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Snowflake DAA-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Semi-structured data processing - Complex joins, subqueries, window functions |
| Topic 2: Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Topic 3: Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Topic 4: Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Views, materialized views, CTEs - Data cleansing, standardization, type conversion |
| Topic 5: Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe |
| Topic 6: Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis |
| Topic 7: Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Align outputs with business requirements - Data visualization and reporting |
Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:
1. Given the following data:
This SELECT statement is executed:
What will be the result?
A) 2.0
B) 1.84
C) 200
D) 1.8
2. A Data Analyst is working with a table that has 1 record per day, with sales information. Which window function would calculate a 7-day moving average of sales, where SALES_DATE represents the date column?
A) AVG(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 7 PRECEDING AND
CURRENT ROW)
B) AVG(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 6 PRECEDING AND
CURRENT ROW)
C) SUM(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 6 PRECEDING AND
CURRENT ROW)
D) SUM(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 7 PRECEDING AND
CURRENT ROW)
3. Which Snowflake SQL would a Data Analyst use in a trained Cortex model named forecast_model to retrieve the components that contribute to the predictions?
A) forecast_model!FORECAST()
B) forecast_model!SHOW_EVALUATION_METRICS()
C) forecast_model!SHOW_TRAINING_LOGS()
D) forecast_model!EXPLAIN_FEATURE_IMPORTANCE()
4. A scorecard tile on a Snowsight dashboard shows a comparison between the industry average employee age (which is 34) and a company's average employee age (which is 38). The scorecard tile looks like this:
Comparison with industry average
How should this tile be interpreted?
A) 38 is the value and 12% is the percent difference from the comparison.
B) 38 is the value and 12% is the standard deviation.
C) 38 is the comparison and 12% is the average age change in the last year.
D) 38 is the comparison and 12% is the projected industry growth.
5. A Data Analyst has created a custom filter called branch_region on a Snowflake dashboard. The filter contains a list of regions where the company has stores. How should the filter be referenced in dashboard queries?
A) $branch_region
B) :branch_region
C) %branch_region
D) ::branch_region
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: B |







