DAA-C01日本語練習問題 & DAA-C01日本語資格取得

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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Transformation and Data Modeling22%-30%- Transform data using SQL
  • 1. Window functions
  • 2. PIVOT/UNPIVOT operations
  • 3. Common Table Expressions (CTEs)
  • 4. QUALIFY clauses
- Design data models
  • 1. Data vault models
  • 2. Snowflake schema design
  • 3. Star schema design
Topic 2: Data Presentation and Data Visualization28%-29%- Create dashboards
  • 1. Present analytical results
  • 2. Snowsight dashboards
- Integrate with BI tools
  • 1. Power BI integration
  • 2. Tableau integration
  • 3. Other partner visualization tools
Topic 3: Data Ingestion and Data Preparation15%-20%- Use best practice considerations relating to data integrity structures
  • 1. Implement constraints
  • 2. Perform table joins between parent/child tables
  • 3. Define primary keys for tables
- Perform data discovery to identify what is needed from available datasets
  • 1. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 2. Identify elements required for business goals using BI reports or SQL analysis
  • 3. Query tables to assess data elements and statistics maintained by Snowflake
  • 4. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 5. Determine the level of data granularity required
- Implement data processing solutions
  • 1. Automate and implement data pipelines (scheduling)
  • 2. Cleanse, conform, and enrich data
  • 3. Respond to processing failures
  • 4. Use logging and monitoring solutions (auditing, data lineage)
- 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
- Prepare data and load into Snowflake
  • 1. Load files using Snowsight
  • 2. Load data from external/internal stages into a table
- Use a collection system to retrieve data
  • 1. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 2. Retrieve data from unstructured sources
  • 3. Synthetic Data Generation
  • 4. Retrieve data from structured sources (CSV)
Topic 4: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Snowflake-specific analytical features
  • 2. Time-series analysis
  • 3. Aggregate functions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam 認定 DAA-C01 試験問題 (Q65-Q70):

質問 # 65
While loading data into Snowflake using named file formats, a file format defined in which location has precedence?

正解：D

解説：
In the Snowflake data loading hierarchy, understanding the order of precedence for file format options is critical for troubleshooting and automation. Snowflake allows you to define file format options (such as CSV delimiter, header settings, or compression type) at multiple levels: as a standalone named File Format object, within a Stage definition, or directly inside a COPY INTO statement.
When data is loaded, Snowflake evaluates these definitions based on a specific override logic. The COPY INTO statement sits at the top of this hierarchy. This means that any file format options or named file formats explicitly called out in the COPY INTO command will override any conflicting settings defined at the stage or table level. For example, if a stage is defined to expect comma-separated files, but the COPY INTO command specifies a pipe delimiter, Snowflake will use the pipe delimiter for that specific load operation.
Evaluating the Options:
* Options A, B, and C are incorrect because while they provide default behaviors for specific objects or namespaces, they are considered "lower-level" configurations.
* Option D is the 100% correct answer. The COPY INTO statement provides the final, explicit instruction to the query engine. This design allows Data Analysts to use general-purpose stages for various file types by simply specifying the unique format requirements at the time of the execution, providing maximum flexibility in the Data Ingestion workflow.


質問 # 66
When creating reports and dashboards, how does evaluating data based on business requirements impact the visualization process?

正解：D

解説：
Evaluating data based on business requirements ensures the dashboard contains relevant and useful content, improving its quality.


質問 # 67
A data analyst is working with a large table partitioned by (DATE type). The table contains millions of rows spanning several years. They need to optimize a query that retrieves sales data for a specific quarter of 2023. The initial query is: 'SELECT FROM sales_data WHERE EXTRACT(YEAR FROM sale_date) = 2023 AND EXTRACT(QUARTER FROM sale_date) = To improve performance using partition pruning, which of the following queries is the MOST efficient alternative?

正解：D

解説：
Option A is the most efficient because it directly uses the 'sale_date' column with a 'BETWEEN' clause using specific date values. This allows Snowflake to directly leverage the partition pruning based on the date range. Options B and C use functions C YEAR, 'QUARTER on the 'sale_date' column, preventing efficient partition pruning. Option D uses 'LIKE', which is not suitable for date comparisons and would likely result in a full table scan, furthermore 'LIKE operator will not work with Date Data type. Option E does not prune to a specific quarter.


質問 # 68
You are designing a data warehouse for a retail company. The company needs to analyze sales data based on product category, customer demographics, and store location. The sales data is initially stored in a semi-structured JSON format with nested arrays for product details and customer information. The BI team requires optimized query performance for aggregations across these dimensions. Which approach is most suitable for this scenario?

正解：B

解説：
Option B is the most suitable approach. A flattened relational data model with separate tables and foreign keys allows for efficient querying and aggregations across different dimensions, which is a key requirement for BI reporting. Flattening the data reduces the overhead of parsing JSON during query execution and enables the use of standard SQL aggregation functions. Option A can lead to performance issues with complex JSON structures. Option D can lead to data redundancy and update anomalies. Option C offers a hybrid approach but can still be inefficient for certain queries. Option E relies too heavily on Snowflake's automatic optimization and will likely underperform compared to a properly designed data model.


質問 # 69
You're working with a Snowflake database containing sales transaction data'. The 'SALES' table includes columns 'transaction id' , product id', 'customer id', 'transaction_date', and 'sales amount'. The Bl team needs to analyze sales trends, identify top-selling products, and segment customers based on their purchase behavior. They also need to support ad-hoc queries with various filtering and aggregation criteria'. The data volume is significant, and query performance is a key concern. Consider the following Snowflake table definition (simplified):

Which of the following SQL queries, combined with appropriate data modeling techniques, will provide the BEST performance for analyzing monthly sales trends by product category, assuming a separate 'PRODUCTS table exists with 'product_id' and 'category' columns?

正解：C

解説：
Option B provides the best performance because it uses a materialized view to pre- compute the monthly sales trends by product category. Subsequent queries to the materialized view will be significantly faster than re-calculating the aggregation each time. Option A performs the join and aggregation every time the query is executed. Option C uses a correlated subquery, which is generally less performant than a join. Option D uses , which might provide the same result but doesn't address the performance issue of recalculating the aggregation every time. Option E uses a temporary table which is unncessary because a materialized view can be created directly from the SALES and PRODUCTS table.


質問 # 70
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