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AWS Certified Machine Learning - Specialty cexamkiller Praxis Dumps & MLS-C01 Test Training Überprüfungen

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Amazon AWS Certified Machine Learning - Specialty MLS-C01 Prüfungsfragen mit Lösungen (Q120-Q125):

120. Frage
A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test"?

A. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.
B. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker
C. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker
D. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced

Antwort: B

121. Frage
A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE)

A. Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker
B. Download the AWS SDK for the Spark environment
C. Convert the DataFrame object to a CSV file, and use the CSV file as input for obtaining inferences from SageMaker.
D. Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket.
E. Install the SageMaker Spark library in the Spark environment.
F. Use the appropriate estimator from the SageMaker Spark Library to train a model.

Antwort: A,E,F

Begründung:
The SageMaker Spark library is a library that enables Apache Spark applications to integrate with Amazon SageMaker for training and hosting machine learning models. The library provides several features, such as:
Estimators: Classes that allow Spark users to train Amazon SageMaker models and host them on Amazon SageMaker endpoints using the Spark MLlib Pipelines API. The library supports various built-in algorithms, such as linear learner, XGBoost, K-means, etc., as well as custom algorithms using Docker containers.
Model classes: Classes that wrap Amazon SageMaker models in a Spark MLlib Model abstraction. This allows Spark users to use Amazon SageMaker endpoints for inference within Spark applications.
Data sources: Classes that allow Spark users to read data from Amazon S3 using the Spark Data Sources API. The library supports various data formats, such as CSV, LibSVM, RecordIO, etc.
To integrate the Spark application with SageMaker, the Machine Learning Specialist should do the following:
Install the SageMaker Spark library in the Spark environment. This can be done by using Maven, pip, or downloading the JAR file from GitHub.
Use the appropriate estimator from the SageMaker Spark Library to train a model. For example, to train a linear learner model, the Specialist can use the following code:

Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker. For example, to get predictions for a test DataFrame, the Specialist can use the following code:

References:
[SageMaker Spark]: A documentation page that introduces the SageMaker Spark library and its features.
[SageMaker Spark GitHub Repository]: A GitHub repository that contains the source code, examples, and installation instructions for the SageMaker Spark library.

122. Frage
An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

A. Listwise deletion
B. Multiple imputation
C. Mean substitution
D. Last observation carried forward

Antwort: B

123. Frage
A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome.
The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome ...gones from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years.
The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance.
Which solution will meet these requirements?

A. Perform reinforcement learning by using claim IDs and dates Instruct the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month
B. Perform classification every month by using supervised learning of the 20X3 outcome categories based on claim contents.
C. Perform classification by using supervised learning of the outcome categories for which partial information on claim contents is provided. Perform forecasting by using claim IDs and dates for all other outcome categories.
D. Perform forecasting by using claim IDs and dates to identify the expected number ot claims in each outcome category every month.

Antwort: D

Begründung:
The best solution for this scenario is to perform forecasting by using claim IDs and dates to identify the expected number of claims in each outcome category every month. This solution has the following advantages:
* It leverages the historical data of claim outcomes and dates to capture the temporal patterns and trends of the claims in each category1.
* It does not require the claim contents or any other features to make predictions, which simplifies the data preparation and reduces the impact of missing or incomplete data2.
* It can handle the high cardinality of the outcome categories, as forecasting models can output multiple values for each time point3.
* It can provide predictions for several months in advance, which is useful for planning and budgeting purposes4.
The other solutions have the following drawbacks:
* A: Performing classification every month by using supervised learning of the 200 outcome categories based on claim contents is not suitable, because it assumes that the claim contents are available and complete for all the records, which is not the case in this scenario2. Moreover, classification models usually output a single label for each input, which is not adequate for predicting the number of claims in each category3. Additionally, classification models do not account for the temporal aspect of the data, which is important for forecasting1.
* B: Performing reinforcement learning by using claim IDs and dates and instructing the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month is not feasible, because it requires a feedback loop between the model and the agents, which might not be available or reliable in this scenario5. Furthermore, reinforcement learning is more suitable for sequential decision making problems, where the model learns from its actions and rewards, rather than forecasting problems, where the model learns from historical data and outputs future values6.
* D: Performing classification by using supervised learning of the outcome categories for which partial information on claim contents is provided and performing forecasting by using claim IDs and dates for all other outcome categories is not optimal, because it combines two different methods that might not be consistent or compatible with each other7. Also, this solution suffers from the same limitations as solution A, such as the dependency on claim contents, the inability to handle multiple outputs, and the ignorance of temporal patterns123.
1: Time Series Forecasting - Amazon SageMaker
2: Handling Missing Data for Machine Learning | AWS Machine Learning Blog
3: Forecasting vs Classification: What's the Difference? | DataRobot
4: Amazon Forecast - Time Series Forecasting Made Easy | AWS News Blog
5: Reinforcement Learning - Amazon SageMaker
6: What is Reinforcement Learning? The Complete Guide | Edureka
7: Combining Machine Learning Models | by Will Koehrsen | Towards Data Science

124. Frage
An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

A. Listwise deletion
B. Multiple imputation
C. Mean substitution
D. Last observation carried forward

Antwort: B

Begründung:
Explanation
Multiple imputation is a technique that uses machine learning to generate multiple plausible values for each missing value in a dataset, based on the observed data and the relationships among the variables. Multiple imputation preserves the integrity of the dataset by accounting for the uncertainty and variability of the missing data, and avoids the bias and loss of information that may result from other methods, such as listwise deletion, last observation carried forward, or mean substitution. Multiple imputation can improve the accuracy and validity of statistical analysis and machine learning models that use the imputed dataset. References:
Managing missing values in your target and related datasets with automated imputation support in Amazon Forecast Imputation by feature importance (IBFI): A methodology to impute missing data in large datasets Multiple Imputation by Chained Equations (MICE) Explained

125. Frage
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