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

1. You have a Snowflake table 'ORDERS' with billions of rows storing order information. The table includes columns like 'ORDER ID', 'CUSTOMER ID', 'ORDER DATE, 'PRODUCT_ID', and 'ORDER AMOUNT'. Analysts frequently run queries filtering by 'ORDER DATE' and 'CUSTOMER ID to analyze customer ordering trends. The performance of these queries is slow. Assuming you've already considered clustering and partitioning, which of the following strategies would BEST improve query performance, specifically targeting these filtering patterns? Assume the table is large enough for search optimization to be beneficial.

A) Enable search optimization on the 'ORDER_ID column.
B) Enable search optimization on both the 'ORDER DATE and 'CUSTOMER IDS columns.
C) Create a materialized view that pre-aggregates the data based on 'ORDER_DATE and "CUSTOMER_ID
D) Enable search optimization on the 'PRODUCT ID column.
E) Enable search optimization on the 'ORDER_DATE' column.

2. A data engineer is using Snowpark Python to build a data pipeline. They need to define a UDF that uses a pre-trained machine learning model stored as a file in a Snowflake stage. The UDF should receive batches of data for scoring. Which of the following is the MOST efficient way to implement this, minimizing data transfer and execution time?

A) Create a UDF with gudf(packages=['snowflake-snowpark-python', 'scikit-learn'], input_types=[ArrayType(StringType())], return_type=FloatType(), replace=True, is_permanent=True, and load the model within the UDF's initialization using 'session.file.get' .
B) Use 'session.read.parquet' to load the model file directly into a Snowpark DataFrame and then use 'DataFrame.foreach' to process each row.
C) Create a UDF that reads the model from the stage for each row that is passed to it using 'session.file.get' inside the UDF's execution logic.
D) Use '@vectorized' decorator from Snowpark to process each batch of data passed to the UDF and load the model inside it. Specify the appropriate data types in the decorator.
E) Load the model from the stage into a DataFrame, then use 'df.mapPartitionS to apply the model to each partition.

3. You've created a JavaScript stored procedure using Snowpark to transform data'. The stored procedure is failing, and you suspect an issue with how Snowpark is handling null values during a join operation. Given two Snowpark DataFrames, and 'df2 , what is the expected behavior when performing an inner join on a column containing null values in both DataFrames, and how can you mitigate potential issues?

A) The behavior of the inner join with null values is undefined and may vary depending on the data types and the specific version of Snowpark. Explicit null handling is always required.
B) Inner Join will not throw an error, and will exclude the rows where join column is null. If you need to join records with null values, pre-processing dataframes using to replace null with a valid sentinel value before performing the join is one way to handle this.
C) The inner join will automatically exclude rows where the join column is null in either DataFrame. There is no need for explicit null handling.
D) The inner join will treat null values as equal, resulting in rows where the join column is null in both DataFrames being included in the result. To avoid this, you should filter out null values before the join.
E) The inner join will exclude rows where the join column is null in either DataFrame. To include these rows, you must use a full outer join instead.

4. You're loading data into a Snowflake table using 'COPY INTO'. You notice that some rows are being rejected due to data validation errors (e.g., data type mismatch, uniqueness constraint violations). You want to implement a strategy to capture these rejected rows for further analysis and correction. Which of the following approaches offers the MOST efficient and reliable method for capturing and storing the rejected rows, minimizing performance impact during the data loading process? Assume no staging table exists before loading data to production table.

A) Option B
B) Option E
C) Option A
D) Option C
E) Option D

5. 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) Define the 'price' column in the Snowflake table as VARCHAR. During data loading use the NULLIFEMPTY function within the COPY INTO statement: 'TRANSFORMATION=
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 the DEFAULT NULL clause. The COPY INTO statement will automatically insert NULL values for missing fields.
D) 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 =
E) Pre-process the JSON data before loading into Snowflake, adding a 'price': null field to any product object missing the price.

Solutions: