Question 91

How does incorporating visualizations in reports and dashboards aid in data comprehension for business use analyses?
  • Question 92

    What considerations should be taken into account when choosing between a dimensional model and a flattened data set for BI requirements in Snowflake? (Select all that apply)
  • Question 93

    You have a large dataset of IoT sensor readings stored in compressed JSON files within an AWS S3 bucket. Each JSON file contains an array of sensor readings with the following structure:
    You need to load this data into a Snowflake table named 'sensor data' with columns 'sensor id', 'timestamp', 'temperature', and 'humidity'. Which of the following Snowflake commands would be the MOST efficient and appropriate to ingest this data, assuming you have already created the table and a named stage pointing to the S3 bucket?
  • Question 94

    You are tasked with ingesting data from a variety of external sources (JSON, CSV, Parquet) into Snowflake to build a unified customer profile. The sources contain inconsistencies in data types, null handling, and naming conventions. Your goal is to create a robust ingestion pipeline that identifies data quality issues and prepares the data for analysis. Which combination of Snowflake features and approaches would be most effective for achieving this?
  • Question 95

    You are responsible for maintaining a dashboard that visualizes sales data'. The dashboard relies on a Snowflake view named 'SALES SUMMARY. Users have reported slow refresh times, especially when filtering by date. The 'SALES SUMMARY view is defined as follows:

    Which of the following actions could significantly improve the dashboard's performance when filtering by date, assuming 'SALES_TRANSACTIONS' table is large?