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Certification Path
The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.
There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.
You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path
Google Certified Professional Data Engineer Exam Sample Questions (Q211-Q216):
NEW QUESTION # 211
Which of these statements about BigQuery caching is true?
- A. By default, a query's results are not cached.
- B. There is no charge for a query that retrieves its results from cache.
- C. Query results are cached even if you specify a destination table.
- D. BigQuery caches query results for 48 hours.
Answer: B
Explanation:
When query results are retrieved from a cached results table, you are not charged for the query. BigQuery caches query results for 24 hours, not 48 hours. Query results are not cached if you specify a destination table. A query's results are always cached except under certain conditions, such as if you specify a destination table.
Reference: https://cloud.google.com/bigquery/querying-data#query-caching
NEW QUESTION # 212
You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?
- A. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
- B. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
- C. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table.
Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly. - D. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
Answer: D
NEW QUESTION # 213
Google Cloud Bigtable indexes a single value in each row. This value is called the _______.
- A. row key
- B. unique key
- C. primary key
- D. master key
Answer: A
Explanation:
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key.
Reference: https://cloud.google.com/bigtable/docs/overview
NEW QUESTION # 214
Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?
- A. Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.
- B. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.
- C. Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.
- D. Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.
Answer: D
NEW QUESTION # 215
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. Create a table on Cloud SQL, and insert and delete rows with the job information
- B. Create a table on Cloud Spanner, and insert and delete rows with the job information
- C. Create an API using App Engine to receive and send messages to the applications
- D. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
Answer: C
NEW QUESTION # 216
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