NEW AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY EXAM ANSWERS, AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY LATEST EXAM CRAM

New AWS-Certified-Machine-Learning-Specialty Exam Answers, AWS-Certified-Machine-Learning-Specialty Latest Exam Cram

New AWS-Certified-Machine-Learning-Specialty Exam Answers, AWS-Certified-Machine-Learning-Specialty Latest Exam Cram

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To prepare for the AWS-Certified-Machine-Learning-Specialty exam, candidates are advised to take online courses, read books, and practice with sample questions. Amazon provides a range of training materials and resources to help candidates prepare for the exam. Candidates can also find a range of third-party resources, including study guides, practice exams, and online courses, to help them prepare for the exam.

Amazon MLS-C01 certification exam consists of 65 multiple-choice and multiple-response questions and has a duration of 180 minutes. It is a challenging exam that requires extensive knowledge and experience in machine learning concepts and technologies. Candidates are required to have a thorough understanding of AWS services and how to use them to build and deploy machine learning models.

The AWS Certified Machine Learning - Specialty exam is a rigorous and challenging test that requires a deep understanding of machine learning concepts and their applications on the AWS platform. Candidates are expected to have a strong foundation in mathematics, statistics, and computer science, as well as hands-on experience in building and deploying machine learning models. To succeed in AWS-Certified-Machine-Learning-Specialty Exam, candidates should also be familiar with AWS services such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q169-Q174):

NEW QUESTION # 169
Each morning, a data scientist at a rental car company creates insights about the previous day's rental car reservation demands. The company needs to automate this process by streaming the data to Amazon S3 in near real time. The solution must detect high-demand rental cars at each of the company's locations. The solution also must create a visualization dashboard that automatically refreshes with the most recent data.
Which solution will meet these requirements with the LEAST development time?

  • A. Use Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.
  • B. Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.
  • C. Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.
  • D. Use Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.

Answer: B

Explanation:
The solution that will meet the requirements with the least development time is to use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3, detect high-demand outliers by using Amazon QuickSight ML Insights, and visualize the data in QuickSight. This solution does not require any custom development or ML domain expertise, as it leverages the built-in features of QuickSight ML Insights to automatically run anomaly detection and generate insights on the streaming data. QuickSight ML Insights can also create a visualization dashboard that automatically refreshes with the most recent data, and allows the data scientist to explore the outliers and their key drivers. References:
1: Simplify and automate anomaly detection in streaming data with Amazon Lookout for Metrics | AWS Machine Learning Blog
2: Detecting outliers with ML-powered anomaly detection - Amazon QuickSight
3: Real-time Outlier Detection Over Streaming Data - IEEE Xplore
4: Towards a deep learning-based outlier detection ... - Journal of Big Data


NEW QUESTION # 170
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 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.
  • B. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
  • C. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.
  • D. onfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.

Answer: C

Explanation:
https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html


NEW QUESTION # 171
A machine learning (ML) specialist is using the Amazon SageMaker DeepAR forecasting algorithm to train a model on CPU-based Amazon EC2 On-Demand instances. The model currently takes multiple hours to train. The ML specialist wants to decrease the training time of the model.
Which approaches will meet this requirement7 (SELECT TWO )

  • A. Replace On-Demand Instances with Spot Instances
  • B. Replace CPU-based EC2 instances with GPU-based EC2 instances.
  • C. Use a pre-trained version of the model. Run incremental training.
  • D. Configure model auto scaling dynamically to adjust the number of instances automatically.
  • E. Use multiple training instances.

Answer: B,E

Explanation:
The best approaches to decrease the training time of the model are C and D, because they can improve the computational efficiency and parallelization of the training process. These approaches have the following benefits:
C: Replacing CPU-based EC2 instances with GPU-based EC2 instances can speed up the training of the DeepAR algorithm, as it can leverage the parallel processing power of GPUs to perform matrix operations and gradient computations faster than CPUs12. The DeepAR algorithm supports GPU-based EC2 instances such as ml.p2 and ml.p33.
D: Using multiple training instances can also reduce the training time of the DeepAR algorithm, as it can distribute the workload across multiple nodes and perform data parallelism4. The DeepAR algorithm supports distributed training with multiple CPU-based or GPU-based EC2 instances3.
The other options are not effective or relevant, because they have the following drawbacks:
A: Replacing On-Demand Instances with Spot Instances can reduce the cost of the training, but not necessarily the time, as Spot Instances are subject to interruption and availability5. Moreover, the DeepAR algorithm does not support checkpointing, which means that the training cannot resume from the last saved state if the Spot Instance is terminated3.
B: Configuring model auto scaling dynamically to adjust the number of instances automatically is not applicable, as this feature is only available for inference endpoints, not for training jobs6.
E: Using a pre-trained version of the model and running incremental training is not possible, as the DeepAR algorithm does not support incremental training or transfer learning3. The DeepAR algorithm requires a full retraining of the model whenever new data is added or the hyperparameters are changed7.
References:
1: GPU vs CPU: What Matters Most for Machine Learning? | by Louis (What's AI) Bouchard | Towards Data Science
2: How GPUs Accelerate Machine Learning Training | NVIDIA Developer Blog
3: DeepAR Forecasting Algorithm - Amazon SageMaker
4: Distributed Training - Amazon SageMaker
5: Managed Spot Training - Amazon SageMaker
6: Automatic Scaling - Amazon SageMaker
7: How the DeepAR Algorithm Works - Amazon SageMaker


NEW QUESTION # 172
A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test"?

  • A. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.
  • B. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker
  • C. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker
  • D. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced

Answer: B

Explanation:
Amazon CloudWatch is a service that can monitor and collect various metrics and logs from AWS resources, such as Amazon SageMaker. Amazon CloudWatch can also generate dashboards to create a single view for the metrics and logs that are of interest. By using Amazon CloudWatch, the Machine Learning Specialist can review the latency, memory utilization, and CPU utilization during the load test, as these are some of the metrics that are outputted by Amazon SageMaker. The Specialist can create a custom dashboard that displays these metrics in different widgets, such as graphs, tables, or text. The dashboard can also be configured to refresh automatically and show the latest data as the load test is running. This approach will allow the Specialist to monitor the performance and resource utilization of the model variant and adjust the Auto Scaling configuration accordingly.
[Monitoring Amazon SageMaker with Amazon CloudWatch - Amazon SageMaker]
[Using Amazon CloudWatch Dashboards - Amazon CloudWatch]
[Create a CloudWatch Dashboard - Amazon CloudWatch]


NEW QUESTION # 173
A company stores its documents in Amazon S3 with no predefined product categories. A data scientist needs to build a machine learning model to categorize the documents for all the company's products.
Which solution will meet these requirements with the MOST operational efficiency?

  • A. Build a custom clustering model. Create a Dockerfile and build a Docker image. Register the Docker image in Amazon Elastic Container Registry (Amazon ECR). Use the custom image in Amazon SageMaker to generate a trained model.
  • B. Train an Amazon SageMaker Neural Topic Model (NTM) model to generate the product categories.
  • C. Tokenize the data and transform the data into tabulai data. Train an Amazon SageMaker k-means mode to generate the product categories.
  • D. Train an Amazon SageMaker Blazing Text model to generate the product categories.

Answer: B

Explanation:
Amazon SageMaker's Neural Topic Model (NTM) is designed to uncover underlying topics within text data by clustering documents based on topic similarity. For document categorization, NTM can identify product categories by analyzing and grouping the documents, making it an efficient choice for unsupervised learning where predefined categories do not exist.
This model minimizes operational overhead by allowing automated topic categorization directly within SageMaker, which is more straightforward than custom Docker image setups or using k-means clustering for tokenized data.


NEW QUESTION # 174
......

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