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Snowflake DEA-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Security and Governance	15%	- Access Control 1. Role hierarchy and ownership 2. Role-based access control (RBAC) 3. GRANT and REVOKE operations - Governance and Compliance 1. Object tagging 2. Access history and auditing 3. Data retention policies 4. Row access policies - Data Security 1. Data masking and tokenization 2. Column-level security 3. Row-level security policies 4. External tokenization
Topic 2: Data Transformation with Snowflake	30%	- Snowflake Scripting 1. Error handling 2. Procedures and control flow 3. Dynamic SQL - Data Processing Patterns 1. MERGE, UPDATE, DELETE operations 2. Zero-copy cloning for ETL 3. Time travel and change data capture - SQL Transformations 1. Data type conversions and handling 2. Window functions advanced usage 3. Working with semi-structured data (VARIANT) 4. Complex JOINs and set operations
Topic 3: Data Ingestion and Consumption	20%	- Bulk Loading and Unloading 1. Data loading performance optimization 2. Handling staged files 3. COPY INTO command options and best practices 4. File format options (CSV, JSON, Parquet, AVRO) - Data Unloading 1. Unloading to internal and external stages 2. Data export best practices 3. Partitioning unloading data - Continuous Data Loading 1. Real-time data ingestion patterns 2. Snowpipe configuration and usage 3. Automating data loading with tasks
Topic 4: Performance Optimization	15%	- Query Optimization 1. Query result caching 2. Indexing strategies with clustering 3. Avoiding common performance pitfalls 4. Query profiling and analysis - Data Optimization 1. Search optimization service 2. Data cache management 3. Materialized views - Warehouse Performance 1. Warehouse scaling policies 2. Resource monitors 3. Multi-cluster warehouses 4. Warehouse sizing and selection
Topic 5: Data Architecture and Processing	20%	- Data Pipeline Design 1. Stream and task patterns 2. Pipeline monitoring and error handling 3. Data scheduling and orchestration - Data Storage Architecture 1. Table types (Permanent, Transient, Temporary) 2. Micro-partitioning and clustering 3. Hybrid Tables concepts - Data Modeling for Performance 1. Dimension handling 2. Slowly changing dimensions (SCD) 3. Star and snowflake schemas

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

1. You have created a masking policy called which redacts salary information based on the user's role. You have applied this policy to the 'SALARY column in the 'EMPLOYEES table. However, after applying the policy, you notice that even users with the 'ACCOUNTADMIN' role are seeing the masked data, which is not the intended behavior. The intention is that 'ACCOUNTADMIN' and 'SECURITYADMIN' roles should always see the real salary data'. What is the MOST likely cause of this issue and what would you suggest fix that?

A) The 'ACCOUNTADMIN' and roles need to have 'SELECT' privilege on the 'SNOWFLAKACCOUNT USAGE.MASKING POLICIES view
B) The masking policy does not explicitly exclude the 'ACCOUNTADMIN' and 'SECURITYADMIN' roles. Modify the masking policy to include a condition that checks for these roles and returns the original value if they are active. e.g., 'CASE WHEN IN ('ACCOUNTADMIN', 'SECURITYADMIN') THEN val ELSE END'
C) The 'ACCOUNTADMIN' role does not have the 'OWNERSHIP' privilege on the table. Grant the 'OWNERSHIP' privilege to 'ACCOUNTADMIN' on the 'EMPLOYEES' table.
D) The 'ACCOUNTADMIW and 'SECURITYADMIIV roles do not have the 'APPLY MASKING POLICY privilege. Grant this privilege to the roles.
E) The masking policy is not properly activated. Run the ALTER TABLE EMPLOYEES MODIFY COLUMN SALARY SET MASKING POLICY salary_mask' command again.

2. 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) 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.
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) Leverage Snowflake's differential privacy features to add noise to the patient ID data, share the modified dataset and perform a JOIN.
E) 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.

3. A data engineer is tasked with optimizing query performance on a Snowflake table named 'SALES DATA, which currently has no clustering key defined. The table contains 'SALE (unique identifier), 'SALE DATE, 'PRODUCT CATEGORY, and 'SALE AMOUNT. The business analysts frequently run queries filtering on 'SALE DATE and then aggregating by 'PRODUCT CATEGORY'. Choosing the right clustering keys for the SALES DATA table is crucial for minimizing disk 1/0 and enhancing query speed. Which of the following clustering key strategies would be MOST effective for the specified query patterns, considering both performance and the potential impact on data loading and DML operations?

A) Creating separate tables for each 'PRODUCT CATEGORY.
B) Clustering only on 'SALE DATE
C) Clustering on followed by 'SALE_DATE'.
D) Clustering only on PRODUCT_CATEGORY.
E) Clustering on 'SALE DATE followed by 'PRODUCT CATEGORY.

4. You have a Snowflake table 'orders_raw' with a VARIANT column named 'order detailS that contains an array of order items represented as JSON objects. Each object has 'item id', 'quantity' , and 'price'. You need to calculate the total revenue for each order. Which SQL statement efficiently flattens the array and calculates the total revenue using LATERAL FLATTEN and appropriate casting?

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

5. You have a Snowflake table called 'RAW ORDERS that contains semi-structured JSON data in a column named 'ORDER DETAILS. You need to extract specific fields from the JSON data, perform some data type conversions, and then load the transformed data into a relational table named 'CLEAN ORDERS'. Your requirements are as follows: 1. Extract the (STRING) from the JSON and store it as 'ORDER ID (NUMBER). 2. Extract the (STRING) from the JSON and store it as 'CUSTOMER ID (NUMBER). 3. Extract the 'order_date' (STRING) from the JSON and store it as 'ORDER DATE' (DATE). 4. Extract (STRING) from the JSON and store it as 'TOTAL AMOUNT' (FLOAT). Which of the following Snowpark Python code snippets correctly transforms the data and loads it into the 'CLEAN ORDERS table using a combination of Snowpark DataFrame operations and SQL? Assume that session 'sp' is already initialized.