Exam AWS Certified Data Analytics - Specialty topic 1 question 28 discussion

It is B based on this link: https://docs.aws.amazon.com/quicksight/latest/user/making-data-driven-decisions-with-ml-in-quicksight.html

The correct answer is C. Here is the explanation: Use a pre-build ML AMI from the AWS Marketplace to create forecasts and then use Amazon QuickSight to visualize the data. This is the most out-of-the-box solution and will require the least amount of management overhead. AWS Glue ML transforms are a great way to automate ML tasks, but they require some technical expertise to set up and use. Amazon QuickSight is a great visualization tool, but it does not have built-in ML capabilities. Calculated fields are a way to create new fields in a data set, but they cannot be used to create forecasts.

Quicksight supports RCF and can connect easily to multiple data sources (S3, JDBC ..etc)

Quicksight uses a built-in version of RCF

B: I passed the test

this scenario is shocking to see because the team is all non-technical ....

Both options B and C involve using Amazon QuickSight to visualize the data. However, option C involves using a pre-built machine learning Amazon Machine Image (AMI) from the AWS Marketplace to create forecasts, which may require more technical expertise to set up and manage than option B, which simply involves using ML-powered forecasting within Amazon QuickSight. Therefore, option B may be more suitable for a non-technical team looking for an out-of-the-box solution with minimal management overhead.

Amazon QuickSight uses a built-in version of the Random Cut Forest (RCF) algorithm. B is correct

Option C is the best solution for this scenario. The company wants an out-of-the-box solution that requires the least amount of management overhead, and using a pre-built ML AMI from the AWS Marketplace to create forecasts and then using Amazon QuickSight to visualize the data is the most straightforward approach. The pre-built ML AMI will provide the Random Cut Forest algorithm for the team to use, and Amazon QuickSight provides an easy-to-use interface for data visualization. This solution will require minimal technical expertise and management overhead from the non-technical team. Option B is not the best solution as using ML-powered forecasting in Amazon QuickSight does not provide the Random Cut Forest algorithm that the company wants to use.

B, Quicksigth has many ML tools.

Correct answer is B as QuickSight ML provides an out-of-the-box ML Random Cut Forest (RCF) algorithm to help visualize complex real-world scenarios, such as detecting seasonality and trends, excluding outliers, and imputing missing values. Options A, C & D are wrong as they do not come with the least operational overhead.

B is correct. Amazon QuickSight uses a built-in version of the Random Cut Forest (RCF) algorithm. The following sections explain what that means and how it is used in Amazon QuickSight. First, let's look at some of the terminology involved: Anomaly – Something that is characterized by its difference from the majority of the other things in the same sample. Also known as an outlier, an exception, a deviation, and so on. Data point – A discrete unit—or simply put, a row—in a dataset. However, a row can have multiple data points if you use a measure over different dimensions. Decision Tree – A way of visualizing the decision process of the algorithm that evaluates patterns in the data. Forecast – A prediction of future behavior based on current and past behavior. Model – A mathematical representation of the algorithm or what the algorithm learns. Seasonality – The repeating patterns of behavior that occur cyclically in time series data. Time series – An ordered set of date or time data in one field or column.

B. Amazon QuickSight enables nontechnical users to confidently forecast their key business metrics. The built-in ML Random Cut Forest algorithm automatically handles complex real-world scenarios such as detecting seasonality and trends, excluding outliers, and imputing missing values. You can interact with the data with point-and-click simplicity.

it is B https://docs.aws.amazon.com/quicksight/latest/user/concept-of-ml-algorithms.html

B is the answer.

Answer should be B

Aggree with B. But one problem is, the question mentions about "inputing missing data". Can quicksight handle missing data for ML related visualization?