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Snowflake DAA-C01 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Transformation and Analysis | - Analytical workloads
|
| Topic 2: Snowflake Architecture and Data Platform Fundamentals | - Data platform fundamentals
|
| Topic 3: Security, Governance, and Data Sharing | - Data sharing and governance
|
| Topic 4: Data Modeling and Performance Optimization | - Performance tuning
|
| Topic 5: Data Loading and Unloading | - Data export
|
Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:
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 more rows than Table_a.
- B. A multi-cluster warehouse was used to run Query 1.
- C. Table_b has a row-access policy defined.
- D. The USE_CACHED_RESULT was set to FALSE before running Query 2.
Correct Answer: C 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
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, SUM(ACTIVE_DRIVERS), AVG(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), VARIANCE(AVERAGE_WAITING_TIME) FROM RIDE_DATA GROUP BY CITY;
- D. SELECT CITY, CORR(ACTIVE_DRIVERS, AVERAGE_WAITING_TIME) FROM RIDE_DATA GROUP BY CITY;
Correct Answer: D 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
A Data Analyst executes a query in a Snowflake worksheet that returns the total number of daily sales, and the total amount for each sale. How can the Analyst check the distribution of the total amount, without running the query again?
- A. Call the WIDTH_BUCKET function.
- B. Go to Chart and select a histogram that includes the two variables.
- C. Go to Chart and select a bar chart that contains the two variables.
- D. Click on the column header in the results and review the histogram.
Correct Answer: D 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
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. Dimensional modelling will save on storage space since it is denormalized.
- B. Dimensions and facts allow power users to run ad-hoc analyses.
- C. Combining facts and dimensions in a single flat table limits the scalability and flexibility.
- D. Dimensional modelling will improve query performance over a single table.
- E. Snowflake generally performs better with dimensional modelling.
Correct Answer: B,C 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
A Data Analyst created a cost overview dashboard in Snowsight. Management has asked for a system date filter to easily change the time period and refresh the data in all dashboard tiles with a single filter selection.
The system date filter is shown below:
The Analyst wants to apply the filter onto individual dashboard components.
Adding which where clause to the queries will apply the filter as required?
- A. Where start_time = :date_filter
- B. Where start_time >= dateadd('days', -7, CURRENT_TIMESTAMP())
- C. Where start_time >= dateadd('days', -7, SYSDATE())
- D. Where start_time = :daterange
Correct Answer: D 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
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