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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. 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?

A) Create a masking policy on the base table but use a context function in the masking policy condition to check the database name. Mask the data only when the database name is the non-production database.
B) Create a masking policy directly on the 'CUSTOMER EMAIL' column in the base table. This will automatically apply the masking to all views referencing the column.
C) Create masking policies on each of the individual views that reference the 'CUSTOMER EMAIL' column, using the same masking function.
D) Create a masking policy on the base table, but exclude the role used by the views from the policy's condition. This will prevent masking for those specific views.
E) Create a separate view that applies the masking function to the 'CUSTOMER EMAIL' column. Replace all existing views with the new masked view.

2. You are monitoring a Snowpipe pipeline that loads data from an external stage into a Snowflake table. You observe the following error messages in the PIPE ERRORS view: 'Invalid UTF-8 detected in string'. The data files on the stage are encoded in UTF-8. Which of the following actions, taken individually or in combination, are MOST likely to resolve this issue? (Select TWO)

A) Drop and recreate the external stage with 'TYPE = INTERNAL'.
B) Modify the COPY INTO statement to include the 'ON ERROR = 'SKIP_FILE" option.
C) Ensure the file format definition explicitly specifies 'ENCODING = 'UTF8".
D) Verify the data files on the stage are actually valid UTF-8 and contain no corrupted characters.
E) Convert the problematic files to UTF-16 encoding before loading them into the stage.

3. 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) 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.
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) Leverage Snowflake's differential privacy features to add noise to the patient ID data, share the modified dataset and perform a JOIN.

4. A data engineering team is building a real-time fraud detection system. They have a large 'TRANSACTIONS table that grows rapidly. They need to calculate the average transaction amount per merchant daily. The following query is used:

This query is run every hour and is performance-critical. Which of the following materialized view definitions would provide the BEST performance improvement, considering the need for near real-time data and minimal latency?

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

5. A Snowflake data warehouse is experiencing performance degradation. A critical reporting dashboard, which relies on a complex SQL query, is taking significantly longer to execute. You've examined the query profile and identified that a user-defined function (UDF) called 'PARSE ADDRESS, written in Python, is consuming a large portion of the execution time. The 'PARSE ADDRESS UDF is used to parse address strings into individual components (street, city, state, zip). What are the most effective strategies to improve the performance of this query, assuming it's difficult to rewrite the query without the UDF?

A) convert the Python UDF 'PARSE_ADDRESS' into a Java UDE
B) Increase the virtual warehouse size to compensate for the UDF's overhead.
C) Rewrite the Python UDF 'PARSE ADDRESS in SQL using Snowflake's built-in string functions.
D) Utilize external functions with a more performant compute service (e.g. AWS Lambda) if the environment allows.
E) Create a materialized view that pre-calculates the output of the 'PARSE ADDRESS UDF for frequently queried address values and joins with the original table.

Solutions: