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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have a Snowpark Python application that performs complex calculations on a large dataset stored in Snowflake. The application is currently running slowly. After profiling, you've identified that the UDFs you're using are the bottleneck. These UDFs perform custom data transformations using a third-party Python library which has a significant initialization overhead. Which of the following strategies would be MOST effective to optimize performance, minimizing both runtime and resource consumption?

A) Increase the size of the Snowflake warehouse being used for the Snowpark workload. This will provide more CPU and memory resources.
B) Implement UDF caching at the Snowflake level by setting the 'VOLATILE property to 'IMMUTABLE or 'STABLE' (if appropriate), and leverage the Snowflake query result cache.
C) Use Snowpark's 'pandas_udf with 'vectorized=True' and pre-initialize the third-party library within the UDF's execution context using a closure or similar technique for reuse across batches.
D) Convert the Snowpark Python application to a Snowpark Java application as Java generally offers better performance than Python.
E) Rewrite the UDFs in SQL using Snowflake's built-in functions to avoid the overhead of Python execution. If the library's functions aren't available, consider creating external functions using a cloud provider's serverless compute service.

2. You need to load data from a stream of CSV files into a Snowflake table. The CSV files are delivered to an AWS S3 bucket and contain header rows. The files occasionally include records where a text field contains a delimiter character (comma) within the text itself, but these fields are properly enclosed within double quotes. You want to create a file format object that correctly handles the data, including quoted delimiters, and skips the header row. Which of the following file format options are required to achieve this? (Choose two)

A) ERROR ON COLUMN COUNT MISMATCH = FALSE
B) FIELD DELIMITER = ','
C) SKIP HEADER = 1
D) FILE_FORMAT = (TYPE = CSV)
E) FIELD OPTIONALLY ENCLOSED BY =

3. Consider the following scenario: You are ingesting JSON data from an external stage into Snowflake. The JSON data contains an array of objects, where each object represents a product with attributes like 'product id', 'name', and 'price'. However, sometimes the 'price' field is missing entirely from some product objects. You want to load this data into a Snowflake table with columns 'product_id', 'name', and 'price' (defined as NUMBER). How can you handle the missing 'price' field gracefully during the COPY INTO operation, ensuring that missing prices are represented as NULL in the Snowflake table without causing errors?

A) Pre-process the JSON data before loading into Snowflake, adding a 'price': null field to any product object missing the price.
B) Define the 'price' column in the Snowflake table as VARIANT. After the data is loaded, create a view that extracts the price using the 'GET' function and converts it to NUMBER using 'TO NUMBER. Handle NULL values in the view using 'price')), NULL, TO 'price')))'.
C) Define the 'price' column in the Snowflake table as NUMBER and use a transformation within the COPY INTO statement to handle missing prices: 'TRANSFORMATION = (price =
D) Define the 'price' column in the Snowflake table as VARCHAR. During data loading use the NULLIFEMPTY function within the COPY INTO statement: 'TRANSFORMATION=
E) Define the 'price' column in the Snowflake table as NUMBER and use the DEFAULT NULL clause. The COPY INTO statement will automatically insert NULL values for missing fields.

4. You are loading data from an S3 bucket into a Snowflake table using the COPY INTO command. The source data contains dates in various formats (e.g., 'YYYY-MM-DD', 'MM/DD/YYYY', 'DD-Mon-YYYY'). You want to ensure that all dates are loaded correctly and consistently into a DATE column in Snowflake. Which of the following COPY INTO options and commands is the MOST appropriate to handle this?

A) Use the 'DATE FORMAT option in the COPY INTO command with a single format string that covers all possible date formats.
B) Use the 'STRTOK TO DATE function within a SELECT statement in a Snowpipe transformation to dynamically parse the dates based on different patterns.
C) Use the 'VALIDATE(O)' command before the COPY INTO command to identify files with invalid date formats and then process them separately.
D) Use the 'ON_ERROR = 'SKIP FILE" option to skip files with invalid date formats.
E) Utilize the 'DATE' function with explicit format strings inside a Snowpipe transformation pipeline. This involves pattern matching using 'CASE WHEN' statements to identify date formats before converting to the DATE data type.

5. A Snowflake data warehouse contains a table named 'SALES TRANSACTIONS' with the following columns: 'TRANSACTION ID', 'PRODUCT D', 'CUSTOMER D', 'TRANSACTION DATE, and 'SALES AMOUNT'. You need to optimize a query that calculates the total sales amount per product for a given month. The 'SALES TRANSACTIONS' table is very large (billions of rows), and queries are slow. Given the following initial query: SELECT PRODUCT ID, SUM(SALES AMOUNT) AS TOTAL SALES FROM SALES TRANSACTIONS WHERE TRANSACTION DATE BETWEEN '2023-01-07' AND '2023-01-31' GäOUP BY PRODUCT ID; Which of the following actions, when combined, would MOST effectively improve the performance of this query?

A) Create a materialized view that pre-aggregates the total sales amount per product and month.
B) Increase the virtual warehouse size to the largest available size.
C) Create a clustering key on 'PRODUCT_ID and 'TRANSACTION_DATE columns in the 'SALES_TRANSACTIONS' table.
D) Create a temporary table with the results of the query and query that table instead.
E) Convert the column to a VARCHAR data type.

Solutions: