A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing.The Data Scientist has been given the following requirements to the cloud solution:•Combine multiple data sources.•Reuse existing PySpark logic.•Run the solution on the existing schedule.•Minimize the number of servers that will need to be managed.Which architecture should the Data Scientist use to build this solution?
Answer(s): D
A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features. The Marketing team has not provided any insight about which features are relevant for churn prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide gap between the training and validation set accuracy.Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team’s needs? (Choose two.)
Answer(s): A,C
A) Adding L1 regularization to the logistic regression classifier can help to improve the model performance and reduce overfitting. This can also help to highlight the relevant features for churn prediction as L1 regularization can shrink the coefficients of irrelevant features to zero.C) Recursive feature elimination can be used to select the most relevant features for the model. This can help to improve the model performance and highlight the relevant features for churn prediction.
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?
Answer(s): B
A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.What combination of services should the team use to build a custom algorithm in Amazon SageMaker? (Choose two.)
Answer(s): C,E
A Machine Learning Specialist wants to determine the appropriate SageMakerVariantInvocationsPerInstance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS. As this is the first deployment, the Specialist intends to set the invocation safety factor to 0.5.Based on the stated parameters and given that the invocations per instance setting is measured on a per- minute basis, what should the Specialist set as the SageMakerVariantInvocationsPerInstance setting?
Answer(s): C
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