AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY VALID TEST SIMULATOR & AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY PREPAWAY DUMPS

AWS-Certified-Machine-Learning-Specialty Valid Test Simulator & AWS-Certified-Machine-Learning-Specialty Prepaway Dumps

AWS-Certified-Machine-Learning-Specialty Valid Test Simulator & AWS-Certified-Machine-Learning-Specialty Prepaway Dumps

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Getting the Results

The minimum passing score for this test is 750 marks. The result will be reported on a scale of 100-1000. Note that there might be some unscored items in the exam that are not identified but don't affect your score. Also, always keep in mind that you don't need to succeed in each section to get a pass status and obtain the certification — only a total amount of points matters. The report is only needed to show individuals their performance in each domain and help them identify what are their weak and strong areas of Machine Learning.

The AWS-Certified-Machine-Learning-Specialty Exam is a challenging certification exam aimed at validating the skills and knowledge of individuals who want to design, implement, and deploy machine learning solutions using AWS services. Candidates who pass AWS-Certified-Machine-Learning-Specialty exam can demonstrate their expertise in machine learning and can enhance their career prospects in the field of data science and machine learning.

>> AWS-Certified-Machine-Learning-Specialty Valid Test Simulator <<

Free PDF Quiz AWS-Certified-Machine-Learning-Specialty - Newest AWS Certified Machine Learning - Specialty Valid Test Simulator

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q104-Q109):

NEW QUESTION # 104
A large JSON dataset for a project has been uploaded to a private Amazon S3 bucket The Machine Learning Specialist wants to securely access and explore the data from an Amazon SageMaker notebook instance A new VPC was created and assigned to the Specialist How can the privacy and integrity of the data stored in Amazon S3 be maintained while granting access to the Specialist for analysis?

  • A. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled. Generate an S3 pre-signed URL for access to data in the bucket
  • B. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Copy the JSON dataset from Amazon S3 into the ML storage volume on the SageMaker notebook instance and work against the local dataset
  • C. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Define a custom S3 bucket policy to only allow requests from your VPC to access the S3 bucket
  • D. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled Use an S3 ACL to open read privileges to the everyone group

Answer: C

Explanation:
Explanation
The best way to maintain the privacy and integrity of the data stored in Amazon S3 is to use a combination of VPC endpoints and S3 bucket policies. A VPC endpoint allows the SageMaker notebook instance to access the S3 bucket without going through the public internet. A bucket policy allows the S3 bucket owner to specify which VPCs or VPC endpoints can access the bucket. This way, the data is protected from unauthorized access and tampering. The other options are either insecure (A and D) or inefficient (B). References: Using Amazon S3 VPC Endpoints, Using Bucket Policies and User Policies


NEW QUESTION # 105
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. 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.
  • B. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data
  • C. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log 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 QuickSight to visualize logs as they are being produced.

Answer: A

Explanation:
https://docs.aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html


NEW QUESTION # 106
An agricultural company is interested in using machine learning to detect specific types of weeds in a 100-acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10 * 10 grids. The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks.
The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints. The model will perform real-time inferencing using the images captured by the cameras.
Which approach should a Machine Learning Specialist take to obtain accurate predictions?

  • A. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
  • B. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
  • C. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.
  • D. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.

Answer: C


NEW QUESTION # 107
A company has a podcast platform that has thousands of users. The company implemented an algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening to.
pausing, and closing the podcast. A machine learning (ML) specialist is designing the ingestion process for these events. The ML specialist needs to transform the data to prepare the data for inference.
How should the ML specialist design the transformation step to meet these requirements with the LEAST operational effort?

  • A. Use Amazon Kinesis Data Streams to ingest event data. Store the data in Amazon S3 by using Amazon Data Firehose. Use AWS Lambda to transform the most recent 10 minutes of data before inference.
  • B. Use Amazon Kinesis Data Streams to ingest event data. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to transform the most recent 10 minutes of data before inference.
  • C. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data.Use AWS Lambda to transform the most recent 10 minutes of data before inference.
  • D. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data.
    Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to transform the most recent 10 minutes of data before inference.

Answer: B

Explanation:
In this scenario, Kinesis Data Streams efficiently ingests real-time event data, while Amazon Managed Service for Apache Flink (formerly Amazon Kinesis Data Analytics) is ideal for transforming and analyzing data in a continuous stream. Apache Flink allows processing of time-based windows, such as the 10-minute sliding window required here, with low operational overhead.
This combination provides an effective solution for low-latency data processing and transformation, meeting the requirements for preparing data for inference with minimal setup and serverless scalability.


NEW QUESTION # 108
The displayed graph is from a foresting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

  • A. The model predicts the trend well, but not the seasonality.
  • B. The model predicts both the trend and the seasonality well.
  • C. The model predicts the seasonality well, but not the trend.
  • D. The model does not predict the trend or the seasonality well.

Answer: D


NEW QUESTION # 109
......

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