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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Use Built-in Functions and Create UDFs10–15%- User-Defined Functions (UDFs)
- Scalar, aggregate, table, system functions
Build and Troubleshoot Advanced SQL Queries20–25%- Query optimization and troubleshooting
- Complex joins, subqueries, window functions
- Semi-structured data processing
Prepare and Present Data10–15%- Data visualization and reporting
- Snowsight dashboards and sharing results
- Align outputs with business requirements
Prepare and Load Data15–20%- Data ingestion methods: COPY INTO, stages, Snowpipe
- External tables and data validation
- File formats: CSV, JSON, Parquet, Avro
Perform Simple Data Transformations for Analysis15–20%- Data cleansing, standardization, type conversion
- Views, materialized views, CTEs
- Handling NULLs and structuring datasets
Perform Predictive Analysis5–10%- Forecasting and predictive modeling
- Using Snowflake ML and built-in analytics
Perform Descriptive and Diagnostic Analysis10–15%- 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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