Question # 1
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store: The user profile: What the user likes and doesn’t like to eat The user account information: Name, address, preferred meal times The order information: When orders are made, from where, to whom The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?
| A. BigQuery
| B. Cloud SQL
| C. Cloud Bigtable
| D. Cloud Datastore |
Question # 2
Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?
| A. Rewrite the job in Pig.
| B. Rewrite the job in Apache Spark.
| C. Increase the size of the Hadoop cluster.
| D. Decrease the size of the Hadoop cluster but also rewrite the job in Hive. |
Question # 3
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 loaded in BigQuery is not flagged as CSV.
| B. The CSV data has invalid rows that were skipped on import.
| C. The CSV data loaded in BigQuery is not using BigQuery’s default encoding.
| D. The CSV data has not gone through an ETL phase before loading into BigQuery. |
The CSV data has invalid rows that were skipped on import.
Question # 4
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?
| A. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
| B. Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
| C. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
| D. Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery. |
Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
Question # 5
You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data. Which two actions should you take? (Choose two.)
| A. Ensure all the tables are included in global dataset.
| B. Ensure each table is included in a dataset for a region.
| C. Adjust the settings for each table to allow a related region-based security group view access.
| D. Adjust the settings for each view to allow a related region-based security group view access.
| E. Adjust the settings for each dataset to allow a related region-based security group view access. |
Ensure each table is included in a dataset for a region.
Adjust the settings for each view to allow a related region-based security group view access.
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