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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Perform Predictive Analysis	5–10%	- Forecasting and predictive modeling - Using Snowflake ML and built-in analytics
Perform Descriptive and Diagnostic Analysis	10–15%	- Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis - Statistical summarization and trend analysis
Prepare and Present Data	10–15%	- Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements
Perform Simple Data Transformations for Analysis	15–20%	- Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs
Build and Troubleshoot Advanced SQL Queries	20–25%	- Semi-structured data processing - Complex joins, subqueries, window functions - Query optimization and troubleshooting
Prepare and Load Data	15–20%	- External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro
Use Built-in Functions and Create UDFs	10–15%	- User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam DAA-C01 Prüfungsfragen mit Lösungen (Q32-Q37):

32. Frage
You have a table named 'product_sales' with columns 'product_id', 'sale date' , and 'sales_amount'. You need to calculate a 7-day moving average of the sales amount for each product. However, you also need to handle cases where there might be missing 'sale_date' entries for a product. How can you best achieve this using Snowflake window functions and without creating temporary tables or stored procedures?

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

Antwort: E

Begründung:
Option B is the most appropriate. using 'RANGE BETWEEN INTERVAL '6 day' PRECEDING AND CURRENT ROW' will calculate the average of sales amounts for the previous 6 days, regardless of whether there are missing days in the 'sale_date' column. This provides a true 7-day moving average. 'ROWS BETWEEN' (Option A) would count rows, not days, potentially including data from more than 7 days if there are multiple sales on a single day, or less than 7 days if there are missing days. 'EXCLUDE CURRENT ROW' (Option C) would exclude the current row's sales amount from the average. 'RANGE BETWEEN 6 PRECEDING AND CURRENT ROW' (Option D) would not be valid Snowflake syntax for dates. Option E will calculate cumulative average, also that WHERE clause will lead to error.

33. Frage
You are tasked with migrating data from an existing relational database to Snowflake. The database contains a 'Customers' table (CustomerlD, Name, City, State) and an 'OrderS table (OrderlD, CustomerlD, OrderDate, TotalAmount). A critical requirement is to maintain data integrity and quickly identify any orphaned records after the initial load. Which approach provides the MOST efficient and robust method to identify orphaned 'Orders' (orders where the CustomerlD does not exist in the 'Customers' table) in Snowflake, considering large data volumes and performance?

A. Use a 'LEFT OUTER JOIN' between 'OrderS and 'Customers' tables and filter for records where 'Customers.CustomerlD is NULL.
B. Load the 'Customers' table into a temporary stage, then use a stored procedure with iterative cursor-based logic to compare each 'OrderlD' against the staged customer data.
C. Load 'Customers' and 'Orders' to separate Snowflake tables. Then using a UDF, implement a fuzzy match using Levenshtein distance between 'Orders.CustomerlD and 'Customers.CustomerlD , identifying potential mismatches for manual review.
D. SELECT OrderlD FROM Orders WHERE NOT EXISTS (SELECT I FROM Customers WHERE Customers. CustomerID = Orders. CustomerID);
E. Perform a full outer join between 'Customers' and 'Orders' tables and filter for records where 'Customers.CustomerlD' is NULL. Use a 'NOT EXISTS' subquery:

Antwort: A

Begründung:
A 'LEFT OUTER JOIN' (or 'LEFT JOIN') is the most efficient and direct method for identifying orphaned records in the 'Orders' table. By joining 'Orders' to 'Customers' on 'CustomerlD, and then filtering for records where 'Customers.CustomerlD' is NULL, you directly isolate the orders with no corresponding customer. 'NOT EXISTS' (option B) can also work, but it is often less performant than a left join with a NULL check, especially with large datasets. A full outer join (option A) would show both orphaned orders and orphaned customers, which isn't the specific goal. Using a stored procedure with a cursor (option D) is highly inefficient and not scalable. The UDF fuzzy match approach (option E) is unnecessarily complex and not designed for direct identification of orphaned foreign keys.

34. Frage
A retail company is using Snowflake to store their daily sales data'. They want to predict sales for the next week, accounting for weekly seasonality and a promotional campaign running on specific days. The sales data is in a table called 'SALES DATA' with columns 'SALE DATE' (DATE) and 'SALES AMOUNT' (NUMBER). Which of the following SQL statements is the MOST efficient and accurate way to achieve this using Snowflake's forecasting features, assuming a confidence interval of 95%?

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

Antwort: C

Begründung:
Option E is the most correct. Option A uses 'timeLimit' in generator which needs additional conditions to be satisfied. Option E utilizes 'TIMESTAMP_INPUT = TRUE to correctly interpret the 'SALE_DATE column. It accurately specifies the forecast horizon (7 days) and confidence interval (0.95), and generates the future dates for forecasting using 'TABLE(GENERATOR(rowcount => 7))' to ensure the generation of 7 days for forecasting. Option D has OVER (ORDER BY NULL)' which is generally less performant than seq4().

35. Frage
You are tasked with creating a dashboard to monitor the performance of several marketing campaigns. The dashboard should allow users to quickly identify underperforming campaigns and drill down to understand the reasons. Which of the following combinations of visualizations and data interaction techniques would BEST achieve this goal? Assume that you are using Snowflake as your data source and connecting to a BI tool.

A. Abar chart comparing the revenue generated by each campaign, with a filter to select a specific date range. Implement cross-filtering such that selecting a bar filters other charts on the dashboard (e.g., demographic distribution, geographic distribution) to show data related to that campaign.
B. A table showing the campaign name, total cost, and total revenue, sorted by revenue in descending order. No drill-down capabilities are included.
C. A bullet chart comparing actual revenue to target revenue for each campaign, sorted by the difference between actual and target. Implement drill-down capabilities to view campaign-specific metrics (e.g., cost per acquisition, conversion rate) and associated customer data.
D. A scatter plot showing campaign cost vs. revenue, without any filters or drill-down capabilities.
E. Apie chart showing the percentage of total revenue generated by each campaign.

Antwort: A,C

Begründung:
Options B and E provide the best combination of visualizations and data interaction techniques. Option B uses a bar chart for easy comparison and cross-filtering to allow for quick drill-down into related data dimensions. Option E utilizes a bullet chart to immediately highlight underperforming campaigns relative to their targets and offers a drill-down for a more in-depth analysis of underlying metrics and customer data. Option A lacks drill-down. Option C is useful for understanding distribution but does not highlight performance against targets or facilitate drill-down. Option D, although useful can quickly see the campaign's performance in one glance, it does not provide in-depth insights into why the campaign isn't performing well.

36. Frage
How do Snowsight's data loading capabilities impact data ingestion?

A. Snowsight enables real-time data ingestion.
B. Data loading is restricted to batch processing only.
C. Snowsight facilitates streamlined data import from various sources.
D. It limits data ingestion to structured formats only.

Antwort: C

Begründung:
Snowsight allows streamlined data import from various sources, enhancing data ingestion capabilities.

37. Frage
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