Snowflake DEA-C02 Actual Free Exam Questions & Community Discussion

You are designing a data pipeline using Snowpipe to ingest data from multiple S3 buckets into a single Snowflake table. Each S3 bucket represents a different data source and contains files in JSON format. You want to use Snowpipe's auto-ingest feature and a single Snowpipe object for all buckets to simplify management and reduce overhead. However, each data source has a different JSON schem a. How can you best achieve this goal while ensuring data is loaded correctly and efficiently into the target table?

You have a table named 'ORDERS' with a column 'ORDER DETAILS' that contains JSON data'. You want to extract a specific nested value ('customer id') from this JSON data using a SQL UDE The JSON structure varies, and sometimes the 'customer id' field might be missing. You need to create a UDF that handles missing fields gracefully and returns NULL if 'customer id' is not found. Also, You are looking for a performant solution that is highly scalable. Which of the following SQL UDF definitions is most appropriate?

You have created a JavaScript UDF named 'calculate discount' in Snowflake that takes two arguments: 'product_price' (NUMBER) and 'discount_percentage' (NUMBER). The UDF calculates the discounted price using the formula: 'product_price (1 - discount_percentage / 100)'. However, when you call the UDF with certain input values, you are encountering unexpected results, specifically with very large or very small numbers due to JavaScript's number precision limitations. Which of the following strategies can you implement to mitigate this issue and ensure accurate calculations within your JavaScript UDF?

You are responsible for monitoring the performance of a Snowflake data pipeline that loads data from S3 into a Snowflake table named 'SALES DATA. You notice that the COPY INTO command consistently takes longer than expected. You want to implement telemetry to proactively identify the root cause of the performance degradation. Which of the following methods, used together, provide the MOST comprehensive telemetry data for troubleshooting the COPY INTO performance?

A data engineer is implementing a data governance policy that requires masking PII data in non-production environments. They have identified a column 'CUSTOMER EMAIL' that needs to be masked. They want to use dynamic data masking in Snowflake, but the 'CUSTOMER EMAIL' column is referenced in several views. Which of the following approaches is MOST appropriate and avoids breaking the existing views?

You are responsible for monitoring the performance of several data pipelines in Snowflake that heavily rely on streams. You notice that some streams consistently lag behind the base tables. You need to proactively identify the root cause and implement solutions. Which of the following metrics and monitoring techniques would be MOST helpful in diagnosing and resolving the stream lag issue? (Select all that apply)

You're designing a Snowpark data transformation pipeline that requires running a Python function on each row of a large DataFrame. The Python function is computationally intensive and needs access to external libraries. Which of the following approaches will provide the BEST combination of performance, scalability, and resource utilization within the Snowpark architecture?

A data engineering team is loading a large fact table 'SALES DATA' daily, partitioned by 'SALE DATE. After several months, query performance degrades significantly. An analyst reports that queries filtering on 'CUSTOMER are slow, despite 'CUSTOMER ID' having high cardinality. The table definition is as follows: CREATE TABLE SALES_DATA ( SALE DATE DATE NOT NULL, CUSTOMER_ID NUMBER NOT NULL, PRODUCT ID NUMBER NOT NULL, SALE_AMOUNT ... Which of the following actions would BEST improve query performance for queries filtering on 'CUSTOMER ID', considering the existing partitioning by 'SALE DATE'?

You have a table 'SALES DATA' in your production environment. You want to create a development environment using cloning, but only want to include data up to a specific point in time to minimize storage costs and potential exposure of recent, sensitive data'. You know there were significant changes to the 'SALES DATA' table structure on '2024-01-15'. Your goal is to create a clone that only includes the structure as of '2024-01-14'. Which Snowflake command is MOST appropriate for this scenario?

You are using Snowpark Python to perform data transformation on a large dataset stored in a Snowflake table named customer transactions'. This table contains columns such as 'customer id', 'transaction date', 'transaction amount', and product_category'. Your task is to identify customers who have made transactions in more than one product category within the last 30 days. Which of the following Snowpark Python snippets is the most efficient way to achieve this, minimizing data shuffling and maximizing query performance?