Design Data Ingestion and Transformation Pipelines ARA-C01 Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   Which pipeline pattern is most appropriate when the source system produces data in large, discrete files at scheduled intervals, and the business does not require real-time data?

   • AMicro-batch processing
   • BBatch processing
   • CStreaming processing
   • DEvent-driven processing
   Reveal Solution Discussion Select an answer first
2. 17foundation · easy

   How does increasing the size of a virtual warehouse affect the cost of running a pipeline?

   • AIt only affects storage cost
   • BIt has no effect on cost
   • CIt increases cost because more compute resources are used
   • DIt decreases cost because queries run faster
   Reveal Solution Discussion Select an answer first
4. 18foundation · easy

   In the ELT approach, where does the transformation of data typically occur?

   • ADuring the data transfer process
   • BIn a separate transformation tool after loading
   • CInside Snowflake after loading
   • DIn the source system before loading
   Reveal Solution Discussion Select an answer first
5. 19foundation · easy

   A team needs to load a large CSV file from an external stage into a Snowflake table once. Which Snowflake feature is specifically designed for this one-time bulk load?

   • ACOPY INTO
   • BExternal table
   • CConnector
   • DSnowpipe
   Reveal Solution Discussion Select an answer first
6. 20application · medium

   A healthcare analytics company receives raw patient records in JSON format. The data must be validated for required fields, transformed into a normalized relational schema, and joined with reference data before being made available to analysts. The team has limited engineering resources and wants to minimize the amount of custom code to maintain. Which transformation strategy should the architect recommend?

   • AUse external functions to call a third-party transformation service for each record during the load.
   • BLoad the JSON directly into the final normalized tables using COPY with automatic schema detection.
   • CUse ELT with SQL: load raw JSON into a staging table, then use SQL views and stored procedures to validate, transform, and join the data within Snowflake.
   • DPerform ETL using an external Python script that reads from Snowflake, transforms the data, and writes it back.
   Reveal Solution Discussion Select an answer first