DAA-C01専門知識訓練 & DAA-C01受験対策

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Security, Governance, and Data Sharing	- Access control and security 1. Authentication and encryption concepts 2. Role-based access control (RBAC) - Data sharing and governance 1. Data masking and policies 2. Secure data sharing
Topic 2: Data Loading and Unloading	- Data ingestion methods 1. Continuous ingestion and Snowpipe concepts 2. COPY INTO and bulk loading - Data export 1. UNLOAD and external stages
Topic 3: Data Modeling and Performance Optimization	- Performance tuning 1. Clustering and pruning techniques 2. Warehouse sizing and auto-suspend/auto-resume - Modeling approaches in Snowflake 1. Data normalization vs denormalization 2. Star and snowflake schemas
Topic 4: Snowflake Architecture and Data Platform Fundamentals	- Snowflake architecture concepts 1. Cloud services layer, compute layer, storage layer 2. Virtual warehouses and scaling - Data platform fundamentals 1. Data lifecycle in Snowflake 2. Separation of storage and compute
Topic 5: Data Transformation and Analysis	- SQL-based transformations 1. Semi-structured data (VARIANT, JSON, XML) 2. Joins, aggregations, window functions - Analytical workloads 1. Materialized views and caching 2. Query optimization for analytics

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Snowflake SnowPro Advanced: Data Analyst Certification Exam 認定 DAA-C01 試験問題 (Q10-Q15):

質問 # 10
You are tasked with loading JSON files into Snowflake using Snowsight. The JSON files are semi-structured and contain nested arrays and objects. You want to flatten the JSON structure during the load process to facilitate easier querying. Which of the following Snowsight-integrated features or approaches are suitable for flattening the JSON data during the load process? Select all that apply.

A. Use Snowsight's 'Load Data' wizard along with Snowflake's built-in functions like 'GET_PATH' and operator within the computed column expressions during the load process to extract and load specific elements from the JSON data into individual columns.
B. Load the JSON data into a staging table as raw JSON strings. Then, write a custom Python User-Defined Function (UDF) that parses the JSON strings and returns a flattened representation of the data. Use this UDF in a 'CREATE TABLE AS SELECT statement to load the flattened data into a new table.
C. Use Snowsight's 'Load Data' wizard to load the JSON files directly into relational tables by defining mappings between JSON elements and table columns. Snowsight automatically flattens the JSON structure based on the defined mappings.
D. Load the JSON data into a VARIANT column in Snowflake. Then, use SQL with 'LATERAL FLATTEN' to query and extract data from the nested structures, creating a view to represent the flattened data.
E. Create a Snowpipe that automatically loads new JSON files from a stage into a Snowflake table. Within the Snowpipe definition, define a transformation that uses 'LATERAL FLATTEN' to flatten the JSON data before loading it into the target table.

正解：A、D、E

解説：
Options A, C, and E are all suitable approaches for flattening JSON data. Option A leverages 'LATERAL FLATTEN' in SQL, which is a common and efficient way to flatten JSON structures after loading into a VARIANT column. Option C utilizes Snowpipe with a transformation including 'LATERAL FLATTEN' , enabling automatic flattening during ingestion. Option E uses computed columns in Snowsight to extract JSON elements during loading using 'GET_PATH' or the operator. Option B is incorrect; Snowsight's 'Load Data' wizard doesn't automatically flatten JSON during the initial load. It mainly handles the basic load of the JSON file into a VARIANT column, not automatic flattening. Option D is a valid but less performant alternative; UDFs can be slower than native Snowflake SQL functions for large datasets.

質問 # 11
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 B
B. Option A
C. Option E
D. Option C
E. Option D

正解：D

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 # 12
How do constraints, such as primary keys, impact table joins between parent/child tables in Snowflake?

A. Constraints restrict data access to authorized user roles
B. Primary keys allow only one-to-one table joins
C. Primary keys ensure data uniqueness for table relationships
D. Constraints limit table join operations to specific data types

正解：C

解説：
Primary keys enforce data uniqueness, ensuring integrity and maintaining relationships between parent and child tables in Snowflake.

質問 # 13
A marketing team wants to visualize customer engagement across different channels (email, website, social media). They have engagement data stored in a Snowflake table 'CUSTOMER ENGAGEMENT' with columns 'CUSTOMER ID, 'CHANNEL', 'EVENT TYPE', 'EVENT TIMESTAMP', and 'EVENT VALUE' (representing engagement score). They need a dashboard that efficiently displays the total engagement score per channel for the last 7 days, allowing for quick drill-down into individual customer behavior. Which approach provides the MOST optimized query and view structure for this requirement, prioritizing performance for a large dataset?

A. Use a stored procedure to calculate the engagement score and update a temporary table daily. The dashboard queries this temporary table.
B. Create a view that directly aggregates data from 'CUSTOMER_ENGAGEMENT for the last 7 days in the dashboard query itself. Use 'GROUP BY CHANNEL' and ' SUM(EVENT_BALUE)'.
C. Load the entire 'CUSTOMER ENGAGEMENT table into the dashboard tool's internal memory and perform the aggregation and filtering there.
D. Create a materialized view 'MV CUSTOMER ENGAGEMENT that pre-aggregates the total engagement score per channel for the last 7 days, refreshed daily using a Snowflake task. The dashboard query then selects directly from this materialized view.
E. Create a standard view that aggregates data from 'CUSTOMER ENGAGEMENT using 'GROUP BY CHANNEL' , but without filtering for the last 7 days. Filter for the last 7 days within the dashboard query.

正解：D

解説：
Materialized views (B) are the most performant option because they pre-compute and store the aggregated data. A daily refresh using a Snowflake task ensures the data is up-to-date. Option A requires the aggregation to be performed every time the dashboard is loaded. Option C is less efficient than B because it filters after aggregation. Option D is not scalable for large datasets. Option E adds unnecessary complexity and potential performance bottlenecks with the stored procedure and temporary table.

質問 # 14
You are working with a dataset containing customer orders and their associated products. The data is stored in two tables: 'ORDERS' (order_id, customer_id, order_date) and 'ORDER ITEMS' (order_id, product_id, quantity, price). The Bl team needs to analyze the top- selling products and the average order value per customer. However, due to data quality issues, some 'order _ id' values in the 'ORDER ITEMS table do not exist in the 'ORDERS' table, leading to data inconsistencies. What is the most robust and efficient way to handle this data inconsistency issue while ensuring accurate reporting?

A. Use a LEFT JOIN from 'ORDERS' to 'ORDER ITEMS to include all orders, even if they don't have corresponding items. Filter out rows with NULL 'product_id' in the final result.
B. Use a RIGHT JOIN from 'ORDERS' to 'ORDER ITEMS' and filter out rows where order details are missing.
C. Use a UNION ALL statement to combine the two tables, creating a single table with all order and item information. Handle inconsistencies during data extraction and loading (ETL) processes.
D. Use an INNER JOIN between 'ORDERS' and 'ORDER ITEMS'. Before joining, use a subquery or CTE to filter 'ORDER ITEMS' to include only 'order_id' values that exist in 'ORDERS'. After joining apply a filter for valid prices(Price > 0). This ensures correct analysis and avoids invalid orders. Select all the rows with valid prices (Price > 0).
E. Create a flattened view that combines 'ORDERS' and 'ORDER_ITEMS' using a FULL OUTER JOIN. Handle missing values in the reporting layer.

正解：D

解説：
Option B provides the most robust and efficient solution. By using an INNER JOIN after filtering the 'ORDER_ITEMS' table to include only valid 'order_id' values (those present in the 'ORDERS' table), you ensure that the analysis is based on consistent and reliable data. The subquery/CTE effectively cleans the data before joining, preventing inaccurate calculations. Filtering for valid prices ensures data quality. Option A would include orders without item details, which is not desired for this analysis. Options C and D might result in inaccurate results due to the incomplete relationship between orders and products. Option E is not suitable since it mixes the distinct column sets, resulting in inaccurate analyses. Always ensure referential integrity where business logic dictates.

質問 # 15
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