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

1. You are developing a Secure UDF in Snowflake to encrypt sensitive customer data'. The UDF should only be accessible by authorized roles. Which of the following steps are essential to properly secure the UDF?

A) Setting the 'SECURITY INVOKER clause when creating the UDF to execute the UDF with the privileges of the caller.
B) Ensuring that the UDF is owned by a role with appropriate permissions and limiting access to this role.
C) Granting the EXECUTE privilege on the UDF only to the roles that require access.
D) Using the 'SECURE keyword when creating the UDF to prevent viewing the UDF definition.
E) Using masking policies instead of Secure UDFs is the recommended approach for data security

2. You have a requirement to create a UDF in Snowflake that transforms data based on a complex set of rules defined in an external Python library. The library requires specific dependencies. You also need to ensure the UDF is secure and that the code is not visible to unauthorized users. Which of the following steps MUST be taken to achieve this?

A) Create a Snowflake Anaconda environment specifying the required Python library dependencies. Then, create a Python UDF, reference the Anaconda environment, and use the 'SECURE' keyword.
B) Package all the Python libaries code into one file, then create an Javascript UDF and load/execute the python code inside the Javascript UDF.
C) Create a Python UDF and directly upload the Python library code into the UDF's body. Snowflake automatically manages dependencies for UDFs.
D) Create an external function pointing to an AWS Lambda function or Azure Function that hosts the Python code and its dependencies. Secure the external function using API integration and role-based access control.
E) Upload the Python library and its dependencies as internal stages. Create a Java UDF that executes the Python code using the 'ProcessBuilder' class. Mark the Java UDF as 'SECURE'

3. You are implementing a data pipeline to load data from AWS S3 into Snowflake. The source data consists of CSV files with a header row. Some of the CSV files have inconsistent data types in a specific column (e.g., sometimes an integer, sometimes a string). You want to use the 'COPY' command to load the data and handle these data type inconsistencies gracefully. Which of the following 'COPY' command options, used in conjunction, would BEST address this issue and avoid load failures? Assume the file format is already defined to specify CSV type, header skip, and field delimiter.

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

4. You are configuring a Snowflake Data Clean Room for two healthcare providers, 'ProviderA' and 'ProviderB', to analyze patient overlap without revealing Personally Identifiable Information (PII). Both providers have patient data in their respective Snowflake accounts, including a 'PATIENT ID' column that uniquely identifies each patient. You need to create a secure join that allows the providers to determine the number of shared patients while protecting the raw 'PATIENT ID' values. Which of the following approaches is the most secure and efficient way to achieve this using Snowflake features? Select TWO options.

A) Leverage Snowflake's differential privacy features to add noise to the patient ID data, share the modified dataset and perform a JOIN.
B) Implement tokenization of the 'PATIENT_ID' column in both ProviderA's and ProviderB's accounts. Share the tokenized values through a secure view and perform a JOIN operation on the tokens. Use a third party to deanonymize the tokens afterwards.
C) Create a hash of the 'PATIENT_ID' column in both ProviderA's and ProviderB's accounts using a consistent hashing algorithm (e.g., SHA256) and a secret salt known only to both providers. Share the hashed values through a secure view and perform a JOIN operation on the hashed values.
D) Utilize Snowflake's Secure Aggregate functions (e.g., APPROX_COUNT_DISTINCT) on the 'PATIENT_ID' column without sharing the underlying data. Each provider calculates the approximate distinct count of patient IDs, and the results are compared to estimate the overlap.
E) Share the raw 'PATIENT_ID' columns between ProviderA and ProviderB using secure data sharing, and then perform a JOIN operation in either ProviderA's or ProviderB's account.

5. You are developing a data transformation pipeline in Snowpark Python to aggregate website traffic data'. The raw data is stored in a Snowflake table named 'website_events' , which includes columns like 'event_timestamp' , 'user_id', 'page_urr , and 'event_type'. Your goal is to calculate the number of unique users visiting each page daily and store the aggregated results in a new table named Considering performance and resource efficiency, select all the statements that are correct:

A) Applying a filter early in the pipeline to remove irrelevant 'event_type' values can significantly reduce the amount of data processed in subsequent aggregation steps.
B) Defining the schema for the table before writing the aggregated results is crucial for ensuring data type consistency and optimal storage.
C) Using followed by is an efficient approach to calculate unique users per page per day.
D) Caching the 'website_eventS DataFrame using 'cache()' before performing the aggregation is always beneficial, especially if the data volume is large.
E) Using is the most efficient method for writing the aggregated results to Snowflake, regardless of data size.

Solutions: