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AWS Certified Machine Learning - Specialty Exam, also known as AWS-Certified-Machine-Learning-Specialty, is a certification exam offered by Amazon Web Services. AWS-Certified-Machine-Learning-Specialty exam is designed for individuals who want to validate their expertise in designing, implementing, and deploying machine learning solutions using AWS services.
To be eligible for the Amazon MLS-C01 exam, candidates must have a minimum of one year of experience using AWS services for machine learning solutions. They should also have a strong understanding of machine learning concepts and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning. Additionally, candidates should have experience with AWS services such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend. AWS-Certified-Machine-Learning-Specialty Exam consists of 65 multiple-choice and multiple-response questions, and the time limit for completing it is 180 minutes. Upon successful completion of the exam, candidates will receive the AWS Certified Machine Learning - Specialty certification.
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NEW QUESTION # 175
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?
Answer: A
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 # 176
A data scientist stores financial datasets in Amazon S3. The data scientist uses Amazon Athena to query the datasets by using SQL.
The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model. The data scientist wants to obtain inferences from the model at the SageMaker endpoint However, when the data .... ntist attempts to invoke the SageMaker endpoint, the data scientist receives SOL statement failures The data scientist's 1AM user is currently unable to invoke the SageMaker endpoint Which combination of actions will give the data scientist's 1AM user the ability to invoke the SageMaker endpoint? (Select THREE.)
Answer: C,D,E
Explanation:
The correct combination of actions to enable the data scientist's IAM user to invoke the SageMaker endpoint is B, C, and E, because they ensure that the IAM user has the necessary permissions, access, and syntax to query the ML model from Athena. These actions have the following benefits:
* B: Including a policy statement for the IAM user that allows the sagemaker:InvokeEndpoint action grants the IAM user the permission to call the SageMaker Runtime InvokeEndpoint API, which is used to get inferences from the model hosted at the endpoint1.
* C: Including an inline policy for the IAM user that allows SageMaker to read S3 objects enables the IAM user to access the data stored in S3, which is the source of the Athena queries2.
* E: Including the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in the Athena SQL query allows the IAM user to invoke the ML model as an external function from Athena, which is a feature that enables querying ML models from SQL statements3.
The other options are not correct or necessary, because they have the following drawbacks:
* A: Attaching the AmazonAthenaFullAccess AWS managed policy to the user identity is not sufficient, because it does not grant the IAM user the permission to invoke the SageMaker endpoint, which is required to query the ML model4.
* D: Including a policy statement for the IAM user that allows the IAM user to perform the sagemaker:
GetRecord action is not relevant, because this action is used to retrieve a single record from a feature group, which is not the case in this scenario5.
* F: Performing a user remapping in SageMaker to map the IAM user to another IAM user that is on the hosted endpoint is not applicable, because this feature is only available for multi-model endpoints, which are not used in this scenario.
References:
* 1: InvokeEndpoint - Amazon SageMaker
* 2: Querying Data in Amazon S3 from Amazon Athena - Amazon Athena
* 3: Querying machine learning models from Amazon Athena using Amazon SageMaker | AWS Machine Learning Blog
* 4: AmazonAthenaFullAccess - AWS Identity and Access Management
* 5: GetRecord - Amazon SageMaker Feature Store Runtime
* : [Invoke a Multi-Model Endpoint - Amazon SageMaker]
NEW QUESTION # 177
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.
What technique should be used to convert this column to binary values.
Answer: B
NEW QUESTION # 178
A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a catalog of the metadata concerning the datasets. The solution should require the least amount of setup and maintenance.
Which solution will allow the company to achieve its goals?
Answer: C
Explanation:
AWS Glue is the correct answer because this option requires the least amount of setup and maintenance since it is serverless, and it does not require management of the infrastructure. A, C, and D are all solutions that can solve the problem, but require more steps for configuration, and require higher operational overhead to run and maintain.
NEW QUESTION # 179
A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.
How should the data scientist transform the data?
Answer: B
Explanation:
Explanation
Amazon Forecast requires the input data to be in a specific format. The data scientist should use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. The target time series dataset should contain the timestamp, item_id, and demand columns, while the item metadata dataset should contain the item_id, category, and lead_time columns. Both datasets should be uploaded as .csv files to Amazon S3 . References:
How Amazon Forecast Works - Amazon Forecast
Choosing Datasets - Amazon Forecast
NEW QUESTION # 180
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