Bidirectional Attention Flow for Machine Comprehension
3: Amazon SageMaker BlazingText
4: Amazon OpenSearch Service

NEW QUESTION # 304
A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.
Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?

A. Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.
B. Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.
C. Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.
D. Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.

Answer: D

Explanation:
Amazon SageMaker script mode is a feature that allows users to use training scripts similar to those they would use outside SageMaker with SageMaker's prebuilt containers for various frameworks such as TensorFlow. Script mode supports reading data from Amazon S3 buckets without requiring any changes to the training script. Therefore, option B is the best method of providing training data to Amazon SageMaker that would meet the business requirements with the least development overhead.
Option A is incorrect because using a local path of the data would not be scalable or reliable, as it would depend on the availability and capacity of the local storage. Moreover, using a local path of the data would not leverage the benefits of Amazon S3, such as durability, security, and performance. Option C is incorrect because rewriting the train.py script to convert TFRecords to protobuf would require additional development effort and complexity, as well as introduce potential errors and inconsistencies in the data format. Option D is incorrect because preparing the data in the format accepted by Amazon SageMaker would also require additional development effort and complexity, as well as involve using additional services such as AWS Glue or AWS Lambda, which would increase the cost and maintenance of the solution.
References:
Bring your own model with Amazon SageMaker script mode
GitHub - aws-samples/amazon-sagemaker-script-mode
Deep Dive on TensorFlow training with Amazon SageMaker and Amazon S3
amazon-sagemaker-script-mode/generate_cifar10_tfrecords.py at master

NEW QUESTION # 305
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. Tokenization
B. Normalization transformation
C. One-hot encoding
D. Binarization

Answer: C

NEW QUESTION # 306
......

If you want to clear the Central Finance in AWS Certified Machine Learning - Specialty (MLS-C01) test, then you need to study well with real AWS Certified Machine Learning - Specialty (MLS-C01) exam dumps of TestSimulate. These Amazon MLS-C01 exam dumps are trusted and updated. We guarantee that you can easily crack the AWS Certified Machine Learning - Specialty (MLS-C01) test if use our actual Central Finance in AWS Certified Machine Learning - Specialty (MLS-C01) dumps.

Latest MLS-C01 Dumps Ppt: https://www.testsimulate.com/MLS-C01-study-materials.html

P.S. Free & New MLS-C01 dumps are available on Google Drive shared by TestSimulate: https://drive.google.com/open?id=1J4O7bSUgBf6_YeTXnfs4oIQ7wgZoTcdg

David Lee David Lee

Biography

Important Links