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NEW QUESTION 50
A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.
The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.
Which feature engineering technique should the Data Scientist use to meet the objectives?

• A. Run self-correlation on all features and remove highly correlated features
• B. Normalize all numerical values to be between 0 and 1
• C. Cluster raw data using k-means and use sample data from each cluster to build a new dataset
• D. Use an autoencoder or principal component analysis (PCA) to replace original features with new features

Answer: B

NEW QUESTION 51
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?

• A. Load the data into Cloud Bigtable, and read the data from Bigtable
• B. Load the data into BigQuery and read the data from BigQuery.
• C. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
• D. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)

Answer: C

NEW QUESTION 52
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?

• A. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model
• B. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
• C. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
• D. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery

Answer: C

NEW QUESTION 53
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