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What Topics Are Covered in AWS Machine Learning - Specialty Certification Exam?

The certification exam for the AWS Machine Learning – Specialty certification tests the candidates' ability to select the best machine learning strategy to improve the business processes. Also, they will be able to identify the best AWS service needed to implement different machine learning solutions. Besides, candidates will be able to design and put into practice reliable, scalable, and cost-optimized machine learning solutions. The AWS MLS-C01 exam, in particular, focuses on four domains. They are the following:

  • Modeling;
  • Data engineering;
  • Machine Learning Implementation and Operations.
  • Exploratory Data Analysis;

The first topic handles data engineering and has 3 sections. The first one handles the creation of data repositories for efficient machine learning strategies. Also, candidates will learn how to effectively identify and implement solutions related to data-ingestion. The third sub-domain included in this section focuses on the implementation and identification of different data-transformation solutions. The second tested area concentrates on exploratory data analysis. When they prepare for this topic, candidates will learn how to sanitize and prepare data for modeling. Also, they will learn how to make a performance when it comes to feature engineering. Finally, candidates will learn how to visualize data and analyze different parameters for machine learning. Within the modeling section, candidates will learn how to frame different business problems related to machine learning issues. Besides, they will find how to select the right models for different machine learning problems. In this section, candidates will also learn how to train effectively for machine learning models. Another essential concept that candidates will discover in this part is related to hyperparameter optimization performance. Last but not least, applicants will understand how to correctly evaluate machine learning models. The final domain concentrates on machine learning operations and implementation. This segment focuses on helping candidates develop advanced abilities in building machine learning solutions to achieve the highest performance and fault tolerance. These solutions will help them ensure availability, resilience, and scalability. Another subtopic included in the last objective deals with the recommendations and implementation of the right machine learning services adapted to the business context. Candidates will also learn how to apply fundamental AWS security practices to solve different machine learning issues. Finally, they will become ready to deploy and operationalize various machine learning solutions. >> AWS-Certified-Machine-Learning-Specialty Reliable Practice Materials <<

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q190-Q195):

NEW QUESTION # 190
A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.
Which model describes the underlying data in this situation?

  • A. A full Bayesian network, since some of the features are statistically dependent.
  • B. A naive Bayesian model, since some of the features are statistically dependent.
  • C. A full Bayesian network, since the features are all conditionally independent.
  • D. A naive Bayesian model, since the features are all conditionally independent.

Answer: B
NEW QUESTION # 191
A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.
The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.
Which solution should the Data Scientist build to satisfy the requirements?

  • A. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL database. Have the Analysts query and run dashboards from the RDS database.
  • B. Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
  • C. Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
  • D. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.

Answer: C
NEW QUESTION # 192
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 DayOfWeek column to binary values.
What technique should be used to convert this column to binary values?

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

Answer: D
NEW QUESTION # 193
A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city As this is a prototype, only daily data from the last year is available Which model is MOST likely to provide the best results in Amazon SageMaker?

  • A. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data
  • B. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor
  • C. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
  • D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_tyce of classifier

Answer: C
NEW QUESTION # 194
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

  • A. The output path specifying where on an Amazon S3 bucket the trained model will persist.
  • B. The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
  • C. The validation channel identifying the location of validation data on an Amazon S3 bucket.
  • D. The training channel identifying the location of training data on an Amazon S3 bucket.
  • E. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
  • F. Hyperparameters in a JSON array as documented for the algorithm used.

Answer: A,D,E
NEW QUESTION # 195
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