 Amazon MLS-C01 : AWS Certified Machine Learning Specialty (MLS-C01) ExamExam Dumps Organized by Honghui
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Exam Number : MLS-C01
Exam Name : AWS Certified Machine Learning Specialty (MLS-C01)
Vendor Name : Amazon
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MLS-C01 exam Format | MLS-C01 Course Contents | MLS-C01 Course Outline | MLS-C01 exam Syllabus | MLS-C01 exam Objectives
Format : Multiple choice, multiple answer
Type : Specialty
Delivery Method : Testing center or online proctored exam
Time : 170 minutes to complete the exam
Introduction
The AWS Certified Machine Learning - Specialty (MLS-C01) examination is intended for individuals who perform a
development or data science role. This exam validates an examinees ability to build, train, tune, and deploy
machine learning (ML) models using the AWS Cloud.
It validates an examinees ability to design, implement, deploy, and maintain ML solutions for given business
problems. It will validate the candidates ability to:
=> Select and justify the appropriate ML approach for a given business problem.
=> Identify appropriate AWS services to implement ML solutions.
=> Design and implement scalable, cost-optimized, reliable, and secure ML solutions
Expectations;
- 1-2 years of experience developing, architecting, or running ML/deep learning workloads on the AWS Cloud
- The ability to express the intuition behind basic ML algorithms
- Experience performing basic hyperparameter optimization
- Experience with ML and deep learning frameworks
- The ability to follow model-training best practices
- The ability to follow deployment and operational best practices
Exam Content
There are two types of questions on the examination:
=> Multiple choice: Has one correct response and three incorrect responses (distractors).
=> Multiple response: Has two or more correct responses out of five or more options.
Select one or more responses that best complete the statement or answer the question. Distractors, or incorrect
answers, are response options that an examinee with incomplete knowledge or skill would likely choose. However,
they are generally plausible responses that fit in the content area defined by the test objective.
Unanswered questions are scored as incorrect; there is no penalty for guessing.
Unscored Content
Your examination may include unscored items that are placed on the test to gather statistical information. These
items are not identified on the form and do not affect your score.
Exam Results
The AWS Certified Machine Learning - Specialty (MLS-C01) examination is a pass or fail exam. The examination is
scored against a minimum standard established by AWS professionals who are guided by certification industry best
practices and guidelines.
Your results for the examination are reported as a score from 100–1,000, with a minimum passing score of 750.
Your score shows how you performed on the examination as a whole and whether or not you passed. Scaled
scoring models are used to equate scores across multiple exam forms that may have slightly different difficulty
levels.
Domain 1: Data Engineering 20%
Domain 2: Exploratory Data Analysis 24%
Domain 3: Modeling 36%
Domain 4: Machine Learning Implementation and Operations 20%
TOTAL 100%
Domain 1: Data Engineering
1.1 Create data repositories for machine learning.
1.2 Identify and implement a data-ingestion solution.
1.3 Identify and implement a data-transformation solution.
Domain 2: Exploratory Data Analysis
2.1 Sanitize and prepare data for modeling.
2.2 Perform feature engineering.
2.3 Analyze and visualize data for machine learning.
Domain 3: Modeling
3.1 Frame business problems as machine learning problems.
3.2 Select the appropriate model(s) for a given machine learning problem.
3.3 Train machine learning models.
3.4 Perform hyperparameter optimization.
3.5 Evaluate machine learning models.
Domain 4: Machine Learning Implementation and Operations
4.1 Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.
4.2 Recommend and implement the appropriate machine learning services and features for a given problem.
4.3 Apply basic AWS security practices to machine learning solutions.
4.4 Deploy and operationalize machine learning solutions.
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Amazon AWS Study Guide
solid AI backed this submit.
Leon is co-founder and CTO at cast AI. formerly vice president of protection items for Oracle Cloud Infrastructure, Leon has more than twenty years of adventure spanning across corporations equivalent to IBM, Truition and HostedPCI. He situated and served as the CTO of Zenedge, acquired by Oracle.
Spot circumstances present bargain, however there’s a trap. AWS can pull the plug at any time, supplying you with most effective two minutes to circulation your Kubernetes workloads in other places. When used neatly, spot circumstances can cause dramatic cost reductions. although, they aren’t a silver bullet and AWS comes with some obstacles.
in case your industry or setup prevents you from operating workloads on spot situations, don’t be anxious. There are nevertheless how you can reduce your cloud invoice even by means of half. The optimum solution to get begun is with cloud instance rightsizing.
In its essence, rightsizing capability:
examining the utilization and efficiency metrics of your circumstances
knowing whether or not they’re running efficiently for the cost
improving the efficiency-to-charge ratio of your infrastructure by means of upgrading, downgrading or terminating instances
here’s a step-with the aid of-step book to opting for the most efficient Amazon EC illustration in your Kubernetes workload.
1. outline Your necessities
You may be tempted to get an inexpensive instance, but you’ll be taking a chance right here. The illustration could journey performance considerations when you run a reminiscence-intensive software.
agree with the requirements of your workload first. To retain cloud expenses at bay, order most effective what you want across these key compute dimensions:
CPU count number
CPU structure
reminiscence
SSD storage
network
So, you’ve recognized a group of matching instance kinds. earlier than deciding on the class, choose between CPU- and GPU-dense circumstances. in case you’re constructing a machine getting to know application, pick a GPU instance. It’s lots quicker in practicing models. AWS delivered a new illustration category designed for inference (study more in the next part).
2. select an instance classification that matches
AWS offers a few styles of EC2 instances that suit diverse use cases and present distinctive parameter ratios. They’re additionally scalable, so it potential so you might at all times get a larger measurement in case your workload calls for it
typical purpose
This class comes with a balanced ratio of CPU to memory. It’s an outstanding healthy for normal-intention applications that use an equal amount of CPU and memory, akin to net servers with low to medium traffic or smaller databases.
Compute-Optimized
These cases are optimized for CPU-intensive workloads and have a excessive ratio of CPU to memory. decide on them for use situations comparable to web servers with medium site visitors, batch preprocessing or application servers.
reminiscence-Optimized
This class offers a excessive memory-to-CPU ratio and works neatly in construction workloads like database servers, analytics or better in-reminiscence caches.
Storage-Optimized
A storage-optimized example is a good choice for workloads that need heavy examine/write operations and low latency. It’s remarkable for large statistics, SQL and NoSQL databases and information warehousing.
Accelerated Computing
These situations use hardware accelerators to carry out tasks similar to records demo matching, photos processing and floating-factor quantity calculations a lot better than application working on CPUs. select them for laptop researching and excessive-efficiency computing (HPC).
Inference class
AWS introduced this category to assist laptop gaining knowledge of applications. EC2 Inf1 presents up to 30% better throughput and 45% lower can charge per inference than AWS EC2 G4 cases.
3. accept as true with the depend of Chips and Processors
To offer compute capabilities, cloud suppliers roll out diverse computers. The chips in these computer systems have different efficiency traits.
right here’s an example scenario. while one instance has an older-era processor it is a bit of slower, the different one is a brand new-generation processor this is a bit of sooner. with none focus, you could turn out to be selecting an example that offers mighty efficiency characteristics you don’t really need.
the way to verify true performance across a number of circumstances
The foremost formulation for selecting the most excellent VM for the job is benchmarking, which capability losing the equal workload onto a special instance type and checking its performance. We did at solid AI when we begun over a 12 months in the past. right here’s what we discovered.
example insight: differences in Cloud persistence
To be mindful illustration efficiency, we developed a metric called “persistence Coefficient.” right here’s how we calculated it:
We measured how lots work a given illustration classification can do in 12 hours and how variable its efficiency is.
word that for a sustained baseload, your aim is stability. For a bursty workload, lessen steadiness works simply satisfactory.
In our case analyze, situations with solid efficiency are nearer to a hundred, and cases with random performance are closer to 0 price.
Making a choice right here might be complex, considering it’s now not clear how a good deal stability you’re getting to your funds when the usage of diverse instance kinds.
In our case examine, the DigitalOcean s1_1 desktop accomplished an persistence coefficient of 0.97107 (97%). The AWS t3_medium_st carried out 0.43152 (43%).
source: solid AI
supply: forged AI
note: trust ARM-Powered circumstances
AWS already presents ARM-primarily based situations – as an instance, the EC2 A1 family uses the Graviton2 ARM processor. ARM is more affordable to run and funky since it consumes less energy. Cloud providers are more likely to charge less for it.
if you’d want to use it, you might should re-architect your start pipeline to assemble your utility for ARM. on the other hand, if you’re working an interpreted stack (consider Python, Ruby or NodeJS), your purposes will doubtless run.
3. Take capabilities of CPU Bursting
Burstable efficiency circumstances were designed to present companies a baseline level of CPU performance with the added alternative of bursting to a more robust level when the workload necessities change suddenly.
Such cases work well with low-latency interactive applications, microservices or small and medium databases.
note that the variety of gathered CPU credits will all the time depend on the example classification you select. In frequent, tremendous circumstances assemble greater credit per hour for 4 hours or greater per day, on general. if you run an e-commerce website that gets a surge of company after every advertising and marketing crusade, a burstable illustration is an excellent fit, however.
agree with This: CPU ability Has Its Limits
through checking out burstable instances, we found that compute capability constantly raises linearly all through the first four hours and after that turns into a good deal more confined. here’s a chart for a t2_2xlarge illustration.
supply: solid AI
4. Double-check Storage transfer boundaries
yet another massive charge generator is records storage. AWS EC2 instances use Elastic Block shop (EBS) for storing disk volumes. When deciding on an illustration type, make sure that it comes with a storage throughput that your software requires. keep away from costly pressure alternatives akin to premium SSD except you predict to make use of them to the fullest.
5. Don’t neglect in regards to the community Bandwidth
if you’re dealing with an immense facts migration or a excessive extent of traffic, take a look on the size of the network connection between your illustration and the consumers assigned to it.
You might locate situations that may bolster 10 or 20 Gbps of transfer speed. be aware, although, that best these cases should be able to support this level of community bandwidth.
Wrap Up: computerized Rightsizing
as an alternative of rightsizing cloud situations manually, that you would be able to get an automated answer that does the job for you.
We developed cast AI to do this with an AI-driven example-choice algorithm. It selects the most useful example classification that meets the workload necessities and applies changes on every occasion your cluster wants further nodes. Your workloads will at all times be running at maximum efficiency and minimum cost.
register to solid AI and analyze your cluster without charge to birth saving from 50% to 90% on your cloud bill.
Amazon net services (AWS) is a sponsor of the new Stack.
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