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NEW QUESTION 25
Which of the following are feature engineering techniques? (Select 2 answers)
- A. Bucketization of a continuous feature
- B. Hidden feature layers
- C. Feature prioritization
- D. Crossed feature columns
Answer: A,D
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
NEW QUESTION 26
A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL `dataset.model’, table . How should you create the ML pipeline?
user_features)
- A. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
- B. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
- C. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.
- D. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query.
Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.
Answer: D
NEW QUESTION 27
You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?
- A. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
- B. Update the Cloud Dataflow pipeline inflight by passing the –update option with the –jobName set to the existing job name
- C. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code
- D. Update the Cloud Dataflow pipeline inflight by passing the –updateoption with the –jobNameset to a new unique job name
Answer: B
Explanation:
Explanation/Reference: https://cloud.google.com/dataflow/docs/guides/updating-a-pipeline
NEW QUESTION 28
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