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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)
IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data The following are high priorities for the company: * Solution simplicity * Fast development time * Low cost * High flexibility What technologies meet the company's requirements?
A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker. Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?
A data engineer needs to provide a team of data scientists with the appropriate dataset to run machine learning training jobs. The data will be stored in Amazon S3. The data engineer is obtaining the data from an Amazon Redshift database and is using join queries to extract a single tabular dataset. A portion of the schema is as follows: ...traction Timestamp (Timeslamp) ...JName(Varchar) ...JNo (Varchar) Th data engineer must provide the data so that any row with a CardNo value of NULL is removed. Also, the TransactionTimestamp column must be separated into a TransactionDate column and a isactionTime column Finally, the CardName column must be renamed to NameOnCard. The data will be extracted on a monthly basis and will be loaded into an S3 bucket. The solution must minimize the effort that is needed to set up infrastructure for the ingestion and transformation. The solution must be automated and must minimize the load on the Amazon Redshift cluster Which solution meets these requirements?
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 company is planning a marketing campaign to promote a new product to existing customers. The company has data (or past promotions that are similar. The company decides to try an experiment to send a more expensive marketing package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to purchase the new product receive the marketing materials. ...company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%. ...should the company retrain the model to meet these requirements?
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features. Which model will meet the business requirement?
An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 Which approach will provide the information required for further analysis?
A Machine Learning Specialist is using Amazon Sage Maker to host a model for a highly available customer-facing application. The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?

Question 90 - MLS-C01 discussion

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A manufacturer of car engines collects data from cars as they are being driven The data collected includes timestamp, engine temperature, rotations per minute (RPM), and other sensor readings The company wants to predict when an engine is going to have a problem so it can notify drivers in advance to get engine maintenance The engine data is loaded into a data lake for training

Which is the MOST suitable predictive model that can be deployed into production'?

A.
Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a recurrent neural network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.
Answers
A.
Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a recurrent neural network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.
B.
This data requires an unsupervised learning algorithm Use Amazon SageMaker k-means to cluster the data
Answers
B.
This data requires an unsupervised learning algorithm Use Amazon SageMaker k-means to cluster the data
C.
Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a convolutional neural network (CNN) to train the model to recognize when an engine might need maintenance for a certain fault.
Answers
C.
Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a convolutional neural network (CNN) to train the model to recognize when an engine might need maintenance for a certain fault.
D.
This data is already formulated as a time series Use Amazon SageMaker seq2seq to model the time series.
Answers
D.
This data is already formulated as a time series Use Amazon SageMaker seq2seq to model the time series.
Suggested answer: A

Explanation:

A recurrent neural network (RNN) is a type of neural network that can process sequential data, such as time series, by maintaining a hidden state that captures the temporal dependencies between the inputs. RNNs are well suited for predicting future events based on past observations, such as forecasting engine failures based on sensor readings. To train an RNN model, the data needs to be labeled with the target variable, which in this case is the type and time of the engine fault. This makes the problem a supervised learning problem, where the goal is to learn a mapping from the input sequence (sensor readings) to the output sequence (engine faults). By using an RNN model, the manufacturer can leverage the temporal information in the data and detect patterns that indicate when an engine might need maintenance for a certain fault.

References:

Recurrent Neural Networks - Amazon SageMaker

Use Amazon SageMaker Built-in Algorithms or Pre-trained Models

Recurrent Neural Network Definition | DeepAI

What are Recurrent Neural Networks? An Ultimate Guide for Newbies!

Lee and Carter go Machine Learning: Recurrent Neural Networks - SSRN

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asked 16/09/2024
Pablo Fernandez Rada
36 questions
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