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Snowflake DEA-C02 Exam Syllabus Topics:

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

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

1. You are developing a Snowpark Python application that processes data from a large table. You want to optimize the performance by leveraging Snowpark's data skipping capabilities. The table 'CUSTOMER ORDERS is partitioned by 'ORDER DATE. Which of the following Snowpark operations will MOST effectively utilize data skipping during data transformation?

A) Creating a new DataFrame with only the columns needed using 'ORDER_DATE', 'ORDER_AMOUNT')' before any filtering operations.
B) Using the 'cache()' method on the DataFrame before filtering by 'ORDER DATE
C) Applying a filter >= '2023-01-01') & (col('ORDER_DATE') <= '2023-03-31'))' after performing a complex join operation.
D) Executing 'df.collect()' to load the entire table into the client's memory before filtering.
E) Applying a filter '2023-01-01') & '2023-03-31'))' before performing any join or aggregation operations.

2. You are designing a data warehouse for an e-commerce company. One of the requirements is to provide fast analytics on order fulfillment times by region. You have two tables: 'ORDERS: Contains order information, including ID, 'ORDER DATE, 'REGION ID, and 'FULFILLMENT DATE. 'REGIONS': Contains region information, including 'REGION ID' and Due to the large size of the 'ORDERS' table and the complexity of calculating fulfillment times, you decide to use materialized views.
Which of the following combinations of materialized view definition and Snowflake features would BEST optimize query performance and minimize data staleness for this scenario? Choose two options.

A) Create a materialized view that joins 'ORDERS and 'REGIONS', calculates the difference between 'FULFILLMENT DATE' and 'ORDER DATE as , and groups by REGION_NAME. Cluster the view by ' REGION_NAME.
B) Partition the 'ORDERS' table by 'ORDER_DATE and create a materialized view that calculates 'FULFILLMENT_TIME grouped by REGION_NAME , clustering by 'ORDER DATE'
C) Create a materialized view that joins 'ORDERS and 'REGIONS', calculates 'FULFILLMENT TIME', and groups by 'REGION NAME'. Do not specify a clustering key.
D) create a materialized view that joins 'ORDERS' and 'REGIONS', calculates 'FULFILLMENT_TIME' grouped by 'REGION_NAME, and cluster by 'REGION NAM Configure incremental data refreshes.
E) Use Snowflake's search optimization service on the 'ORDERS' table instead of creating a materialized view.

3. A data engineer needs to optimize the performance of a series of complex transformations performed using Snowflake stored procedures. These procedures involve multiple table joins, aggregations, and data filtering operations. The current execution time is unacceptably long. Which of the following optimization strategies are most likely to provide the greatest performance improvements, considering both code-level optimizations and Snowflake's architecture? Select all that apply.

A) Use external functions written in languages like Python or Java for computationally intensive tasks that are not efficiently handled by SQL, taking advantage of Snowflake's support for external functions.
B) Implement data caching within the stored procedures using temporary tables to store intermediate results, reducing the need to repeatedly query the same data.
C) Refactor the stored procedures to leverage set-based operations (e.g., using SQL statements with joins and aggregations) whenever possible, allowing Snowflake to parallelize the execution across multiple nodes.
D) Rewrite the stored procedures to use procedural logic (e.g., loops and cursors) more extensively, as this allows for fine-grained control over the execution flow and can improve performance for certain operations.
E) Ensure the underlying tables are appropriately clustered and consider using materialized views for frequently accessed aggregations or pre-computed results to improve query performance.

4. You have a Snowflake table, 'CUSTOMER ORDERS', with columns like 'CUSTOMER ID', 'ORDER DATE', 'ORDER AMOUNT', and 'REGION'. A Bl dashboard relies on a query that aggregates data from this table, but the query performance is consistently slow. The query frequently filters by 'ORDER DATE and groups by 'REGION'. Based on the following 'EXPLAIN' output, which combination of techniques should be considered to improve the performance the most?

A) Create a materialized view that pre-aggregates the data by 'ORDER DATE and 'REGION'
B) Create an index on the 'ORDER DATE column.
C) Increase the virtual warehouse size to 'LARGE or 'XLARGE.
D) Redesign the dashboard to minimize the data being displayed at once to the user.
E) Cluster the 'CUSTOMER ORDERS table on 'ORDER DATE' and 'REGION'.

5. You have a Snowflake table 'CUSTOMER DATA with a column 'EMAIL' containing customer email addresses. You need to classify this column as 'PII' using a tag named 'SENSITIVITY' and value 'CONFIDENTIAL'. Also, you want all queries accessing this 'EMAIL' column to be logged, with specific details about who accessed it and when. You already have appropriate roles and privileges to perform the required operations. Which of the following SQL statements, when executed in sequence, will achieve this goal, assuming appropriate logging mechanisms are already configured to read from the Snowflake ACCESS HISTORY view?

A)

B)

C)

D)

E)