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Snowflake DAA-C01 Exam Syllabus Topics:

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

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最新的 SnowPro Advanced DAA-C01 免費考試真題 (Q14-Q19):

問題 #14
A financial institution uses Snowflake to store customer transaction data'. They need to create a dashboard that visualizes daily transaction volume and average transaction amount for fraud detection purposes. This dashboard needs to be automatically updated every hour. The current dashboard query performance is slow, especially during peak hours. Given that the 'TRANSACTIONS table contains billions of rows, which of the following strategies would BEST optimize both the query performance and the automated update process?

答案：D

解題說明：
Materialized views are designed for pre-computation of aggregations, providing significant performance improvements. Scheduling a task to refresh the materialized view ensures automatic updates. Regular views are calculated at query time and would not improve performance. Increasing warehouse size (C) might help, but it's not the most efficient solution. Temporary tables are not persistent and truncation/reload is inefficient. Dashboard caching (E) does not solve underlying Snowflake performance issues.


問題 #15
What is a critical consideration when using data shares in Snowflake to join data with existing datasets?

答案：B


問題 #16
A financial institution stores transaction data with various attributes, including transaction ID, account ID, transaction type, amount, and timestamp. They need to build a dashboard to monitor transaction volumes and identify unusual patterns in real-time. The data is streaming continuously. Consider these requirements: near real-time analysis, complex pattern recognition, and the need to store the raw transaction data for auditing. Which of the following approaches is optimal, considering the need for both speed and historical data integrity?

答案：A

解題說明：
Option B is the most suitable solution. Snowflake Streams and Tasks provide a mechanism for incrementally updating a data model (in this case, a star schema) based on changes in the source data. This enables near real-time updates to aggregated tables used for the dashboard. Storing the raw data in a separate, immutable table ensures data integrity for auditing purposes. Option A might work, but managing large flattened tables with constant updates can be challenging. Option C is not efficient for complex analysis. Option D lacks the structure needed for complex joins and aggregations. Option E is irrelevant as it proposes using external data instead of processing the institution's own data.


問題 #17
A financial institution needs to collect stock ticker data for intraday trading analysis. The data source provides updates every second. They need to maintain a 5-minute rolling average of stock prices for each ticker. The system needs to be highly available and resilient to data source interruptions. Considering the need for near real-time analysis and potential data source instability, which combination of technologies and approaches would be MOST effective?

答案：C

解題說明：
A stream processing framework like Kafka is ideal for handling high-velocity data streams. Kafka provides fault tolerance and the ability to perform real-time aggregations (rolling average with tumbling window). While Snowpipe can ingest the raw data quickly, calculating the rolling average on-demand (using a VIEW) may not meet the near real-time requirement and can be inefficient. A scheduled task might not be able to handle the volume and frequency of data. The key to answering this question is understanding the need for real-time aggregation AND resilience to potential data source outages, both of which Kafka elegantly addresses.


問題 #18
You have identified inconsistencies in the data type of the 'ORDER DATE' column across several tables within your Snowflake database. Some tables store it as DATE, while others store it as VARCHAR. You need to create a unified view that presents 'ORDER DATE' consistently as DATE, handling potential conversion errors gracefully. You have to use safe aggregate operations. Which of the following approaches provides the most robust and error-tolerant solution?

答案：A

解題說明：
The most robust and error-tolerant solution is option E, using 'TRY CAST(ORDER DATE AS DATE)'. This function attempts to convert the 'ORDER_DATE' to a DATE data type, and if the conversion fails (e.g., the VARCHAR value cannot be parsed as a date), it returns NULL without raising an error. This ensures that the view creation and queries against it will not fail due to data type conversion issues. Option A, , is an older function, and ' TRY_CAST' is the preferred, more general function. Option B, 'IS_DATE is not a valid snowflake Function. Option C, ' TO DATE will fail the query if the data in column is not a valid date. Option D 'SAFE_CAST is not a valid snowflake function.


問題 #19
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