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Snowflake DEA-C02 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions:

Question 1

You are tasked with designing a data pipeline that ingests JSON data from an external stage (AWS S3). The JSON files contain records for various product types, each having a different set of attributes. Some product types might have attributes that are not present in other types. You want to create a single Snowflake table that can accommodate all product types without defining a rigid schema upfront and also be queryable efficiently. Which of the following approaches, combining external tables, schema evolution and querying, would be MOST effective? (Choose two)

A. Create a stored procedure that dynamically infers the schema from the JSON files and creates a new Snowflake table based on the inferred schema.
B. Load all the data into a raw Snowflake internal table. Use dynamic SQL to infer distinct product types and create views on top of the raw table for each product type.
C. Create a single external table with a VARIANT column to store the entire JSON record for each product. Use LATERAL FLATTEN to extract specific attributes during querying.
D. Create a single external table with a VARIANT column and use the 'VALIDATE function to identify and handle schema inconsistencies during data loading.
E. Create a separate external table for each product type, defining the schema for each table based on the attributes present in the corresponding JSON files.

Question 2

You have a Snowflake Stream named 'ORDERS STREAM' on an 'ORDERS' table, which is used to incrementally load data into a historical orders table named 'HISTORICAL ORDERS'. The data pipeline involves a series of tasks: 1) Consume changes from the 'ORDERS STREAM', 2) Apply transformations and data quality checks, and 3) Merge the changes into 'HISTORICAL ORDERS' using a MERGE statement. After a recent data load, you notice that the 'HISTORICAL ORDERS' table contains duplicate records for certain 'ORDER values. The MERGE statement uses 'ORDER ID' as the matching key. You have confirmed that the transformation logic is correct and idempotent. Examine the MERGE statement below. What could be causing the duplicates, given the context of Streams and incremental loading?

A. The stream's or 'BEFORE clause is being used incorrectly, potentially rewinding the stream to an earlier point in time.
B. The MERGE statement is not correctly handling updates and deletes from the stream. The 'WHEN NOT MATCHED' and 'WHEN MATCHED' clauses are not mutually exclusive, leading to potential insertions of duplicate rows.
C. The 'ORDERS STREAM' is retaining historical data beyond the data retention period, causing older records to be re-processed.
D. Multiple tasks are concurrently consuming from the same 'ORDERS STREAM' without proper coordination, causing records to be processed multiple times.
E. The stream is not configured to capture DELETE operations from the ORDERS table, causing records that should have been removed in HISTORICAL ORDERS to remain.

Question 3

You are implementing row access policies on a 'SALES DATA table to restrict access based on the 'REGION' column. Different users are allowed to see data only for specific regions. You have a mapping table 'USER REGION MAP' with columns 'USERNAME' and 'REGION'. You want to create a row access policy that dynamically filters the 'SALES DATA' based on the user and their allowed region. Which of the following options represents a correct approach to create and apply this row access policy?

A. Option A
B. Option E
C. Option B
D. Option C
E. Option D

Question 4

You are tasked with building a data pipeline that ingests data from various sources into Snowflake, processes it, and then writes the final results back to a data lake in AWS S3, partitioned by date. The data in S3 should be queryable by other applications outside of Snowflake. You choose to use Snowflake Iceberg tables for this purpose. Which of the following is the correct SQL statement to create an Iceberg table 'analytics.public.daily_summary' in Snowflake, backed by an S3 bucket 's3://your-bucket/data/daily_summary/', partitioned by the column, and specifying 'parquet' as the file format?

A. Option A
B. Option E
C. Option B
D. Option C
E. Option D

Question 5

You are designing a data product for the Snowflake Marketplace that provides daily weather forecasts. You need to ensure that consumers of your data receive the latest forecast data every morning automatically with minimal latency. Which of the following strategies offers the MOST efficient and cost-effective solution for updating the shared data?

A. Share the raw data files stored in an external stage with the consumers. Consumers will then need to create their own pipelines to process and load the data.
B. Create a scheduled task that executes a full refresh of the shared tables every morning at 6 AM. This task uses CREATE OR REPLACE TABLE AS SELECT to rebuild the tables with the latest forecast data.
C. Manually upload a new CSV file containing the latest forecast data to a Snowflake stage and then load it into the shared tables every morning at 6 AM.
D. Create a stored procedure that truncates and reloads the shared tables with the latest forecast data from a staging table. Schedule this stored procedure to run every morning at 6 AM using a Snowflake task.
E. Implement a continuous data pipeline using Snowflake Streams and Tasks to incrementally update the shared tables as new forecast data becomes available. The stream tracks changes and tasks apply those changes to the shared tables.

Solutions: