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下載Google Certified Professional Data Engineer Exam考試題庫
NEW QUESTION 51
Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set.
You want to increase the AUC of the model. What should you do?
- A. Train a classifier with deep neural networks, because neural networks would always beat SVMs
- B. Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC
- C. Perform hyperparameter tuning
- D. Deploy the model and measure the real-world AUC; it’s always higher because of generalization
Answer: C
NEW QUESTION 52
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?
- A. The CSV data has invalid rows that were skipped on import.
- B. The CSV data has not gone through an ETL phase before loading into BigQuery.
- C. The CSV data loaded in BigQuery is not flagged as CSV.
- D. The CSV data loaded in BigQuery is not using BigQuery’s default encoding.
Answer: A
NEW QUESTION 53
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this data. You want to maximize performance and usability for your data science team. Which two strategies should you adopt?
Choose 2 answers.
- A. Denormalize the data as must as possible.
- B. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
- C. Preserve the structure of the data as much as possible.
- D. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery’s support for external data sources to query.
- E. Use BigQuery UPDATE to further reduce the size of the dataset.
Answer: B,D
NEW QUESTION 54
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?
- A. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
- B. Create an API using App Engine to receive and send messages to the applications
- C. Create a table on Cloud Spanner, and insert and delete rows with the job information
- D. Create a table on Cloud SQL, and insert and delete rows with the job information
Answer: B
NEW QUESTION 55
You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
No interaction by the user on the site for 1 hour
Has added more than $30 worth of products to the basket
Has not completed a transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?
- A. Use a global window with a time based trigger with a delay of 60 minutes.
- B. Use a fixed-time window with a duration of 60 minutes.
- C. Use a session window with a gap time duration of 60 minutes.
- D. Use a sliding time window with a duration of 60 minutes.
Answer: A
NEW QUESTION 56
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