Snowflake SnowPro Advanced DEA-C02

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

1. You are tasked with implementing a Row Access Policy (RAP) on a table 'customer_data' that contains Personally Identifiable Information (PII). The policy must meet the following requirements: 1. Data analysts with the 'ANALYST role should only see anonymized customer data (e.g., masked email addresses, hashed names). 2. Data engineers with the 'ENGINEER role should see the full, unmasked customer data for data processing purposes. 3. No other roles should have access to the data'. You create the following UDFs: 'MASK EMAIL(email address VARCHAR)': Returns an anonymized version of the email address. 'HASH NAME(name VARCHAR): Returns a hash of the customer name. Which of the following is the most efficient and secure way to implement this RAP, assuming minimal performance impact is desired?

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

2. You have a table 'ORDERS in your Snowflake database. You are implementing a new data transformation pipeline. Before deploying the pipeline to production, you want to validate the changes in a development environment. You decide to use Time Travel to create a snapshot of the 'ORDERS' table before the transformation and compare it with the transformed data'. Which sequence of SQL commands would best facilitate this validation, assuming your development database and schema structure mirrors production?

A)

B)

C)

D)

E)

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

4. A data engineering team uses Snowflake to analyze website clickstream data stored in AWS S3. The data is partitioned by year and month in the S3 bucket. They need to query the data frequently for reporting purposes but don't want to ingest the entire dataset into Snowflake due to storage costs and infrequent full dataset analysis. Which approach is the MOST efficient and cost-effective way to enable querying of this data in Snowflake?

A) Load all the data into a Snowflake table and create a materialized view on top of the table to pre-aggregate the data for reporting.
B) Use Snowflake's COPY INTO command to ingest data directly from S3 into a Snowflake table on a scheduled basis.
C) Create a Snowflake external stage pointing to the S3 bucket, define an external table on the stage, and use partitioning metadata to optimize queries.
D) Create a Snowflake internal stage, copy the necessary files into the stage, and then load the data into a Snowflake table.
E) Create a Snowpipe pointing to the S3 bucket and ingest the data continuously into a Snowflake table.

5. You are designing a data sharing solution where the consumer account needs real-time access to a secure view that aggregates data from several tables in your provider account. The consumer should not be able to see the underlying tables. Which of the following approaches offers the MOST secure and efficient way to implement this data sharing while minimizing the risk of data leakage and performance impact on your provider account?

A) Create a materialized view on top of the tables, refresh it periodically, and share the materialized view.
B) Create a shared database and grant SELECT privilege on the underlying tables directly to the consumer's role.
C) Create a secure view that joins the tables and share only the secure view using a data share.
D) Create a standard view that joins the tables and share the view using a data share. Implement row-level security policies on the underlying tables.
E) Create a UDF that encapsulates the data aggregation logic and share the UDF's result using a data share, calling the UDF on demand.

Solutions: