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Snowflake DAA-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Complex joins, subqueries, window functions - Semi-structured data processing |
| Prepare and Present Data | 10–15% | - Data visualization and reporting - Snowsight dashboards and sharing results - Align outputs with business requirements |
| Prepare and Load Data | 15–20% | - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation - File formats: CSV, JSON, Parquet, Avro |
| Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Views, materialized views, CTEs - Handling NULLs and structuring datasets |
| Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis |
Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:
Question 1
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, VARIANCE(ACTIVE_DRIVERS, AVERAGE_WAITING_TIME) FROM
RIDE_DATA GROUP BY CITY;
C. SELECT CITY, SUM(ACTIVE_DRIVERS), AVG(AVERAGE_WAITING_TIME) FROM
RIDE_DATA GROUP BY CITY;
D. SELECT CITY, SUM(ACTIVE_DRIVERS), VARIANCE(AVERAGE_WAITING_TIME) FROM RIDE_DATA GROUP BY CITY;
Question 2
When sharing views across accounts, which function should be used on the row access policy for the secure views to give users access to rows in a base table?
A. CURRENT_AVAILABLE_ROLES
B. CURRENT_ROLE
C. CURRENT_USER
D. CURRENT_ACCOUNT
Question 3
Why would a Data Analyst use a dimensional model rather than a single flat table to meet BI requirements for a virtual warehouse? (Select TWO).
A. Combining facts and dimensions in a single flat table limits the scalability and flexibility.
B. Dimensional modelling will save on storage space since it is denormalized.
C. Dimensions and facts allow power users to run ad-hoc analyses.
D. Dimensional modelling will improve query performance over a single table.
E. Snowflake generally performs better with dimensional modelling.
Question 4
This query is run:
SQL
SELECT
customer.id,
ANY_VALUE(customer.name),
SUM(orders.value)
FROM customer
JOIN orders ON customer.id = orders.customer_id
GROUP BY customer.id;
What is the effect of ANY_VALUE in this syntax?
A. It will return some value of the expression from the group, with a non-deterministic result.
B. It will return the minimum value of those generated by the expression, with a deterministic result.
C. It will return an equivalent NULL value when the expression is evaluated.
D. It will return a value equivalent to the median of those generated by the expression, which may be a non-deterministic result.
Question 5
A Data Analyst runs a query in a Snowflake worksheet, and selects a numeric column from the result grid.
What automatically-generated contextual statistic can be visualized?
A. A key distribution
B. A histogram, displayed for all numeric, date, and time columns
C. A frequency distribution, displayed for all numeric columns
D. MIN/MAX values for the column
Solutions:
| Question 1 Answer: A | Question 2 Answer: D | Question 3 Answer: A,C | Question 4 Answer: A | Question 5 Answer: B |
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