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Google Certified Professional Data Engineer Exam Sample Questions (Q225-Q230):
NEW QUESTION # 225
Your financial services company is moving to cloud technology and wants to store 50 TB of financial timeseries data in the cloud. This data is updated frequently and new data will be streaming in all the time.
Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
Which product should they use to store the data?
- A. Google Cloud Storage
- B. Google Cloud Datastore
- C. Cloud Bigtable
- D. Google BigQuery
Answer: C
Explanation:
Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series
NEW QUESTION # 226
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?
- A. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.
- B. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
- C. Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.
- D. Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
Answer: B
NEW QUESTION # 227
You are using BigQuery and Data Studio to design a customer-facing dashboard that displays large quantities of aggregated data. You expect a high volume of concurrent users. You need to optimize tie dashboard to provide quick visualizations with minimal latency. What should you do?
- A. Use BigQuery BI Engine with streaming data.
- B. Use BigQuery Bl Engine with authorized views
- C. Use BigQuery BI Engine with materialized views
- D. Use BigQuery Bl Engine with logical reviews
Answer: A
NEW QUESTION # 228
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?
- A. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
- B. Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
- C. Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.
- D. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.
Answer: B
NEW QUESTION # 229
What are all of the BigQuery operations that Google charges for?
- A. Storage, queries, and exporting data
- B. Storage, queries, and loading data from a file
- C. Queries and streaming inserts
- D. Storage, queries, and streaming inserts
Answer: D
Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
NEW QUESTION # 230
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