A manufacturing company has a large set of labeled historical sales data. The manufacturer would like to predict how many units of a particular part should be produced each quarter.
Which machine learning approach should be used to solve this problem?
B
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
✑ Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
✑ Support event-driven ETL pipelines
✑ Provide a quick and easy way to understand metadata
Which approach meets these requirements?
A
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes.
What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?
B
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
D
A company is running a machine learning prediction service that generates 100 TB of predictions every day. A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.
Which solution requires the LEAST coding effort?
C
A Machine Learning Specialist is preparing data for training on Amazon SageMaker. The Specialist is using one of the SageMaker built-in algorithms for the training. The dataset is stored in .CSV format and is transformed into a numpy.array, which appears to be negatively affecting the speed of the training.
What should the Specialist do to optimize the data for training on SageMaker?
C
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000
Test set images = 100 (constant test set)
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?
B
A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis.
Which of the following services would both ingest and store this data in the correct format?
C
A data scientist has explored and sanitized a dataset in preparation for the modeling phase of a supervised learning task. The statistical dispersion can vary widely between features, sometimes by several orders of magnitude. Before moving on to the modeling phase, the data scientist wants to ensure that the prediction performance on the production data is as accurate as possible.
Which sequence of steps should the data scientist take to meet these requirements?
D
Reference:
https://www.kdnuggets.com/2018/12/six-steps-master-machine-learning-data-preparation.html
A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.
Which approach should the Specialist use to continue working?
B
