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Amazon AWS-Certified-Machine-Learning-Specialty Exam | Exam AWS-Certified-Machine-Learning-Specialty Preview - High-Efficient Test Sample Questions for your AWS-Certified-Machine-Learning-Specialty Preparing
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The Amazon AWS Certified Machine Learning – Specialty certification verifies that you can effectively train, tune, build, and deploy machine learning models using AWS. This certificate validates the professionals’ skills, such as:
- Designing and implementing secure, scalable, cost-optimized & reliable machine learning solutions;
- Identify the correct AWS services to implement machine learning solutions.
- Selecting and justifying the correct machine learning (ML) approach for a specific business problem;
To obtain Amazon AWS Certified Machine Learning – Specialty, the applicants must pass the MLS-C01 exam.
Preparation Process
Many useful resources are available for the Amazon MLS-C01 exam. Let’s take a closer look at them.
- Discussion Forums
Besides the official training options offered by the vendor, many websites provide the candidates with the topic details and useful information related to the exam as well as the certification. Some of these sites include Quora and ExamLabs.com.
- AWS Machine Learning Documentation
The official AWS Machine Learning Documentation is one of the best preparation guides for the Amazon AWS Certified Machine Learning – Specialty exam. It is a great material to learn all the topics included in the exam content. You can find documentation, such as splitting data, data transformation, as well as types of data.
- AWS Training Courses
The vendor offers the official Amazon AWS Machine Learning Certification Training, which you can access by visiting the Amazon website. It is good to know that these training courses are usually available as free.
Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q204-Q209):
NEW QUESTION # 204
A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults. The model analyzes new loan applications and predicts the risk of loan default. To train the model, the data scientist manually extracted loan data from a database. The data scientist performed the model training and deployment steps in a Jupyter notebook that is hosted on SageMaker Studio notebooks.
The model's prediction accuracy is decreasing over time. Which combination of slept in the MOST operationally efficient way for the data scientist to maintain the model's accuracy? (Select TWO.)
- A. Use SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the model, and deploys a new version of the model.
- B. Rerun the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model.
- C. Export the training and deployment code from the SageMaker Studio notebooks into a Python script.Package the script into an Amazon Elastic Container Service (Amazon ECS) task that an AWS Lambda function can initiate.
- D. Configure SageMaker Model Monitor with an accuracy threshold to check for model drift. Initiate an Amazon CloudWatch alarm when the threshold is exceeded. Connect the workflow in SageMaker Pipelines with the CloudWatch alarm to automatically initiate retraining.
- E. Store the model predictions in Amazon S3 Create a daily SageMaker Processing job that reads the predictions from Amazon S3, checks for changes in model prediction accuracy, and sends an email notification if a significant change is detected.
Answer: A,D
Explanation:
* Option A is correct because SageMaker Pipelines is a service that enables you to create and manage automated workflows for your machine learning projects. You can use SageMaker Pipelines to orchestrate the steps of data extraction, model training, and model deployment in a repeatable and scalable way1.
* Option B is correct because SageMaker Model Monitor is a service that monitors the quality of your models in production and alerts you when there are deviations in the model quality. You can use SageMaker Model Monitor to set an accuracy threshold for your model and configure a CloudWatch alarm that triggers when the threshold is exceeded. You can then connect the alarm to the workflow in SageMaker Pipelines to automatically initiate retraining and deployment of a new version of the model2.
* Option C is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Creating a daily SageMaker Processing job that reads the predictions from Amazon S3 and checks for changes in model prediction accuracy is a manual and time-consuming process. It also requires you to write custom code to perform the data analysis and send the email notification.
Moreover, it does not automatically retrain and deploy the model when the accuracy drops.
* Option D is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Rerunning the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model is a manual and error-prone process. It also requires you to monitor the model's performance and initiate the retraining and deployment steps yourself. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.
* Option E is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Exporting the training and deployment code from the SageMaker Studio notebooks into a Python script and packaging the script into an Amazon ECS task that an AWS Lambda function can initiate is a complex and cumbersome process. It also requires you to manage the infrastructure and resources for the Amazon ECS task and the AWS Lambda function. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.
References:
* 1: SageMaker Pipelines - Amazon SageMaker
* 2: Monitor data and model quality - Amazon SageMaker
NEW QUESTION # 205
A developer at a retail company is creating a daily demand forecasting model. The company stores the historical hourly demand data in an Amazon S3 bucket. However, the historical data does not include demand data for some hours.
The developer wants to verify that an autoregressive integrated moving average (ARIMA) approach will be a suitable model for the use case.
How should the developer verify the suitability of an ARIMA approach?
- A. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Resample data by using the aggregate daily total. Perform a Seasonal Trend decomposition.
- B. Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Impute missing hourly values. Choose ARIMA as the machine learning (ML) problem. Check the model performance.
- C. Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Choose ARIMA as the machine learning (ML) problem. Check the model performance.
- D. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Impute hourly missing data. Perform a Seasonal Trend decomposition.
Answer: D
Explanation:
The best solution to verify the suitability of an ARIMA approach is to use Amazon SageMaker Data Wrangler. Data Wrangler is a feature of SageMaker Studio that provides an end-to-end solution for importing, preparing, transforming, featurizing, and analyzing data. Data Wrangler includes built-in analyses that help generate visualizations and data insights in a few clicks. One of the built-in analyses is the Seasonal-Trend decomposition, which can be used to decompose a time series into its trend, seasonal, and residual components. This analysis can help the developer understand the patterns and characteristics of the time series, such as stationarity, seasonality, and autocorrelation, which are important for choosing an appropriate ARIMA model. Data Wrangler also provides built-in transformations that can help the developer handle missing data, such as imputing with mean, median, mode, or constant values, or dropping rows with missing values. Imputing missing data can help avoid gaps and irregularities in the time series, which can affect the ARIMA model performance. Data Wrangler also allows the developer to export the prepared data and the analysis code to various destinations, such as SageMaker Processing, SageMaker Pipelines, or SageMaker Feature Store, for further processing and modeling.
The other options are not suitable for verifying the suitability of an ARIMA approach. Amazon SageMaker Autopilot is a feature-set that automates key tasks of an automatic machine learning (AutoML) process. It explores the data, selects the algorithms relevant to the problem type, and prepares the data to facilitate model training and tuning. However, Autopilot does not support ARIMA as a machine learning problem type, and it does not provide any visualization or analysis of the time series data. Resampling data by using the aggregate daily total can reduce the granularity and resolution of the time series, which can affect the ARIMA model accuracy and applicability.
References:
* Analyze and Visualize
* Transform and Export
* Amazon SageMaker Autopilot
* ARIMA Model - Complete Guide to Time Series Forecasting in Python
NEW QUESTION # 206
An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted. The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models.
During the model evaluation, the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images.
Which of the following should be used to resolve this issue? (Choose two.)
- A. Add vanishing gradient to the model.
- B. Add L2 regularization to the model.
- C. Use gradient checking in the model.
- D. Make the neural network architecture complex.
- E. Perform data augmentation on the training data.
Answer: C,E
NEW QUESTION # 207
A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data Which AWS service should the Data Scientist use?
- A. Amazon Athena
- B. Amazon QuickSight
- C. AWS Glue
- D. Amazon Redshift Spectrum
Answer: A
Explanation:
Amazon Athena is a serverless interactive query service that can analyze data in Amazon S3 using standard SQL. Amazon Athena can also query data from other sources, such as MySQL and Amazon Redshift, by using federated queries. Federated queries allow Amazon Athena to run SQL queries across data sources, such as relational and non-relational databases, data warehouses, and data lakes. By using Amazon Athena, the Data Scientist can perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon Redshift, and then calculating the average of a few selected columns from the joined data. Amazon Athena can also integrate with other AWS services, such as AWS Glue and Amazon QuickSight, to provide additional features, such as data cataloging and visualization.
References:
What is Amazon Athena? - Amazon Athena
Federated Query Overview - Amazon Athena
Querying Data from Amazon S3 - Amazon Athena
Querying Data from MySQL - Amazon Athena
[Querying Data from Amazon Redshift - Amazon Athena]
NEW QUESTION # 208
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. Use the SageMaker batch transform feature
- B. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.
- C. Compress the training data into Apache Parquet format.
- D. Ensure that the input mode for the training job is set to Pipe.
Answer: C
NEW QUESTION # 209
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