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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are tasked with creating a resilient data pipeline using Snowpark Python. The pipeline transforms data from a raw stage to a processed stage. A key transformation involves joining two DataFrames, 'dfl' and 'df2 , based on a common column, 'id'. You want to ensure that even if 'df2 is temporarily unavailable or contains unexpected data, the pipeline continues to process 'dfl' using a default value for missing data from 'df2. Which of the following approaches provides the best balance of resilience and data integrity? Assume you have defined a default dataframe 'df default' already.

A) Use 'broadcast hint on 'df2 before performing the join to reduce the chances of join failure, assuming 'df2 is a small dataframe.
B) Perform a 'left_outer' join of 'dfl' with 'df2. If the join fails, catch the exception and proceed without the join.
C) Perform a 'left_outer' join of 'dfl' with 'df2. If 'df2 is unavailable or returns no data, replace 'df2' with a default DataFrame Cdf_default) and proceed with the join.
D) Use a 'try-except' block to catch any exceptions during the join operation. If an exception occurs, use the 'fillna()' method to replace missing values with the default data value.
E) Write a custom Python UDF that attempts to retrieve the corresponding data from 'df2 based on the 'id' column. If the retrieval fails for a particular ID, return a default value.

2. You are designing a data protection strategy for a Snowflake database. You need to implement dynamic data masking on the 'CREDIT CARD' column in the 'TRANSACTIONS' table. The requirement is that users with the 'FINANCE ADMIN' role should see the full credit card number, while all other users should see only the last four digits. You have the following masking policy:

What is the next step to apply this masking policy to the 'CREDIT CARD' column?

A)

B)

C)

D)

E)

3. You have configured a Kafka Connector to load JSON data into a Snowflake table named 'ORDERS. The JSON data contains nested structures. However, Snowflake is only receiving the top- level fields, and the nested fields are being ignored. Which configuration option within the Kafka Connector needs to be adjusted to correctly flatten and load the nested JSON data into Snowflake?

A) Enable the 'snowflake.ingest.stage' property and set it to a Snowflake internal stage.
B) Apply the 'org.apache.kafka.connect.transforms.Flatten' transformation to the 'transforms' configuration.
C) Configure the 'snowflake.data.field.name' property to specify the column in the Snowflake table where the entire JSON should be loaded as a VARIANT.
D) Set the 'value.converter.schemas.enable' property to 'true'.
E) Use the 'transforms' configuration with the 'org.apache.kafka.connect.transforms.ExtractField$Value' transformation to extract specific fields.

4. You are tasked with creating a Python script to load data from a CSV file stored in an AWS S3 bucket into a Snowflake table. You have the following requirements: 1. Use the 'COPY INTO' command for efficient data loading. 2. Handle potential schema evolution in the CSV file (e.g., new columns being added). 3. Automatically create the target table if it doesn't exist, inferring the schema from the CSV. Which combination of Snowflake Python connector functionalities and 'COPY INTO' options would best address these requirements, assuming you have an AWS IAM role configured for Snowflake access to S3?

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

5. A data engineer accidentally truncated a critical table 'ORDERS' in the 'SALES DB' database. The table contained important historical order data, and the data retention period is set to the default. Which of the following options represents the MOST efficient and reliable way to recover the truncated table and its data, minimizing downtime and potential data loss?

A) Restore the entire Snowflake account to a previous point in time before the table was truncated.
B) Contact Snowflake support and request them to restore the table from a system-level backup.
C) Use the UNDROP TABLE command to restore the table. If UNDROP fails, clone the entire SALES_DB database to a point in time before the truncation using Time Travel.
D) Create a new table 'ORDERS' and manually re-insert the data from the application's logs and backups.
E) Use Time Travel to create a clone of the truncated table from a point in time before the truncation. Then, swap the original table with the cloned table.

Solutions: