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NEW QUESTION: 1
Which method could be utilized to identify both DML operations and the SQL statements needed to undo those operations for a specific schema owner? (Choose all that apply.)
A. Query DBA_TRANSACTION_QUERY for TABLE_NAME, OPERATION, and UNDO_SQL. Limit rows by START_SCN and TABLE_OWNER.
B. Query FLASHBACK_TRANSACTION_QUERY for TABLE_NAME, OPERATION, and UNDO_SQL. Limit rows by START_TIMESTAMP and TABLE_OWNER.
C. Query FLASHBACK_TRANSACTION_QUERY for TABLE_NAME, OPERATION, and UNDO_SQL. Limit rows by START_SCN and TABLE_OWNER.
D. Query DBA_TRANSACTION_QUERY for TABLE_NAME, OPERATION, and UNDO_SQL. Limit rows by START_SCN and TABLE_OWNER.
Answer: B,C

NEW QUESTION: 2
You need to provision and deploy the order workflow.
Which three components should you include? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Workflow definition
B. Connections
C. On-premises Data Gateway
D. Resources
E. Functions
Answer: A,C,E
Explanation:
Explanation/Reference:
Explanation:
Scenario: The order workflow fails to run upon initial deployment to Azure.

Question Set 3

NEW QUESTION: 3
Which of the following are feature engineering techniques? (Select 2 answers)
A. Bucketization of a continuous feature
B. Feature prioritization
C. Crossed feature columns
D. Hidden feature layers
Answer: A,C
Explanation:
Explanation
Selecting and crafting the right set of feature columns is key to learning an effective model.
Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into.
Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.
Reference:
https://www.tensorflow.org/tutorials/wide#selecting_and_engineering_features_for_the_model