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Snowflake DAA-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Topic 2: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 3: Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause analysis |
| Topic 4: Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation |
| Topic 5: Prepare and Present Data | 10–15% | - Align outputs with business requirements - Data visualization and reporting - Snowsight dashboards and sharing results |
| Topic 6: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Semi-structured data processing - Complex joins, subqueries, window functions - Query optimization and troubleshooting |
| Topic 7: Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Views, materialized views, CTEs - Handling NULLs and structuring datasets |
Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:
A large, complicated query is used to generate a data set for a report on the most recent month. It is taking longer than expected. A review of the Query Profile shows excessive spilling. How can the performance of the query be improved WITHOUT increasing costs?
- A. Change the source tables into external tables to establish and take advantage of custom partitioning.
- B. Split the query into multiple steps, replacing Common Table Expressions (CTEs) with temporary tables to process the data in smaller batches.
- C. Run the query against zero-copy clones of the source tables to avoid contention with other queries.
- D. Create a materialized view clustered on a date column, on the table that is causing the spilling.
Correct Answer: B 🗳️
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A Data Analyst has been asked to predict sales revenue through the end of the year. Which function will provide this information?
- A. VARIANCE
- B. REGR_SLOPE
- C. COVAR_SAMP
- D. CORR
Correct Answer: B 🗳️
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A Data Analyst needs to create a custom filter called state in a Snowflake dashboard using a SQL query.
Which query will include this custom filter?
- A. select avg(total_income), state from taxpayer_wages group by state having state = 'state';
- B. select avg(total_income), state from taxpayer_wages group by state having state = :state;
- C. select avg(total_income), state from taxpayer_wages group by state having state = state;
- D. select avg(total_income), state from taxpayer_wages group by state having state = @state;
Correct Answer: B 🗳️
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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
Correct Answer: B 🗳️
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There are two similarly-structured and sized tables, Table_a and Table_b, in a schema with data populated in both tables. A Data Analyst is running queries as part of a preliminary analysis of the data to check the MAX value of a numeric column named num which is present in both the tables:
* Query 1: SELECT MAX(num) FROM Table_a;
* Query 2: SELECT MAX(num) FROM Table_b;
After running the queries, the Analyst observed that Query 2 ran significantly slower than Query 1. Why is this occurring?
- A. Table_b has a row-access policy defined.
- B. A multi-cluster warehouse was used to run Query 1.
- C. Table_b has more rows than Table_a.
- D. The USE_CACHED_RESULT was set to FALSE before running Query 2.
Correct Answer: A 🗳️
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