RELIABLE MLS-C01 TEST FORUM, VALID MLS-C01 TEST PREP

Reliable MLS-C01 Test Forum, Valid MLS-C01 Test Prep

Reliable MLS-C01 Test Forum, Valid MLS-C01 Test Prep

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The AWS Certified Machine Learning - Specialty certification exam covers a variety of topics, including data engineering, data preprocessing, modeling, deep learning, and deployment. Candidates will be tested on their ability to understand and use various AWS services, such as Amazon SageMaker, AWS Lambda, AWS Glue, and AWS Kinesis, among others. They will also need to demonstrate their expertise in designing and implementing machine learning algorithms, as well as their ability to troubleshoot and optimize machine learning models.

Amazon MLS-C01 Certification Exam is a comprehensive exam that covers a wide range of machine learning topics, including data preparation, feature engineering, model selection, and deployment. It also tests the candidate's ability to work with AWS services such as Amazon SageMaker, AWS Deep Learning AMIs, and Amazon EMR. MLS-C01 exam consists of 65 multiple-choice and multiple-response questions and has a duration of 180 minutes.

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MLS-C01 Exam Tests, MLS-C01 Braindumps, MLS-C01 Actual Test

In this version, you don't need an active internet connection to use the MLS-C01 practice test software. This software mimics the style of real test so that users find out pattern of the real test and kill the exam anxiety. PDFTorrent offline practice exam is customizable and users can change questions and duration of AWS Certified Machine Learning - Specialty (MLS-C01) mock tests. All the given practice questions in the desktop software are identical to the AWS Certified Machine Learning - Specialty (MLS-C01) actual test.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q102-Q107):

NEW QUESTION # 102
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs What does the Specialist need to do1?

  • A. Build the Docker container to be NVIDIA-Docker compatible
  • B. Organize the Docker container's file structure to execute on GPU instances.
  • C. Set the GPU flag in the Amazon SageMaker Create TrainingJob request body
  • D. Bundle the NVIDIA drivers with the Docker image

Answer: A

Explanation:
Explanation
To leverage the NVIDIA GPUs on Amazon EC2 P3 instances, the Machine Learning Specialist needs to build the Docker container to be NVIDIA-Docker compatible. NVIDIA-Docker is a tool that enables GPU-accelerated containers to run on Docker. It automatically configures the container to access the NVIDIA drivers and libraries on the host system. The Specialist does not need to bundle the NVIDIA drivers with the Docker image, as they are already installed on the EC2 P3 instances. The Specialist does not need to organize the Docker container's file structure to execute on GPU instances, as this is not relevant for GPU compatibility. The Specialist does not need to set the GPU flag in the Amazon SageMaker Create TrainingJob request body, as this is only required for using Elastic Inference accelerators, not EC2 P3 instances.
References: NVIDIA-Docker, Using GPU-Accelerated Containers, Using Elastic Inference in Amazon SageMaker


NEW QUESTION # 103
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 # 104
A machine learning (ML) engineer is integrating a production model with a customer metadata repository for real-time inference. The repository is hosted in Amazon SageMaker Feature Store. The engineer wants to retrieve only the latest version of the customer metadata record for a single customer at a time.
Which solution will meet these requirements?

  • A. Use the SageMaker Feature Store BatchGetRecord API with the record identifier. Filter to find the latest record.
  • B. Use the SageMaker Feature Store GetRecord API with the record identifier.
  • C. Create an Amazon Athena query to retrieve the data from the feature table.
  • D. Create an Amazon Athena query to retrieve the data from the feature table. Use the write_time value to find the latest record.

Answer: B

Explanation:
To retrieve only the latest version of a record in real-time for a single entity (like a customer), the correct method is to use the GetRecord API provided by SageMaker Feature Store.
"Use the GetRecord API to retrieve the latest feature values for a specific record identifier from the online store. This operation returns the latest version of the record only." This is optimal for low-latency, single-record lookups, such as real-time inference scenarios. BatchGetRecord is used for multiple records, and Athena queries are for offline/batch analysis and not suited for real-time access.


NEW QUESTION # 105
A retail company uses a machine learning (ML) model for daily sales forecasting. The company's brand manager reports that the model has provided inaccurate results for the past 3 weeks.
At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company's ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies.
What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?

  • A. Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.
  • B. Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.
  • C. Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.
  • D. Create a line chart with the weekly mean absolute error (MAE) of the model.

Answer: B

Explanation:
Explanation
The best way to visualize the model's degradation is to create a histogram of the model errors over the last 3 weeks and compare it with a histogram of the model errors from before that period. A histogram is a graphical representation of the distribution of numerical data. It shows how often each value or range of values occurs in the data. A model error is the difference between the actual value and the predicted value. A high model error indicates a poor fit of the model to the data. By comparing the histograms of the model errors, the ML team can see if there is a significant change in the shape, spread, or center of the distribution. This can indicate if the model is underfitting, overfitting, or drifting from the data. A line chart or a scatter plot would not be as effective as a histogram for this purpose, because they do not show the distribution of the errors. A line chart would only show the trend of the errors over time, which may not capture the variability or outliers. A scatter plot would only show the relationship between the errors and another variable, such as daily sales, which may not be relevant or informative for the model's performance. References:
Histogram - Wikipedia
Model error - Wikipedia
SageMaker Model Monitor - visualizing monitoring results


NEW QUESTION # 106
A large consumer goods manufacturer has the following products on sale
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched Which solution should a Machine Learning Specialist apply?

  • A. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
  • B. Train a custom ARIMA model to forecast demand for the new product.
  • C. Train a custom XGBoost model to forecast demand for the new product
  • D. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product

Answer: D

Explanation:
Explanation
The company wants to predict the demand for a new product that will soon be launched, based on the sales history of similar products. This is a time series forecasting problem, which requires a machine learning algorithm that can learn from historical data and generate future predictions.
One of the most suitable solutions for this problem is to use the Amazon SageMaker DeepAR algorithm, which is a supervised learning algorithm for forecasting scalar time series using recurrent neural networks (RNN). DeepAR can handle multiple related time series, such as the sales of different products, and learn a global model that captures the common patterns and trends across the time series.
DeepAR can also generate probabilistic forecasts that provide confidence intervals and quantify the uncertainty of the predictions.
DeepAR can outperform traditional forecasting methods, such as ARIMA, especially when the dataset contains hundreds or thousands of related time series. DeepAR can also use the trained model to forecast the demand for new products that are similar to the ones it has been trained on, by using the categorical features that encode the product attributes. For example, the company can use the product type, brand, flavor, size, and price as categorical features to group the products and learn the typical behavior for each group.
Therefore, the Machine Learning Specialist should apply the Amazon SageMaker DeepAR algorithm to forecast the demand for the new product, by using the sales history of the existing products as the training dataset, and the product attributes as the categorical features.
References:
DeepAR Forecasting Algorithm - Amazon SageMaker
Now available in Amazon SageMaker: DeepAR algorithm for more accurate time series forecasting


NEW QUESTION # 107
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