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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Transformation and Data Modeling	22%-30%	- Transform data using SQL 1. Window functions 2. Common Table Expressions (CTEs) 3. PIVOT/UNPIVOT operations 4. QUALIFY clauses - Design data models 1. Star schema design 2. Snowflake schema design 3. Data vault models
Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Aggregate functions 2. Snowflake-specific analytical features 3. Time-series analysis
Data Presentation and Data Visualization	28%-29%	- Integrate with BI tools 1. Tableau integration 2. Other partner visualization tools 3. Power BI integration - Create dashboards 1. Snowsight dashboards 2. Present analytical results
Data Ingestion and Data Preparation	15%-20%	- Use a collection system to retrieve data 1. Retrieve data from unstructured sources 2. Retrieve data from structured sources (CSV) 3. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 4. Synthetic Data Generation - Perform data discovery to identify what is needed from available datasets 1. Identify elements required for business goals using BI reports or SQL analysis 2. Determine the level of data granularity required 3. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 4. Query tables to assess data elements and statistics maintained by Snowflake 5. Evaluate required transformations (table joins, set operations, ASOF JOINS) - Prepare data and load into Snowflake 1. Load data from external/internal stages into a table 2. Load files using Snowsight - Use best practice considerations relating to data integrity structures 1. Perform table joins between parent/child tables 2. Implement constraints 3. Define primary keys for tables - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Create tables and views 2. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) 3. Find external data sets that correlate with available data - Implement data processing solutions 1. Respond to processing failures 2. Automate and implement data pipelines (scheduling) 3. Use logging and monitoring solutions (auditing, data lineage) 4. Cleanse, conform, and enrich data

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q37-Q42):

NEW QUESTION # 37
You are preparing data for analysis in Snowflake. You have two tables: 'Products' (ProductlD, ProductName, CategorylD) and 'Categories' (CategorylD, CategoryName). You need to create a view that combines product and category information, but also needs to efficiently handle situations where a 'ProductlD' might be missing a 'CategorylD' assignment in the 'Products' table. Furthermore, you need to ensure the view performs optimally for ad-hoc querying by analysts. Which of the following SQL statements creates the MOST performant and robust view, ensuring minimal impact from missing 'CategorylD' values?

A. CREATE VIEW ProductCategories AS SELECT ProductlD, ProductName, (SELECT CategoryName FROM Categories WHERE CategorylD =Products.CategorylD) AS CategoryName FROM Products;
B. CREATE VIEW ProductCategories AS SELECT p.ProductlD, p.ProductName, c.CategoryName FROM Products p INNER JOIN Categories c ONp.CategorylD = c.CategorylD;
C. CREATE OR REPLACE VIEW ProductCategories AS SELECT p.ProductlD, p.ProductName, CASE WHEN c.CategoryName IS NULL THEN 'Unknown' ELSE c.CategoryName END AS CategoryName FROM Products p LEFT JOIN Categories c ON p.CategorylD = c.CategorylD;
D. CREATE VIEW ProductCategories AS SELECT p.ProductlD, p.ProductName, NVL(c.CategoryName, 'Unknown') AS CategoryName FROM Products p LEFT JOIN Categories c ON p.CategorylD = c.CategorylD;
E. CREATE VIEW ProductCategories AS SELECT p.ProductlD, p.ProductName, c.categoryName FROM Products p FULL OUTER JOIN categories c ON p.CategorylD = c.CategorylD;

Answer: D

Explanation:
The best option is B. It uses a 'LEFT JOIN' to ensure all products are included, even those without a 'CategorylD' in the 'Categories' table. ' NVL' efficiently handles 'NULL' values in 'CategoryName' by replacing them with 'Unknown'. A 'FULL OUTER JOIN' (option A) is not appropriate as we specifically need all Products. Using 'CASE WHEN' (option C) is functionally equivalent to 'NVL' but often less efficient. 'INNER JOIN' (option D) will exclude products without a matching 'Categoryl[Y. The subquery approach (option E) is highly inefficient and will not perform well, especially with large tables.

NEW QUESTION # 38
The following code is run:

Then this statement is executed:

What will be the output of this statement?

Answer: C

Explanation:
To determine the correct output, a Data Analyst must understand the behavior of the ILIKE operator, wildcard characters, and the ESCAPE clause in Snowflake.
1. Pattern Matching with ILIKE: The ILIKE operator performs case-insensitive pattern matching. The provided pattern is 'p%

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