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Google Professional-Data-Engineer Certification Exam is designed for professionals who want to demonstrate their skills in designing and building data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is ideal for data engineers, data analysts, and database administrators who work with big data and want to enhance their skills. Professional-Data-Engineer Exam covers a broad range of topics, including data processing, data storage, data analysis, and machine learning.
Google Certified Professional Data Engineer Exam Sample Questions (Q248-Q253):
NEW QUESTION # 248
Which of these sources can you not load data into BigQuery from?
- A. Google Cloud SQL
- B. File upload
- C. Google Cloud Storage
- D. Google Drive
Answer: A
Explanation:
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Reference: https://cloud.google.com/bigquery/loading-data
NEW QUESTION # 249
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)
- A. A good use for the wide and deep model is a recommender system.
- B. A good use for the wide and deep model is a small-scale linear regression problem.
- C. The wide model is used for memorization, while the deep model is used for generalization.
- D. The wide model is used for generalization, while the deep model is used for memorization.
Answer: A,C
Explanation:
Can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems.
Reference: https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html
NEW QUESTION # 250
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics.
Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?
- A. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
- B. Add capacity (memory and disk space) to the database server by the order of 200.
- C. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
- D. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
Answer: A
NEW QUESTION # 251
You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?
- A. Create a separate table for each ID.
- B. Recreate the table with a partitioning column and clustering column.
- C. Use the bq query - -maximum_bytes_billed flag to restrict the number of bytes billed.
- D. Use the LIMIT keyword to reduce the number of rows returned.
Answer: B
Explanation:
https://cloud.google.com/bigquery/docs/best-practices-costs
NEW QUESTION # 252
You work for an economic consulting firm that helps companies identify economic trends as they happen.
As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
- A. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
- B. Load the data every 30 minutes into a new partitioned table in BigQuery.
- C. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
- D. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
Answer: A
Explanation:
The regional storage is cheaper than BigQuery storage.
NEW QUESTION # 253
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