For those who work in data science or artificial intelligence/machine learning (AI/ML) development, the AWS Certified Machine Learning - Specialty (MLS-C01) exam is designed for them. The test verifies a candidate's capacity to use the AWS Cloud to design, develop, implement, optimize, train, tune, and manage ML solutions for specific business challenges.
The exam also confirms a candidate's competence to do the following tasks:
- Choose and justify the best machine learning technique for a specific business challenge.
- Identify relevant AWS services for implementing machine learning solutions.
- Create and execute scalable, cost-effective, dependable, and secure machine learning systems.
Who ought to take this Exam?
The ideal applicant should have at least two years' worth of first-hand experience creating, architecting, and executing machine learning or deep learning workloads on the AWS Cloud.
Preferred familiarity with AWS
The following knowledge is what the ideal applicant should possess:
- the capacity to communicate the idea behind fundamental machine learning (ML) algorithms;
- practical experience with basic hyperparameter optimization;
- familiarity with ML and deep learning frameworks;
- The capacity to adhere to model-training best practices.
- The capacity to adhere to operational best practices and optimal deployment methods
Syllabus details about AWS-Certified-Machine-Learning-Specialty-Certification exam
Domain 1: Data Engineering
- Create data repositories for machine learning.
- Identify and implement a data-ingestion solution.
- Identify and implement a data-transformation solution.
Domain 2: Exploratory Data Analysis
- Sanitize and prepare data for modeling.
- Perform feature engineering.
- Analyze and visualize data for machine learning.
Domain 3: Modeling
- Frame business problems as machine learning problems.
- Select the appropriate model(s) for a given machine learning problem.
- Train machine learning models.
- Perform hyperparameter optimization.
Domain 4: Machine Learning Implementation and Operations
- Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.
- Recommend and implement the appropriate machine learning services and features for a given problem.
- Apply basic AWS security practices to machine learning solutions.
- Deploy and operationalize machine learning solutions.
MLS-C01 Exam Info:
|Exam Name||AWS Certified Machine Learning Specialty|
|No. of Questions||65|
|Exam Cost||USD 300|
|Passing score||750/1000 Or 75%|
|Exam Time Duration||180 minutes|
|Language||English, Korean, Japanese, Simplified Chinese|
|Format of Exam||Multiple Choice Questions, Drag and Drop, Multiple Answers, Scenario-based|
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A company needs to quickly make sense of a large amount of data and gain insight from it.The data is in different formats, the schemas change frequently, and new data sources areadded regularly. The company wants to use AWS services to explore multiple datasources, suggest schemas, and enrich and transform the data. The solution should requirethe least possible coding effort for the data flows and the least possible infrastructuremanagement.Which combination of AWS services will meet these requirements?
A. Amazon EMR for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
B. Amazon Kinesis Data Analytics for data ingestion Amazon EMR for data discovery, enrichment, and transformation Amazon Redshift for querying and analyzing the results in Amazon S3
C. AWS Glue for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
D. AWS Data Pipeline for data transfer AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment,and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
ANSWER : A
A Machine Learning Specialist is deciding between building a naive Bayesian model or afull Bayesian networkfor a classification problem. The Specialist computes the Pearson correlation coefficientsbetween eachfeature and finds that their absolute values range between 0.1 to 0.95.Which model describes the underlying data in this situation?
A. A naive Bayesian model, since the features are all conditionally independent.
B. A full Bayesian network, since the features are all conditionally independent.
C. A naive Bayesian model, since some of the features are statistically dependent.
D. A full Bayesian network, since some of the features are statistically dependent.
ANSWER : C
A retail company wants to combine its customer orders with the product description datafrom its product catalog. The structure and format of the records in each dataset isdifferent. A data analyst tried to use a spreadsheet to combine the datasets, but the effortresulted in duplicate records and records that were not properly combined. The companyneeds a solution that it can use to combine similar records from the two datasets andremove any duplicates.Which solution will meet these requirements?
A. Use an AWS Lambda function to process the data. Use two arrays to compare equalstrings in the fields from the two datasets and remove any duplicates.
B. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Callthe AWS Glue SearchTables API operation to perform a fuzzy-matching search on the twodatasets, and cleanse the data accordingly.
C. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Usethe FindMatches transform to cleanse the data.
D. Create an AWS Lake Formation custom transform. Run a transformation for matchingproducts from the Lake Formation console to cleanse the data automatically.
ANSWER : D
A logistics company needs a forecast model to predict next month's inventory requirementsfor a single item in 10 warehouses. A machine learning specialist uses Amazon Forecast todevelop a forecast model from 3 years of monthly data. There is no missing data. Thespecialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolutepercentage error (MAPE) is much larger than the MAPE produced by the current humanforecasters.Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)
A. Set PerformAutoML to true.
B. Set ForecastHorizon to 4.
C. Set ForecastFrequency to W for weekly.
D. Set PerformHPO to true.
E. Set FeaturizationMethodName to filling.
ANSWER : C,D