You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?
Answer(s): A
Using a sales_region user attribute is the best solution because it allows you to dynamically filter data based on each manager's assigned region. By adding an access_filter Explore filter on the region_name dimension that references the sales_region user attribute, each manager sees only the sales metrics specific to their region. This approach is easy to implement, scalable, and avoids duplicating dashboards or Explores, making it both efficient and maintainable.
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage. The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?
Answer(s): C
The ETL (Extract, Transform, Load) methodology is the best approach for this scenario because it allows you to extract data from the files, transform it by applying the necessary data cleansing (including removing malicious SQL injections), and then load the sanitized data into BigQuery. By transforming the data before loading it into BigQuery, you ensure that only clean and safe data is stored, which is critical for security and data quality.
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need to clean the data to ensure that it is accurate and consistent before using it for analysis.What should you do?
Answer(s): B
Using BigQuery to batch load the data and perform cleaning and analysis with SQL is the best approach for this scenario. BigQuery provides powerful SQL capabilities to handle missing values, enforce correct data types, and remove duplicates efficiently. This method simplifies the pipeline by leveraging BigQuery's built-in processing power for both cleaning and analysis, reducing the need for additional tools or services and minimizing complexity.
Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys. What should you do?
Using Google-managed encryption keys (GMEK) is the best choice when you want to encrypt sensitive data in Cloud Storage without the operational overhead of managing encryption keys. GMEK is the default encryption mechanism in Google Cloud, and it ensures that data is automatically encrypted at rest with no additional setup or maintenance required. It provides strong security while eliminating the need for manual key management.
You work for a financial organization that stores transaction data in BigQuery. Your organization has a regulatory requirement to retain data for a minimum of seven years for auditing purposes. You need to ensure that the data is retained for seven years using an efficient and cost-optimized approach.What should you do?
Setting a table-level retention policy in BigQuery to seven years is the most efficient and cost- optimized solution to meet the regulatory requirement. A table-level retention policy ensures that the data cannot be deleted or overwritten before the specified retention period expires, providing compliance with auditing requirements while keeping the data within BigQuery for easy access and analysis. This approach avoids the complexity and additional costs of exporting data to Cloud Storage.
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