There is a high demand for Amazon Development certification, therefore there is an increase in the number of Amazon MLS-C01 exam candidates. Many resources are available on the internet to prepare for the AWS Certified Machine Learning - Specialty exam. Dumpkiller is one of the best certification exam preparation material providers where you can find newly released Amazon MLS-C01 Dumps for your exam preparation. With years of experience in compiling top-notch relevant Amazon MLS-C01 dumps questions, we also offer the Amazon MLS-C01 practice test (online and offline) to help you get familiar with the actual exam environment.
Amazon MLS-C01 actual test questions have effective high-quality content and cover many the real test questions. Amazon MLS-C01 study guide is the best product to help you achieve your goal. If you pass exam and obtain a certification with our Amazon MLS-C01 Study Materials, you can apply for satisfied jobs in the large enterprise and run for senior positions with high salary and high benefits.
Our MLS-C01 preparation exam is compiled specially for it with all contents like exam questions and answers from the real MLS-C01 exam. If you make up your mind of our MLS-C01 exam prep, we will serve many benefits like failing the first time attached with full refund service, protecting your interests against any kinds of loss. In a word, you have nothing to worry about with our MLS-C01 Study Guide.
NEW QUESTION # 294
An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?
Answer: D
Explanation:
https://worldwidescience.org/topicpages/i/imputing+missing+values.html
NEW QUESTION # 295
A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible.
How can the ML team solve this issue?
Answer: B
Explanation:
Explanation
The correct solution for changing the scaling behavior of the SageMaker instances is to increase the cooldown period for the scale-out activity. The cooldown period is the amount of time, in seconds, after a scaling activity completes before another scaling activity can start. By increasing the cooldown period for the scale-out activity, the ML team can ensure that the new instances are ready before launching additional instances. This will prevent over-scaling and reduce costs1 The other options are incorrect because they either do not solve the issue or require unnecessary steps. For example:
Option A decreases the cooldown period for the scale-in activity and increases the configured maximum capacity of instances. This option does not address the issue of launching additional instances before the new instances are ready. It may also cause under-scaling and performance degradation.
Option B replaces the current endpoint with a multi-model endpoint using SageMaker. A multi-model endpoint is an endpoint that can host multiple models using a single endpoint. It does not affect the scaling behavior of the SageMaker instances. It also requires creating a new endpoint and updating the application code to use it2 Option C sets up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.
Amazon API Gateway is a service that allows users to create, publish, maintain, monitor, and secure APIs. AWS Lambda is a service that lets users run code without provisioning or managing servers.
These services do not affect the scaling behavior of the SageMaker instances. They also require creating and configuring additional resources and services34 References:
1: Automatic Scaling - Amazon SageMaker
2: Create a Multi-Model Endpoint - Amazon SageMaker
3: Amazon API Gateway - Amazon Web Services
4: AWS Lambda - Amazon Web Services
NEW QUESTION # 296
A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files.
Each record contains 200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only.
How should the Machine Learning Specialist transform the dataset to minimize query runtime?
Answer: D
Explanation:
Using compressions will reduce the amount of data scanned by Amazon Athena, and also reduce your S3 bucket storage. It's a Win-Win for your AWS bill. Supported formats: GZIP, LZO, SNAPPY (Parquet) and ZLIB.
https://www.cloudforecast.io/blog/using-parquet-on-athena-to-save-money-on-aws/
NEW QUESTION # 297
An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production. The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests.
Which one model can serve user requests at a time. The company must measure the performance of the new experimental model without affecting the current live traffic Which solution will meet these requirements?
Answer: A
Explanation:
The best solution for this scenario is to use shadow deployment, which is a technique that allows the company to run the new experimental model in parallel with the existing model, without exposing it to the end users. In shadow deployment, the company can route the same user requests to both models, but only return the responses from the existing model to the users. The responses from the new experimental model are logged and analyzed for quality and performance metrics, such as accuracy, latency, and resource consumption12. This way, the company can validate the new experimental model in a production environment, without affecting the current live traffic or user experience.
The other solutions are not suitable, because they have the following drawbacks:
A: A/B testing is a technique that involves splitting the user traffic between two or more models, and comparing their outcomes based on predefined metrics. However, this technique exposes the new experimental model to a portion of the end users, which might affect their experience if the model is not reliable or consistent with the existing model3.
B: Canary release is a technique that involves gradually rolling out the new experimental model to a small subset of users, and monitoring its performance and feedback. However, this technique also exposes the new experimental model to some end users, and requires careful selection and segmentation of the user groups4.
D: Blue/green deployment is a technique that involves switching the user traffic from the existing model (blue) to the new experimental model (green) at once, after testing and verifying the new model in a separate environment. However, this technique does not allow the company to validate the new experimental model in a production environment, and might cause service disruption or inconsistency if the new model is not compatible or stable5.
References:
1: Shadow Deployment: A Safe Way to Test in Production | LaunchDarkly Blog
2: Shadow Deployment: A Safe Way to Test in Production | LaunchDarkly Blog
3: A/B Testing for Machine Learning Models | AWS Machine Learning Blog
4: Canary Releases for Machine Learning Models | AWS Machine Learning Blog
5: Blue-Green Deployments for Machine Learning Models | AWS Machine Learning Blog
NEW QUESTION # 298
A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.
Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Select TWO)
Answer: A,D
Explanation:
The data scientist should use Amazon SageMaker Ground Truth to sort the data into two groups named
"enrolled" or "not enrolled." This will create a labeled dataset that can be used for supervised learning. The data scientist should then use a classification algorithm to run predictions on the test data. A classification algorithm is a suitable choice for predicting a binary outcome, such as enrollment status, based on the input features, such as academic performance. A classification algorithm will output a probability for each class label and assign the most likely label to each observation.
References:
* Use Amazon SageMaker Ground Truth to Label Data
* Classification Algorithm in Machine Learning
NEW QUESTION # 299
......
Dumpkiller provides updated and valid Amazon Exam Questions because we are aware of the absolute importance of updates, keeping in mind the dynamic AWS Certified Machine Learning - Specialty exam syllabus. We provide you update checks for 1 year after purchase for absolutely no cost. We also give a 30% discount on all Amazon MLS-C01 Dumps.
Dump MLS-C01 Check: https://www.dumpkiller.com/MLS-C01_braindumps.html
The accuracy rate of MLS-C01 training material is very high, so you only need to use the training material that guarantees you will pass the exam with ease, In order to improve the MLS-C01 passing score of our candidates, we take every step to improve our profession and check the updating of MLS-C01 pass guide, However, when asked whether the MLS-C01 latest dumps are reliable, costumers may be confused.
jQuery Mobile offers a way to do this via the `$.mobile` MLS-C01 object's `loadPage` method, Now consider the other side of the equation, The accuracy rate of MLS-C01 training material is very high, so you only need to use the training material that guarantees you will pass the exam with ease.
In order to improve the MLS-C01 Passing Score of our candidates, we take every step to improve our profession and check the updating of MLS-C01 pass guide.
However, when asked whether the MLS-C01 latest dumps are reliable, costumers may be confused, Boring life will wear down your passion for life, Our company hired the top experts in each qualification examination field to write the MLS-C01 prepare materials, so as to ensure that our products have a very high quality, so that users can rest assured that the use of our research materials.