[Exploratory Data Analysis]
A company uses sensors on devices such as motor engines and factory machines to measure parameters, temperature and pressure. The company wants to use the sensor data to predict equipment malfunctions and reduce services outages.
The Machine learning (ML) specialist needs to gather the sensors data to train a model to predict device malfunctions The ML spoctafst must ensure that the data does not contain outliers before training the ..el.
What can the ML specialist meet these requirements with the LEAST operational overhead?
[Exploratory Data Analysis]
A machine learning (ML) engineer is preparing a dataset for a classification model. The ML engineer notices that some continuous numeric features have a significantly greater value than most other features. A business expert explains that the features are independently informative and that the dataset is representative of the target distribution.
After training, the model's inferences accuracy is lower than expected.
Which preprocessing technique will result in the GREATEST increase of the model's inference accuracy?
[Modeling]
A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested The company also wants be able to save the results in its data lake for later processing and analysis
What is the MOST efficient way to accomplish these tasks'?
[Data Engineering]
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?
[Modeling]
An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers
want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored
for offline analysis.
What approach would be the MOST effective to perform near-real time defect detection?
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