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AWS Certified Machine Learning – Speciality MLS-C01

The AWS Certified Machine Learning - Specialty (MLS-C01) exam is created for those who work in artificial intelligence/machine learning (AI/ML) development or data science. In order to design, create, deploy, optimize, train, tune, and manage ML solutions for particular business issues, a candidate must pass a test that assesses their ability to use the AWS Cloud.

Skills required before taking AWS Certified Machine Learning – Specialty exam

The test also verifies a candidate's ability to do the following tasks:

  • Select and defend the optimal machine learning approach for a particular business problem.
  • For the purpose of deploying machine learning solutions, find pertinent AWS services.
  • Design and implement robust, cost-efficient, secure, and scalable machine learning systems.

Who should be the ideal candidate for AWS MLS-C01 exam?

A minimum of two years of hands-on experience designing, architecting, and running machine learning or deep learning workloads on the AWS Cloud should be required of the ideal candidate.

Before taking MLS-C01 exam a much needed AWS knowledge is required

The ideal candidate will have the following knowledge:

  1. The ability to convey how basic machine learning (ML) methods work;
  2. A working knowledge of hyperparameter optimization;
  3. Knowledge of machine learning and deep learning frameworks;
  4. The ability to follow recommended model-training procedures.
  5. Ability to follow operational best practices and efficient deployment techniques

In how many languages a candidate should take AWS MLS-C01 exam?

For this exam Amazon offered this exam in three languages, which are English, Japanese, and simplified Chinese.

Exam formatting MLS-C01 Exam

Exam fee: $300 USD
Time duration: 180 minutes
Language of Exam: English.
Passing score: 750 marks out of 1000
Total Questions: 65 questions

What are the questions formatting in AWS MLS-C01 exam?

A candidate must be asked multiple choice questions, multi response type questions, drop and drag options and some scenario based type questions will be asked in this exam.

Syllabus details about AWS-Certified-Machine-Learning-Specialty-Certification Exam

Domain 1: Data Engineering


          1.1 data repositories for machine learning.
         1.2 understand and implement a data-ingestion solution.
         1.3 Recognize and create a data-transformation solution.


Domain 2: Exploratory Data Analysis

         2.1 prepare data for modeling.
         2.2 Do feature engineering.
         2.3 develop data for machine learning.

Domain 3: Modeling

         3.1 pin point the business problems as machine learning issues.
         3.2 pick the appropriate model(s) for a given machine learning problem.
         3.3 practice and mold machine learning models.
         3.4 understand to perform hyperparameter optimization.

Domain 4: Machine Learning Implementation and Operations

         4.1 Develop machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.
         4.2 Implement and recognize the appropriate machine learning services and features for a given problem.
         4.3 Know about the basics of AWS security practices to machine learning solutions.
         4.4 apply and present machine learning solutions.

On AWS platforms, the AWS Certified Machine Learning - Specialty is in extremely high demand and is highly regarded by companies. AWS MLS-C01 Exam is difficult to pass because to its high market worth, however MLS-C01 dumps PDF may genuinely aid you in doing so. It will provide you the information you need to have a thorough comprehension of all the exam themes deemed important. Your proficiency in implementing cloud projects is validated by this certification.

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With skillfully established data constraints and an authentic selection of exam materials, our AWS MLS-C01 practice test questions are the best available. No additional information is used to ensnare students. The MLS-C01 dumps were created with the goal of making preparation as simple as possible for candidates while also relieving them of having to memorize unnecessary details. Later, you may practice your exam questions using our online testing system, which serves as the greatest exam stimulator and keeps you updated on your test preparation and the amount of additional study time needed to pass this exam on the first time.

Sample Questions

Question 1

A company ingests machine learning (ML) data from web advertising clicks into an AmazonS3 data lake. Click data is added to an Amazon Kinesis data stream by using the KinesisProducer Library (KPL). The data is loaded into the S3 data lake from the data stream byusing an Amazon Kinesis Data Firehose delivery stream. As the data volume increases, anML specialist notices that the rate of data ingested into Amazon S3 is relatively constant.There also is an increasing backlog of data for Kinesis Data Streams and Kinesis DataFirehose to ingest.Which next step is MOST likely to improve the data ingestion rate into Amazon S3?

A. Increase the number of S3 prefixes for the delivery stream to write to. 
B. Decrease the retention period for the data stream. 
C. Increase the number of shards for the data stream. 
D. Add more consumers using the Kinesis Client Library (KCL). 


Question 2

A machine learning specialist is running an Amazon SageMaker endpoint using the built-inobject detection algorithm on a P3 instance for real-time predictions in a company'sproduction application. When evaluating the model's resource utilization, the specialistnotices that the model is using only a fraction of the GPU.Which architecture changes would ensure that provisioned resources are being utilizedeffectively?

A. Redeploy the model as a batch transform job on an M5 instance. 
B. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to theinstance. 
C. Redeploy the model on a P3dn instance. 
D. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) clusterusing a P3 instance. 


Question 3

A company wants to predict the sale prices of houses based on available historical salesdata. The targetvariable in the company’s dataset is the sale price. The features include parameters suchas the lot size, livingarea measurements, non-living area measurements, number of bedrooms, number ofbathrooms, year built,and postal code. The company wants to use multi-variable linear regression to predicthouse sale prices.Which step should a machine learning specialist take to remove features that are irrelevantfor the analysisand reduce the model’s complexity?

A. Plot a histogram of the features and compute their standard deviation. Remove featureswith high variance. 
B. Plot a histogram of the features and compute their standard deviation. Remove featureswith low variance. 
C. Build a heatmap showing the correlation of the dataset against itself. Remove featureswith low mutual correlation scores. 
D. Run a correlation check of all features against the target variable. Remove features withlow target variable correlation scores. 


Question 4

A data scientist is developing a pipeline to ingest streaming web traffic data. The datascientist needs toimplement a process to identify unusual web traffic patterns as part of the pipeline. Thepatterns will be useddownstream for alerting and incident response. The data scientist has access to unlabeledhistoric data to use,if needed.The solution needs to do the following:Calculate an anomaly score for each web traffic entry.Adapt unusual event identification to changing web patterns over time.Which approach should the data scientist implement to meet these requirements?

A. Use historic web traffic data to train an anomaly detection model using the AmazonSageMaker Random Cut Forest (RCF) built-in model. Use an Amazon Kinesis Data Stream to process theincoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by callingthe RCF model to calculate the anomaly score for each record. 
B. Use historic web traffic data to train an anomaly detection model using the AmazonSageMaker built-in XGBoost model. Use an Amazon Kinesis Data Stream to process the incoming web trafficdata. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the XGBoostmodel to calculate the anomaly score for each record. 
C. Collect the streaming data using Amazon Kinesis Data Firehose. Map the deliverystream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time againstthe streaming data with the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for eachrecord using a tumbling window. 
D. Collect the streaming data using Amazon Kinesis Data Firehose. Map the deliverystream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time againstthe streaming data with the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores foreach record using a sliding window. 


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