EXAMCOLLECTION MLS-C01 VCE, BRAINDUMP MLS-C01 FREE

Examcollection MLS-C01 Vce, Braindump MLS-C01 Free

Examcollection MLS-C01 Vce, Braindump MLS-C01 Free

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Earning the AWS Certified Machine Learning - Specialty certification demonstrates to employers and colleagues that you have the skills and knowledge needed to design and deploy machine learning models on the AWS platform. It can help you stand out in a competitive job market and increase your earning potential.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is an excellent way for individuals to demonstrate their expertise in machine learning on the AWS platform. AWS Certified Machine Learning - Specialty certification is ideal for data scientists, machine learning engineers, and software developers who are looking to advance their career in the field of ML. With the right preparation, candidates can pass the MLS-C01 Exam and earn the AWS Certified Machine Learning - Specialty certification, which is widely recognized as a mark of excellence in the field of ML.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q118-Q123):

NEW QUESTION # 118
A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features. The Marketing team has not provided any insight about which features are relevant for churn prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide gap between the training and validation set accuracy.
Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team's needs? (Choose two.)

  • A. Add features to the dataset
  • B. Add L1 regularization to the classifier
  • C. Perform linear discriminant analysis
  • D. Perform t-distributed stochastic neighbor embedding (t-SNE)
  • E. Perform recursive feature elimination

Answer: B,E

Explanation:
The Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features. The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome. However, the Data Scientist observes that there is a wide gap between the training and validation set accuracy, which indicates that the model is overfitting the data and generalizing poorly to new data.
To improve the model performance and satisfy the Marketing team's needs, the Data Scientist can use the following methods:
Add L1 regularization to the classifier: L1 regularization is a technique that adds a penalty term to the loss function of the logistic regression model, proportional to the sum of the absolute values of the coefficients. L1 regularization can help reduce overfitting by shrinking the coefficients of the less important features to zero, effectively performing feature selection. This can simplify the model and make it more interpretable, as well as improve the validation accuracy.
Perform recursive feature elimination: Recursive feature elimination (RFE) is a feature selection technique that involves training a model on a subset of the features, and then iteratively removing the least important features one by one until the desired number of features is reached. The idea behind RFE is to determine the contribution of each feature to the model by measuring how well the model performs when that feature is removed. The features that are most important to the model will have the greatest impact on performance when they are removed. RFE can help improve the model performance by eliminating the irrelevant or redundant features that may cause noise or multicollinearity in the data. RFE can also help the Marketing team understand the direct impact of the relevant features on the model outcome, as the remaining features will have the highest weights in the model.
References:
Regularization for Logistic Regression
Recursive Feature Elimination


NEW QUESTION # 119
A Machine Learning Specialist is working with a large cybersecurity company that manages security events in real time for companies around the world. The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested. The company also wants be able to save the results in its data lake for later processing and analysis.
What is the MOST efficient way to accomplish these tasks?

  • A. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data. Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data.
  • B. Ingest the data into Apache Spark Streaming using Amazon EMR, and use Spark MLlib with k- means to perform anomaly detection. Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake.
  • C. Ingest the data and store it in Amazon S3. Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.
  • D. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection. Then use Kinesis Data Firehose to stream the results to Amazon S3.

Answer: D

Explanation:
https://aws.amazon.com/tw/blogs/machine-learning/use-the-built-in-amazon-sagemaker-random- cut-forest-algorithm-for-anomaly-detection/


NEW QUESTION # 120
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes.
What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

  • A. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
  • B. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
  • C. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
  • D. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

Answer: A


NEW QUESTION # 121
A company has an ecommerce website with a product recommendation engine built in TensorFlow. The recommendation engine endpoint is hosted by Amazon SageMaker. Three compute-optimized instances support the expected peak load of the website.
Response times on the product recommendation page are increasing at the beginning of each month. Some users are encountering errors. The website receives the majority of its traffic between 8 AM and 6 PM on weekdays in a single time zone.
Which of the following options are the MOST effective in solving the issue while keeping costs to a minimum? (Choose two.)

  • A. Configure the endpoint to use Amazon Elastic Inference (EI) accelerators.
  • B. Reconfigure the endpoint to use burstable instances.
  • C. Configure the endpoint to automatically scale with the Invocations Per Instance metric.
  • D. Create a new endpoint configuration with two production variants.
  • E. Deploy a second instance pool to support a blue/green deployment of models.

Answer: A,C

Explanation:
Explanation
The solution A and C are the most effective in solving the issue while keeping costs to a minimum. The solution A and C involve the following steps:
Configure the endpoint to use Amazon Elastic Inference (EI) accelerators. This will enable the company to reduce the cost and latency of running TensorFlow inference on SageMaker. Amazon EI provides GPU-powered acceleration for deep learning models without requiring the use of GPU instances. Amazon EI can attach to any SageMaker instance type and provide the right amount of acceleration based on the workload1.
Configure the endpoint to automatically scale with the Invocations Per Instance metric. This will enable the company to adjust the number of instances based on the demand and traffic patterns of the website.
The Invocations Per Instance metric measures the average number of requests that each instance processes over a period of time. By using this metric, the company can scale out the endpoint when the load increases and scale in when the load decreases. This can improve the response time and availability of the product recommendation engine2.
The other options are not suitable because:
Option B: Creating a new endpoint configuration with two production variants will not solve the issue of increasing response time and errors. Production variants are used to split the traffic between different models or versions of the same model. They can be useful for testing, updating, or A/B testing models. However, they do not provide any scaling or acceleration benefits for the inference workload3.
Option D: Deploying a second instance pool to support a blue/green deployment of models will not solve the issue of increasing response time and errors. Blue/green deployment is a technique for updating models without downtime or disruption. It involves creating a new endpoint configuration with a different instance pool and model version, and then shifting the traffic from the old endpoint to the new endpoint gradually. However, this technique does not provide any scaling or acceleration benefits for the inference workload4.
Option E: Reconfiguring the endpoint to use burstable instances will not solve the issue of increasing response time and errors. Burstable instances are instances that provide a baseline level of CPU performance with the ability to burst above the baseline when needed. They can be useful for workloads that have moderate CPU utilization and occasional spikes. However, they are not suitable for workloads that have high and consistent CPU utilization, such as the product recommendation engine. Moreover, burstable instances may incur additional charges when they exceed their CPU credits5.
References:
1: Amazon Elastic Inference
2: How to Scale Amazon SageMaker Endpoints
3: Deploying Models to Amazon SageMaker Hosting Services
4: Updating Models in Amazon SageMaker Hosting Services
5: Burstable Performance Instances


NEW QUESTION # 122
A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.
What combination of services should the team use to build a custom algorithm in Amazon SageMaker?
(Choose two.)

  • A. Amazon S3
  • B. AWS CodeStar
  • C. Amazon ECR
  • D. AWS Secrets Manager
  • E. Amazon ECS

Answer: A,C


NEW QUESTION # 123
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