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Topics in AWS Certified Machine Learning - Specialty

The following will be discussed in AMAZON MLS-C01 practice exam and AMAZON MLS-C01 practice exams:

• Modeling
• Exploratory Data Analysis
• Data Engineering
• Machine Learning Implementation and Operations

How to Prepare for the AWS Certified Machine Learning Specialty Exam

Preparation Guide for For AWS Certified Machine Learning Specialty Exam Introduction Amazon Web Services is the current market leader in the field of cloud computing. Many organizations are boarding the AWS train for very promising benefits. Profitability, flexibility, ease of use and comprehensive support are the pillars of AWS's popularity. As AWS gained popularity, many companies began looking for AWS certified professionals. AWS certified machine learning specialty certification is one of many AWS certifications popular today. AWS provides certification to validate an individual's skills and experience in AWS specific tools, resources and technologies. The following discussion will focus on the details of the AWS certified machine learning specialty certification. The purpose of the following discussion is to support the preparation of candidates for the certification exam. >> AWS-Certified-Machine-Learning-Specialty Exam Dumps Free <<

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What is the duration of the AWS Certified Machine Learning - Specialty

• Length of Examination: 130 minutes
• Number of Questions: 54
• Passing Score: 720
• Format: Multiple choices, multiple answers
• Language : English, Japanese, Korean, and Simplified Chinese

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q198-Q203):

NEW QUESTION # 198
A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?

• A. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.
• B. Сonfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
• C. Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.
• D. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.

Answer: A Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html
NEW QUESTION # 199
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII).
The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?

• A. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
• B. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
• C. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
• D. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance

Answer: B Explanation:
Explanation/Reference: https://docs.aws.amazon.com/AmazonS3/latest/dev/example-bucket-policies-vpc-endpoint.html
NEW QUESTION # 200
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

• A. Hyperparameters in a JSON array as documented for the algorithm used.
• B. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
• C. The validation channel identifying the location of validation data on an Amazon S3 bucket.
• D. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
• E. The training channel identifying the location of training data on an Amazon S3 bucket.
• F. The output path specifying where on an Amazon S3 bucket the trained model will persist.

Answer: B,E,F
NEW QUESTION # 201
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 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
• B. Use AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from within AWS IoT Analytics to carry out analysis for anomalies.
• C. Use Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random Cut Forest (RCF) algorithm to determine anomalies.
• D. Use Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry out Apache Spark ML k-means clustering to determine anomalies.

Answer: D Explanation:
analysis.
NEW QUESTION # 202
A company is building a new version of a recommendation engine. Machine learning (ML) specialists need to keep adding new data from users to improve personalized recommendations. The ML specialists gather data from the users' interactions on the platform and from sources such as external websites and social media.
The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this data is stored in Amazon S3. A set of Python scripts was coded to do the job and is stored in a large Amazon EC2 instance. The whole process takes more than 20 hours to finish, with each script taking at least an hour. The company wants to move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers.
Which approach will address all of these requirements with the LEAST development effort?

• A. Load the data into Amazon DynamoDB. Convert the scripts to an AWS Lambda function. Execute the pipeline by triggering Lambda executions. Store the results in Amazon S3.
• B. Create an AWS Glue job. Convert the scripts to PySpark. Execute the pipeline. Store the results in Amazon S3.
• C. Load the data into an Amazon Redshift cluster. Execute the pipeline by using SQL. Store the results in Amazon S3.
• D. Create a set of individual AWS Lambda functions to execute each of the scripts. Build a step function by using the AWS Step Functions Data Science SDK. Store the results in Amazon S3.

Answer: A
NEW QUESTION # 203
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