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Snowflake DAA-C01 Exam Syllabus Topics:

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

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최신 SnowPro Advanced DAA-C01 무료샘플문제 (Q27-Q32):

질문 # 27
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 p.ProductlD, p.ProductName, c.categoryName FROM Products p FULL OUTER JOIN categories c ON p.CategorylD = c.CategorylD;
B. 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;
C. CREATE VIEW ProductCategories AS SELECT ProductlD, ProductName, (SELECT CategoryName FROM Categories WHERE CategorylD =Products.CategorylD) AS CategoryName FROM Products;
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 INNER JOIN Categories c ONp.CategorylD = c.CategorylD;

정답：D

설명：
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.

질문 # 28
You are analyzing sales data for a retail company. The 'sales' table contains columns 'product id' (INT), 'sale date' (DATE), and 'sale_amount' (NUMBER). You need to calculate the percentage contribution of each product's sales to the total sales on each day. You want the result to include 'sale date', 'product_id', 'sale_amount', 'daily total' , and 'percentage_contribution'. Which of the following Snowflake queries achieves this correctly?

A. Option E
B. Option C
C. Option A
D. Option D
E. Option B

정답：C,D

설명：
Options A and D are correct. Option A uses a window function OVER (PARTITION BY to calculate the total sales for each day. This is efficient because it calculates the total sales for each day in a single pass. Option D uses a subquery to calculate the total sales for each day and then joins this result back to the original table. Option B calculates a single total over the entire table, not by day. Option C calculates a single total sales amount over the entire table and assigns it to every row, which is incorrect. Option E orders the sales amount by the date but doesnt correctly partition by 'sale_date' leading to cumulative sum instead.

질문 # 29
What potential problem can be identified in the Query profile below?

A. The query is not using a foreign Key.
B. There is query spilling.
C. There is an exploding join
D. There Is inefficient pruning.

정답：C

설명：
The provided image shows a specific section of a Snowflake Query Profile, which is a visual representation of the execution plan and the actual performance of a query. Analyzing this profile is a critical skill for a Data Analyst to identify performance bottlenecks.
1. Identifying the Exploding Join: The most striking evidence in this profile is the relationship between the input and output row counts of the Join [5] operator.
* Input Row Counts: The join receives approximately 95.26k rows from one branch and 150k rows from the other.
* Output Row Count: The join produces a staggering 571.6M rows.
When a join operation produces a significantly larger number of rows than the sum of its inputs, it is known as an exploding join (or a "Cartesian product-like" behavior). This typically occurs when the join condition is not restrictive enough or when there are many-to-many relationships with duplicate keys in both joining tables. In this specific case, joining on C1.C_NATIONKEY = C2.C_NATIONKEY has caused the row count to balloon from thousands to over half a billion.
2. Impact on Performance: Exploding joins consume excessive CPU and memory resources to process the massive intermediate result set. This often leads to secondary problems like spilling (Option A), where the data exceeds the virtual warehouse's memory and must be written to disk. However, the profile clearly identifies the join itself as the root cause.
Evaluating the Options:
* Option A is a symptom, but the visual evidence of row expansion directly points to the join logic itself.
* Option C (inefficient pruning) would be identified by a high percentage of partitions scanned in the Table Scan nodes, not by a row count explosion after a join.
* Option D is irrelevant; while foreign keys can help the optimizer, their absence doesn't cause this specific visual profile on its own.
* Option B is the 100% correct answer. The "exploded" arrow indicating 571.6M rows leaving a join fed by significantly smaller inputs is the textbook definition of an exploding join.

질문 # 30
When designing a data collection system, what factors should be considered when assessing how often data needs to be collected? (Select all that apply)

A. Business requirements
B. Data source availability
C. Data collection tool limitations
D. Volume of data

정답：A,D

설명：
Assessing data collection frequency involves considering business requirements and the volume of data necessary for analysis.

질문 # 31
You're working with a large dataset containing website user activity, including 'session_id', 'user_id', 'timestamp', and 'page_view'. You suspect bot activity is skewing your engagement metrics. Bots tend to have very short session durations and high page view counts within those sessions. Which of the following Snowflake SQL queries, used in combination, would be MOST effective in identifying and flagging potential bot sessions, considering performance on a large dataset? Assume you have access to Snowflake's statistical functions.

정답：B

설명：
Option E is most effective because it identifies bot sessions based on two distinct criteria: unusually high page view counts AND unusually short session durations, which are characteristic of bots. By combining these two conditions with standard deviations, it reduces the likelihood of false positives. All other answer choices only check rate and not the count or session duration .

질문 # 32
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