Free Google Professional-Data-Engineer Exam Dumps Questions & Answers

You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 3090 days of data, the query scans the entire table.
You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries.
What should you do?

• A.Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
• B.Re-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.
• C.Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
• D.Write an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

• A.Create a table in BigQuery, and append the new samples for CPU and memory to the table
• B.Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
• C.Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.
• D.Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second

You need to load a dataset with multiple terabytes of clickstream data into BigQuery. The data arrives each day as compressed JSON files in a Cloud Storage bucket. You need a low-cost, programmatic, and scalable solution to load the data into BigQuery. What should you do?

• A.Use the BigQuery Data Transfer Service from Cloud Storage.
• B.Create an external table in BigQuery pointing to the Cloud Storage bucket and run the INSERT INTO ... SELECT * FROM external_table command.
• C.Create a Cloud Run function to run a Python script to read and parse each JSON file, and use the BigQuery streaming insert API.
• D.Use Cloud Data Fusion to create a pipeline to load the JSON files into BigQuery.

You currently use a SQL-based tool to visualize your data stored in BigQuery. The data visualizations require the use of outer joins and analytic functions. Visualizations must be based on data that is no less than 4 hours old. Business users are complaining that the visualizations are too slow to generate. You want to improve the performance of the visualization queries while minimizing the maintenance overhead of the data preparation pipeline. What should you do?

• A.Create views for the visualization queries. Reference the views in the data visualization tool.
• B.Create materialized views with the allow_non_incremental_definition option set to true for the visualization queries. Specify the max_staleness parameter to 4 hours and the enable_refresh parameter to true. Reference the materialized views in the data visualization tool.
• C.Create materialized views for the visualization queries. Use the incremental updates capability of BigQuery materialized views to handle changed data automatically. Reference the materialized views in the data visualization tool.
• D.Create a Cloud Function instance to export the visualization query results as parquet files to a Cloud Storage bucket. Use Cloud Scheduler to trigger the Cloud Function every 4 hours.
  Reference the parquet files in the data visualization tool.

Your company is selecting a system to centralize data ingestion and delivery. You are considering messaging and data integration systems to address the requirements. The key requirements are:
- The ability to seek to a particular offset in a topic, possibly back
to the start of all data ever captured
- Support for publish/subscribe semantics on hundreds of topics
- Retain per-key ordering
Which system should you choose?

• A.Apache Kafka
• B.Firebase Cloud Messaging
• C.Cloud Storage
• D.Dataflow