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ModelingThis is the most prominent part of this exam with a weightage of 36%. This means that it is impossible to get the passing score if you have not mastered this topic. It covers the following sections, including framing business problems as machine learning problems and training machine learning model. In addition, the test takers need to be ready to demonstrate their competence in selecting an appropriate model for specific machine learning problems. They have to possess the ability to perform hyperparameter optimization as well as perform the task of evaluating machine learning models.
Data EngineeringThis domain makes up about 20% of the total questions in the entire exam content. It measures the test takers¡¯ ability to accomplish the technical tasks such as creating machine and learning data repositories. Another section that is included in this topic is implementing & identifying the data ingestion solution. Besides that, the students are also required to demonstrate their skills in implementing and identifying data transformation solutions.
Machine Learning Implementation and OperationsThis is the last objective of the certification exam. It mainly includes the concepts of machine learning services as well as implementation. It carries about 20% weightage in the test. One of the most prominent sections covered within this domain is building machine learning solutions for availability, fault tolerance, resiliency, and performance. It also evaluates the candidates¡¯ knowledge and skills in implementing and recommending the correct machine learning services particularly for a given problem. In addition, the examinees should be ready to demonstrate that they know about applying basic Amazon Web Services (AWS) security practices to specific machine learning solutions. Also, this part requires that the IT pros attempting this test make evident that they have the skills and knowledge required to deploy and operationalize machine learning solutions.
Exploratory Data AnalysisThe second subject area covers about 24% of the total weightage and requires that the learners know about data for machine learning. They should know more about machine modeling concepts as well. The questions in this section demand that the candidates possess the skills in preparing data & sanitizing that particular data for modeling. Other items seek to verify their expertise in performing feature engineering. Last but not least, the applicants should demonstrate their understanding of how to perform visualization and canalization of data for machine learning.
MLS-C01 Vce Exam | MLS-C01 ExamcollectionWe are never complacent about our achievements, so all content of our MLS-C01 exam questions are strictly researched by proficient experts who absolutely in compliance with syllabus of this exam. Accompanied by tremendous and popular compliments around the world, to make your feel more comprehensible about the MLS-C01 study prep, all necessary questions of knowledge concerned with the exam are included into our MLS-C01 simulating exam. Preparation ProcessMany useful resources are available for the Amazon MLS-C01 Exam. Let¡¯s take a closer look at them.
AWS Machine Learning DocumentationThe official AWS Machine Learning Documentation is one of the best preparation guides for the Amazon AWS Certified Machine Learning ¨C Specialty exam. It is a great material to learn all the topics included in the exam content. You can find documentation, such as splitting data, data transformation, as well as types of data.
AWS Training CoursesThe vendor offers the official Amazon AWS Machine Learning Certification Training, which you can access by visiting the Amazon website. It is good to know that these training courses are usually available as free.
Discussion ForumsBesides the official training options offered by the vendor, many websites provide the candidates with the topic details and useful information related to the exam as well as the certification. Some of these sites include Quora and ExamLabs.com.
The AWS Certified Machine Learning - Specialty certification is a valuable credential for professionals who want to advance their careers in the field of ML. Certified individuals have a competitive edge in the job market, as they demonstrate their ability to design and implement cutting-edge ML solutions on the AWS platform. Moreover, the certification is recognized by industry leaders and organizations, which further enhances its value and credibility. Overall, the AWS Certified Machine Learning - Specialty exam is a challenging but rewarding certification that can help individuals prove their expertise in the field of ML and advance their careers. Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q318-Q323):NEW QUESTION # 318
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker.
The historical training data is stored in Amazon RDS.
Which approach should the Specialist use for training a model using that data?
A. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
B. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.
C. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
D. Write a direct connection to the SQL database within the notebook and pull data in
Answer: A
NEW QUESTION # 319
IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi- structured data The following are high priorities for the company:
* Solution simplicity
* Fast development time
* Low cost
* High flexibility
What technologies meet the company's requirements?
A. Amazon RDS and Amazon ES
B. Amazon DynamoDB and DynamoDB Accelerator (DAX)
C. Amazon Redshift and AWS Glue
D. Amazon S3 and Amazon Athena
Answer: D
Explanation:
Amazon S3 and Amazon Athena are technologies that meet the company's requirements for a temporary ad hoc solution for machine learning data storage and query. Amazon S3 and Amazon Athena have the following features and benefits:
* Amazon S3 is a service that provides scalable, durable, and secure object storage for any type of data.
Amazon S3 can store csv and JSON files, as well as other formats, and can handle large volumes of data with high availability and performance. Amazon S3 also integrates with other AWS services, such as Amazon Athena, for further processing and analysis of the data.
* Amazon Athena is a service that allows querying data stored in Amazon S3 using standard SQL.
Amazon Athena can query over semi-structured data, such as JSON, as well as structured data, such as csv, without requiring any loading or transformation. Amazon Athena is serverless, meaning that there is no infrastructure to manage and users only pay for the queries they run. Amazon Athena also supports the use of AWS Glue Data Catalog, which is a centralized metadata repository that can store and manage the schema and partition information of the data in Amazon S3.
Using Amazon S3 and Amazon Athena, the company can achieve the following high priorities:
* Solution simplicity: Amazon S3 and Amazon Athena are easy to use and require minimal configuration and maintenance. The company can simply upload the csv and JSON files to Amazon S3 and use Amazon Athena to query them using SQL. The company does not need to worry about provisioning, scaling, or managing any servers or clusters.
* Fast development time: Amazon S3 and Amazon Athena can enable the company to quickly access and analyze the data without any data preparation or loading. The company can use the existing workforce of SQL experts to write and run queries on Amazon Athena and get results in seconds or minutes.
* Low cost: Amazon S3 and Amazon Athena are cost-effective and offer pay-as-you-go pricing models.
Amazon S3 charges based on the amount of storage used and the number of requests made. Amazon Athena charges based on the amount of data scanned by the queries. The company can also reduce the costs by using compression, encryption, and partitioning techniques to optimize the data storage and query performance.
* High flexibility: Amazon S3 and Amazon Athena are flexible and can support various data types, formats, and sources. The company can store and query any type of data in Amazon S3, such as csv, JSON, Parquet, ORC, etc. The company can also query data from multiple sources in Amazon S3, such as data lakes, data warehouses, log files, etc.
The other options are not as suitable as option A for the company's requirements for the following reasons:
* Option B: Amazon Redshift and AWS Glue are technologies that can be used for data warehousing and data integration, but they are not ideal for a temporary ad hoc solution. Amazon Redshift is a service that provides a fully managed, petabyte-scale data warehouse that can run complex analytical queries using SQL. AWS Glue is a service that provides a fully managed extract, transform, and load (ETL) service that can prepare and load data for analytics. However, using Amazon Redshift and AWS Glue would require more effort and cost than using Amazon S3 and Amazon Athena. The company would need to load the data from Amazon S3 to Amazon Redshift using AWS Glue, which can take time and incur additional charges. The company would also need to manage the capacity and performance of the Amazon Redshift cluster, which can be complex and expensive.
* Option C: Amazon DynamoDB and DynamoDB Accelerator (DAX) are technologies that can be used for fast and scalable NoSQL database and caching, but they are not suitable for the company's data storage and query needs. Amazon DynamoDB is a service that provides a fully managed, key-value and document database that can deliver single-digit millisecond performance at any scale. DynamoDB Accelerator (DAX) is a service that provides a fully managed, in-memory cache for DynamoDB that can improve the read performance by up to 10 times. However, using Amazon DynamoDB and DAX would not allow the company to continue to use SQL as a query language, as Amazon DynamoDB does not support SQL. The company would need to use the DynamoDB API or the AWS SDKs to access and query the data, which can require more coding and learning effort. The company would also need to transform the csv and JSON files into DynamoDB items, which can involve additional processing and complexity.
* Option D: Amazon RDS and Amazon ES are technologies that can be used for relational database and search and analytics, but they are not optimal for the company's data storage and query scenario.
Amazon RDS is a service that provides a fully managed, relational database that supports various database engines, such as MySQL, PostgreSQL, Oracle, etc. Amazon ES is a service that provides a fully managed, Elasticsearch cluster, which is mainly used for search and analytics purposes. However, using Amazon RDS and Amazon ES would not be as simple and cost-effective as using Amazon S3 and Amazon Athena. The company would need to load the data from Amazon S3 to Amazon RDS, which can take time and incur additional charges. The company would also need to manage the capacity and performance of the Amazon RDS and Amazon ES clusters, which can be complex and expensive. Moreover, Amazon RDS and Amazon ES are not designed to handle semi-structured data, such as JSON, as well as Amazon S3 and Amazon Athena.
References:
* Amazon S3
* Amazon Athena
* Amazon Redshift
* AWS Glue
* Amazon DynamoDB
* [DynamoDB Accelerator (DAX)]
* [Amazon RDS]
* [Amazon ES]
NEW QUESTION # 320
A term frequency-inverse document frequency (tf-idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences:
1. Please call the number below.
2. Please do not call us.
What are the dimensions of the tf-idf matrix?
A. (2, 8)
B. (2, 10)
C. (8, 10)
D. (2, 16)
Answer: D
Explanation:
There are 2 sentences, 8 unique unigrams, and 8 unique bigrams, so the result would be (2,16).
The phrases are "Please call the number below" and "Please do not call us." Each word individually (unigram) is "Please," "call," "the," "number," "below," "do," "not," and "us." The unique bigrams are "Please call," "call the," "the number," "number below," "Please do," "do not," "not call," and "call us."
NEW QUESTION # 321
A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured. Amazon SageMaker Model Monitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset. This dataset is statistically representative of the current production traffic. The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations.
What should the ML specialist do to resolve the violations?
A. Manually trigger the monitoring job to re-evaluate the SageMaker endpoint traffic sample.
B. Delete the endpoint and recreate it with the original configuration.
C. Run the Model Monitor baseline job again on the new training set. Configure Model Monitor to use the new baseline.
D. Retrain the model again by using a combination of the original training set and the new training set.
Answer: C
Explanation:
Explanation
The ML specialist should run the Model Monitor baseline job again on the new training set and configure Model Monitor to use the new baseline. This is because the baseline job computes the statistics and constraints for the data quality and model quality metrics, which are used to detect violations. If the training set changes, the baseline job should be updated accordingly to reflect the new distribution of the data and the model performance. Otherwise, the old baseline may not be representative of the current production traffic and may cause false alarms or miss violations. References:
Monitor data and model quality - Amazon SageMaker
Detecting and analyzing incorrect model predictions with Amazon SageMaker Model Monitor and Debugger | AWS Machine Learning Blog
NEW QUESTION # 322
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?
A. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
B. The precision of the model is 86%, which is greater than the accuracy of the model.
C. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
D. The precision of the model is 86%, which is less than the accuracy of the model.
