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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam 認定 DAA-C01 試験問題 (Q22-Q27):

質問 # 22
You are analyzing sales data in Snowflake to identify seasonal trends and patterns. You have a table 'SALES DATA with columns 'SALE DATE (DATE) and 'SALE_AMOUNT (NUMBER). Which of the following SQL queries and visualization techniques would be MOST effective in identifying and visualizing these seasonal trends? Assume the data spans several years.

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

正解：A

解説：
Option C is the most effective because it combines weekly sales aggregation with a box plot analysis of monthly sales across multiple years. The weekly aggregation provides a granular view of sales trends, while the box plot effectively visualizes the distribution of sales for each month, allowing for easy identification of monthly seasonal patterns and outliers. Option A only shows monthly sales volume, not the distribution of sales within each month across years. Option B shows the yearly trend, not seasonal variations. Option D doesn't aggregate the data and hence can't show you the seasonality. Option E only shows the daily variance across weeks.

質問 # 23
You're building a Snowflake forecasting model to predict website traffic. Your dataset contains 'VISIT DATE (DATE), 'PAGE VIEWS (NUMBER), and 'PROMOTION FLAG' (BOOLEAN, indicating whether a promotion was active that day). You suspect that promotional periods significantly impact traffic, but need to account for days after a promotion that show residual impact. Which of the following strategies can you employ to improve your forecasting model to handle promotion and their lagging effects. Select two correct options.

A. Use the 'HOLIDAY_DETECTION' parameter in the model creation statement. Snowflake will automatically detect promotions as holidays and incorporate them into the forecast.
B. Create a new feature called 'DAYS SINCE PROMOTION' that calculates the number of days since the last promotion. Include this feature in the model's INPUT.
C. Use a simple moving average on the 'PAGE VIEWS' column over a 7-day period, ignoring the 'PROMOTION FLAG' entirely, as Snowflake's forecasting will automatically learn the promotional effects through the averaged data.
D. Create multiple lagged features for 'PROMOTION FLAG'. For example, 'PROMOTION FLAG LAGI' would be the 'PROMOTION FLAG' value from the previous day, from two days ago, and so on. Include these lagged features in the model's INPUT.
E. Remove the 'PROMOTION FLAG' column entirely, as promotions introduce too much noise in the data and make accurate forecasting impossible.

正解：B、D

解説：
Options A and C are correct. Option A helps the model directly capture the time elapsed since a promotion, allowing it to learn the decaying effect. Option C captures the lagged effects of promotions by including ' PROMOTION_FLAG' values from previous days as separate features. Option B is incorrect because simple moving average is a bad approach that may not be able to learn complex patterns of promotion effects on forecasting data, moreover promotional periods will be ignored. Option D is incorrect as promotions are valuable signals, not noise. Option E is incorrect because Snowflake's 'HOLIDAY DETECTION' feature automatically deals with typical public holidays, not self defined promotional campaigns.

質問 # 24
When selecting and implementing an effective data model, what considerations are crucial for ensuring its suitability for BI requirements? (Select all that apply)

A. Extensive data denormalization
B. Scalability and flexibility
C. Performance and ease of maintenance
D. Conformity to specific database standards only

正解：B、C

解説：
Effective data models should ensure scalability, flexibility, good performance, and ease of maintenance to meet BI requirements effectively.

質問 # 25
A data analyst needs to perform an exploratory ad-hoc analysis on a large customer order dataset in Snowflake to identify potential fraud. The dataset contains sensitive Personally Identifiable Information (PII). Which of the following approaches BEST balances the need for ad-hoc analysis with data security and compliance?

A. Grant the analyst direct access to the production database with no restrictions, relying on the analyst's ethical responsibility.
B. Mask the PII columns using SnoMlake's Dynamic Data Masking feature in the production environment and grant the analyst access to the production database.
C. Create a clone of the production database and grant the analyst full access to the clone.
D. Export a sample of the data to a local environment, removing all PII, and provide the sample to the analyst.
E. Use Snowflake's data sharing capabilities to share a subset of the production data with the analyst, applying row-level security to filter out sensitive data.

正解：B

解説：
Dynamic Data Masking allows the analyst to perform ad-hoc analysis on the production data without exposing sensitive PII. Cloning with full access is risky. Exporting a sample may not be representative. Unrestricted access is a security violation. Data sharing with row-level security can be complex and might still inadvertently expose data.

質問 # 26
When performing a diagnostic analysis, what action aids in identifying demographics and relationships? (Select all that apply)

A. Ignoring data relationships for focused analysis
B. Collecting related data
C. Analyzing statistical trends
D. Focusing solely on isolated data points

正解：B、C

解説：
Analyzing statistical trends and collecting related data are crucial in identifying demographics and relationships in diagnostic analysis.

質問 # 27
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