EXAM MLS-C01 SIMULATOR & REAL MLS-C01 EXAM DUMPS

Exam MLS-C01 Simulator & Real MLS-C01 Exam Dumps

Exam MLS-C01 Simulator & Real MLS-C01 Exam Dumps

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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.

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Topics in AWS Certified Machine Learning - Specialty

The following will be discussed in AMAZON MLS-C01 Practice Exam and AMAZON MLS-C01 practice exams:

  • Data Engineering
  • Modeling
  • Machine Learning Implementation and Operations
  • Exploratory Data Analysis

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q139-Q144):

NEW QUESTION # 139
A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model What should the Specialist do to address the performance issues with the current solution?

  • A. Ensure that the input mode for the training job is set to Pipe.
  • B. Use the SageMaker batch transform feature
  • C. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.
  • D. Compress the training data into Apache Parquet format.

Answer: A

Explanation:
Explanation
The input mode for the training job determines how the training data is transferred from Amazon S3 to the SageMaker instance. There are two input modes: File and Pipe. File mode copies the entire training dataset from S3 to the local file system of the instance before starting the training job. This can cause a long delay before the training job launches, especially if the dataset is large. Pipe mode streams the data from S3 to the instance as the training job runs. This can reduce the startup time and improve the I/O throughput, as the data is read in smaller batches. Therefore, to address the performance issues with the current solution, the Specialist should ensure that the input mode for the training job is set to Pipe. This can be done by using the SageMaker Python SDK and setting the input_mode parameter to Pipe when creating the estimator or the fit method12. Alternatively, this can be done by using the AWS CLI and setting the InputMode parameter to Pipe when creating the training job3.
References:
Access Training Data - Amazon SageMaker
Choosing Data Input Mode Using the SageMaker Python SDK - Amazon SageMaker CreateTrainingJob - Amazon SageMaker Service


NEW QUESTION # 140
A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.
Which storage option provides the most processing flexibility and will allow access control with IAM?

  • A. Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
  • B. Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.
  • C. Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.
  • D. Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.

Answer: B

Explanation:
Explanation
The best storage option for the trucking company is to use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies. A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Amazon S3 is the ideal choice for building a data lake because it offers high durability, scalability, availability, and security. You can store any type of data in Amazon S3, such as images, videos, audio, text, etc. You can also use AWS services such as Amazon Rekognition, Amazon SageMaker, and Amazon EMR to analyze and process the data in the data lake. To ensure the data is only accessible to specific IAM users, you can use bucket policies to grant or deny access to the S3 buckets based on the IAM user's identity or role. Bucket policies are JSON documents that specify the permissions for the bucket and the objects in it. You can use conditions to restrict access based on various factors, such as IP address, time, source, etc. By using bucket policies, you can control who can access the data in the data lake and what actions they can perform on it.
References:
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Build a Data Lake Foundation with Amazon S3 AWS Machine Learning Training - Using Bucket Policies and User Policies


NEW QUESTION # 141
A Machine Learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar- sized datasets. The team's leaders need to accelerate the training process.
What can a Machine Learning Specialist do to address this concern?

  • A. Use Amazon SageMaker Pipe mode.
  • B. Use AWS Glue to transform the CSV dataset to the JSON format.
  • C. Use Amazon Machine Learning to train the models.
  • D. Use Amazon Kinesis to stream the data to Amazon SageMaker.

Answer: A

Explanation:
Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure


NEW QUESTION # 142
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?

  • A. Blue/green deployment
  • B. Shadow deployment
  • C. A/B testing
  • D. Canary release

Answer: B

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 # 143
Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?

  • A. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
  • B. Initialize the model with random weights in all layers and replace the last fully connected layer.
  • C. Initialize the model with pre-trained weights in all layers including the last fully connected layer.
  • D. Initialize the model with random weights in all layers including the last fully connected layer.

Answer: A


NEW QUESTION # 144
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