2026 Valid Snowflake Exam DAA-C01 Review

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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q54-Q59):

NEW QUESTION # 54
How does the utilization of Regular views differ from Materialized views in data analysis?

A. Regular views offer better query performance compared to Materialized views.
B. Materialized views provide a persisted snapshot, unlike Regular views.
C. Materialized views offer limited data accessibility compared to Regular views.
D. Regular views are exclusively used for exploratory analyses.

Answer: B

Explanation:
Materialized views provide a persisted snapshot of data, differentiating them from Regular views.

NEW QUESTION # 55
A data analyst is tasked with creating a near-real-time dashboard using Streamlit and Snowflake to monitor website traffic. The website traffic data is continuously ingested into a Snowflake table named 'WEB TRAFFIC EVENTS' with columns 'EVENT TIME' (TIMESTAMP LTZ), 'PAGE URL' (VARCHAR), and 'USER ID' (VARCHAR). The analyst wants to ensure the Streamlit dashboard automatically reflects the latest data in Snowflake with minimal latency. Which of the following approaches would provide the MOST efficient and near-real-time data updates in the Streamlit dashboard?

A. Leverage Snowflake's Change Data Capture (CDC) capabilities to track changes in the ' table. Streamlit can then query the CDC stream to retrieve only the changes since the last refresh, minimizing the amount of data transferred and processed. Utilize 'st.cache_data' with a short TTL.
B. Use Streamlit's 'st.cache_data' decorator with a long TTL (time-to-live) on a function that queries Snowflake directly for each dashboard refresh. This will cache the query results for a longer period, reducing the load on Snowflake.
C. Implement Snowflake's Snowpipe to continuously load data into a separate summary table containing pre-aggregated metrics (e.g., page views per minute). Streamlit can then query this summary table frequently without impacting Snowflake's performance. Use 'st.cache_data' with a short TTL to refresh the data quickly.
D. Use Streamlit's to directly edit data in the Snowflake table from the dashboard. This provides real-time updates by allowing users to modify the data directly.
E. Configure Streamlit to use the Snowflake Connector for Python with auto-commit enabled. This ensures that any changes made to the data in Streamlit are immediately reflected in Snowflake.

Answer: A

Explanation:
Leveraging Snowflake's CDC capabilities is the most efficient way to achieve near-real-time data updates in Streamlit. CDC allows you to retrieve only the changes made to the table since the last refresh, significantly reducing the amount of data transferred and processed compared to querying the entire table each time. Snowpipe is a good option, but is more oriented to data ingestion and not data changes. Option A caches results, which is opposite of real time. Option B is useful, but more complex than needed. Option C is not related to the problem. Option D is incorrect because 'auto-commit' applies only to data updates from the application to Snowflake. Streamlit can then query this CDC stream to get an updated set of data. Using 'st.cache_data' with a short TTL ensures the dashboard reflects recent data changes from the CDC Stream.

NEW QUESTION # 56
A Snowflake table 'transactions' stores data about financial transactions. The table includes the following columns: 'transaction_id' (INTEGER), 'account_id' (INTEGER), 'transaction_date' (DATE), and 'transaction_amount' (NUMBER). You need to analyze the moving average of transaction amounts for each account over a 7-day window. The moving average should be calculated for each transaction date, considering the 3 preceding days, the current day, and the 3 following days. You want to show the 'account_id' , 'transaction_date', 'transaction amount', and the calculated 'moving_average". What's the most appropriate and efficient Snowflake query to perform this calculation?

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

Answer: D

Explanation:
Option B is the correct answer. The 'PARTITION BY account_id' clause ensures that the moving average is calculated separately for each account. The 'ORDER BY transaction_date ASCS clause orders the transactions within each account by date. The 'ROWS BETWEEN 3 PRECEDING AND 3 FOLLOWING' clause defines the window frame as the current row, the 3 preceding rows, and the 3 following rows based on row number (not date ranges). Option A doesn't partition by account_id, so the moving average will be across all accounts which is not what we want. Option C uses 'RANGE instead of which relies on finding all entries within that date range, that may have many entries making the numbers incorrect. Option D only considers preceding rows, not following ones. Option E doesn't have any windowing and therefore the data is calculated to the current point, without consideration for following or previous days.

NEW QUESTION # 57
A retail company wants to understand the relationship between promotional campaigns and sales uplift across different store locations and product categories. You have the following Snowflake tables: 'SALES': 'transaction id', 'store id', 'product_category', 'sale date', 'sale_amount' 'PROMOTIONS': 'promotion id', 'store id', 'product category', 'promotion start date', promotion_end_date', 'discount_percentage' Which analytical approach and corresponding SQL query would be MOST effective in determining if specific promotional campaigns consistently result in a statistically significant sales uplift, considering potential variations across different store locations and product categories? Assume you want to compare sales during the promotion period to a control period (before the promotion). (Select TWO)

A. Calculate the average sales amount for each store and product category during promotion periods and compare it to the overall average sales amount across all periods using a t-test or similar statistical test to determine significance. This would involve exporting data into a statistical tool.
B. Create a complex query that joins SALES and PROMOTIONS and calculates the percentage increase in sales during the promotion period compared to the average sales for the same store and product category in the 3 months prior, ignoring the potential for seasonality and other confounding factors.
C. Employ a difference-in-differences (DID) approach, comparing the change in sales from the control period to the promotion period for the 'treatment group' (stores/product categories with promotions) relative to the change for a 'control group' (stores/product categories without promotions). This requires careful identification of suitable control groups.
D. Use a simple linear regression model in a Snowflake UDF (User-Defined Function) to predict sales based on the presence or absence of a promotion, without accounting for store or product category fixed effects.
E. Run a series of AIB tests by randomly assigning different discount percentages to different stores and product categories during the promotion period and track the resulting sales uplift, using a Mann-Whitney U test for statistical significance.

Answer: A,C

Explanation:
Options A and C are the most effective. Option A utilizes t-tests to assess the statistical significance of sales during promotion periods versus the overall average. Combining statistical analysis with Snowflake data extraction provides insightful results. Option C proposes the Difference-in-Differences (DID) approach which is very effective. It uses a control group to account for external factors that may have also influenced sales. Comparing treated (promotion stores) and controlled store to find the diff in diff provides statistically significant evidence. Options B and E are less rigorous. B doesn't account for important fixed effects and E doesn't consider seasonality and confounding factors. Option D suggests an AIB test which is not practical as sales can not be assigned randomly during a promotion.

NEW QUESTION # 58
In Snowflake, how does leveraging Time Travel contribute to handling data anomalies and ensuring data integrity?

A. Restricts data access for specific user roles
B. Enables querying data as of specific timestamps
C. Provides real-time data updates
D. Validates data types for consistency

Answer: B

Explanation:
Time Travel in Snowflake allows querying data as of specific timestamps, aiding in addressing data anomalies and ensuring data integrity by accessing historical data versions.

NEW QUESTION # 59
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