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Snowflake DAA-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Semi-structured data processing - Query optimization and troubleshooting - Complex joins, subqueries, window functions |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis |
| Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs |
| Prepare and Present Data | 10–15% | - Align outputs with business requirements - Snowsight dashboards and sharing results - Data visualization and reporting |
| Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation |
Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:
A Data Analyst for a ride-sharing company needs to assess the relationship between the number of active drivers in a city, and the average waiting time for passengers. Which query will determine if an increase in the number of active drivers is associated with a decrease in the average waiting time?
- A. SELECT CITY, CORR(ACTIVE_DRIVERS, AVERAGE_WAITING_TIME) FROM RIDE_DATA GROUP BY CITY;
- B. SELECT CITY, SUM(ACTIVE_DRIVERS), AVG(AVERAGE_WAITING_TIME) FROM
RIDE_DATA GROUP BY CITY; - C. SELECT CITY, SUM(ACTIVE_DRIVERS), VARIANCE(AVERAGE_WAITING_TIME) FROM RIDE_DATA GROUP BY CITY;
- D. SELECT CITY, VARIANCE(ACTIVE_DRIVERS, AVERAGE_WAITING_TIME) FROM
RIDE_DATA GROUP BY CITY;
Correct Answer: A 🗳️
Explanation: Only visible for TestValid members. You can sign-up / login (it's free).
Which Snowflake feature or object can be used to dynamically create and execute SQL statements?
- A. System-defined functions
- B. Stored procedures
- C. Tasks
- D. User-Defined Functions (UDFs)
Correct Answer: B 🗳️
Explanation: Only visible for TestValid members. You can sign-up / login (it's free).
The image shows a table with a variant column that is storing a JSON record:
This SQL query is run:
What will be the result?
- A.
- B.
- C.
- D.
Correct Answer: C 🗳️
Explanation: Only visible for TestValid members. You can sign-up / login (it's free).
What are the PRIMARY reasons for using integrity constraints on Snowflake tables? (Select TWO).
- A. To enforce that records with a NOT NULL column must have values in that column.
- B. To enforce that records with a UNIQUE column must have all distinct values in that column.
- C. To enforce that a value in a foreign key must match the value of a primary or unique key in a parent table.
- D. To enable team members to see how tables relate to one another.
- E. To allow for clustering on the primary and foreign keys by specifying them as clustering keys.
Correct Answer: A,D 🗳️
Explanation: Only visible for TestValid members. You can sign-up / login (it's free).
What will be the output of this query?
SELECT 100::FLOAT * 20.78::INTEGER || ' Square Feet';
- A. 2078.00 Square Feet
- B. 2078 Square Feet
- C. A conversion error
- D. 2100 Square Feet
Correct Answer: D 🗳️
Explanation: Only visible for TestValid members. You can sign-up / login (it's free).
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