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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Perform Descriptive and Diagnostic Analysis	10–15%	- Exploratory and ad-hoc analysis - Statistical summarization and trend 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 Predictive Analysis	5–10%	- Forecasting and predictive modeling - Using Snowflake ML and built-in analytics
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
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%	- Align outputs with business requirements - Snowsight dashboards and sharing results - Data visualization and reporting

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q37-Q42):

NEW QUESTION # 37
You're working with product catalog data in Snowflake. The product information is stored in a table named 'PRODUCTS' , and a key attribute, 'attributes' , contains a semi-structured JSON object for each product. This 'attributes' object can have varying keys, but you are interested in extracting specific keys and pivoting them into columns. The relevant JSON structure is as follows : { "color": "red", "size": "L", "material": "cotton", "style": "casual"} '"What method is the MOST efficient to transform this data to a relational structure, assuming you want to analyze product attributes such as 'color' and 'size' as separate columns?

A. Creating a view with direct JSON path accessors (e.g., for each desired attribute.
B. Using LATERAL FLATTEN to unnest the 'attributes' and then using a CASE statement to pivot the data.
C. Creating a new table with a 'VARIANT column for the attributes and performing transformations in a BI tool.
D. Using dynamic SQL to generate a query that extracts the required attributes using JSON path accessors and then creates a new table.
E. Using a stored procedure to iterate through each row, parse the JSON, and update a new table with pivoted columns.

Answer: A

Explanation:
Option B is the most efficient. Directly accessing the JSON elements using path accessors like allows Snowflake to optimize the query execution, which typically offers superior performance compared to flattening and pivoting with 'CASE statements. Flattening (Option A) introduces unnecessary complexity and overhead when specific attributes are known and desired. Options C and D are generally inefficient and should be avoided for this type of transformation. Creating a view is more performant and simple. Option E is overkill and introduces complexity that isn't needed since the required attributes are known.

NEW QUESTION # 38
When handling Parquet files in Snowflake, what limitations or challenges might arise? (Select all that apply)

A. Restrictions in metadata retrieval
B. Constraints in querying nested Parquet structures
C. Issues in handling large Parquet files
D. Difficulties in accessing specific file types

Answer: B,C

Explanation:
Challenges include handling large Parquet files and querying nested Parquet structures, which might pose limitations or complexities when working with Parquet files in Snowflake.

NEW QUESTION # 39
You have a large table 'WEB EVENTS with columns 'EVENT TIMESTAMP, 'USER ID', 'PAGE URL', and 'EVENT _ TYPE. You need to create a materialized view that efficiently calculates the daily unique user count for a specific set of 'PAGE URL' values. The 'WEB EVENTS table is frequently updated. Which of the following approaches would be MOST performant and scalable for this scenario?

A. Create a materialized view that directly selects 'COUNT(DISTINCT USER_ID)' grouped by ' and with filtering on the desired 'PAGE URL' values.
B. Create a materialized view using a window function to calculate the running total of unique users each day, then extract the final value for each day.
C. Ingest the daily unique user count data via a 3rd party tool into a new table and create a view using that table.
D. Create a materialized view that first calculates the total number of events for each user on each day and then aggregates that data to calculate the unique user count.
E. Create a standard view that filters the 'WEB_EVENTS' table and calculates 'COUNT(DISTINCT USER_ID)' grouped by and 'PAGE URL'.

Answer: A

Explanation:
Calculating 'COUNT(DISTINCT directly in the materialized view is the most efficient approach. Pre-calculating unnecessary aggregates adds overhead. Standard views do not provide the performance benefits of materialized views. Window functions are generally less performant than direct aggregations in this scenario, and the new data can be calculated directly in Snowflake, avoiding dependency with 3rd party tools.

NEW QUESTION # 40
A Snowflake table 'transactions' stores data about financial transactions. The table includes the following columns: 'transaction_id' (INTEGER), 'account_id' (INTEGER), 'transaction_date' (DATE), and 'transaction_amount' (NUMBER). You need to analyze the moving average of transaction amounts for each account over a 7-day window. The moving average should be calculated for each transaction date, considering the 3 preceding days, the current day, and the 3 following days. You want to show the 'account_id' , 'transaction_date', 'transaction amount', and the calculated 'moving_average". What's the most appropriate and efficient Snowflake query to perform this calculation?

A. Option B
B. Option A
C. Option C
D. Option E
E. Option D

Answer: A

Explanation:
Option B is the correct answer. The 'PARTITION BY account_id' clause ensures that the moving average is calculated separately for each account. The 'ORDER BY transaction_date ASCS clause orders the transactions within each account by date. The 'ROWS BETWEEN 3 PRECEDING AND 3 FOLLOWING' clause defines the window frame as the current row, the 3 preceding rows, and the 3 following rows based on row number (not date ranges). Option A doesn't partition by account_id, so the moving average will be across all accounts which is not what we want. Option C uses 'RANGE instead of which relies on finding all entries within that date range, that may have many entries making the numbers incorrect. Option D only considers preceding rows, not following ones. Option E doesn't have any windowing and therefore the data is calculated to the current point, without consideration for following or previous days.

NEW QUESTION # 41
Consider a table 'customer_orderS with columns 'order_id' (INT), 'customer_id' (INT), 'order_date' (DATE), and 'order_total' (NUMBER). The table is partitioned by 'order_date'. You need to create a materialized view that summarizes the total order value per customer, per month. Which of the following materialized view definitions will both achieve the desired summarization and effectively leverage partition pruning for efficient refreshes?

A. Option C
B. Option A
C. Option E
D. Option B
E. Option D

Answer: B

Explanation:
Option A is the best choice. It groups by and 'DATE TRUNC('month', order_datey , which creates a monthly aggregation. Since the base table is partitioned by 'order_date' , the materialized view refresh can efficiently use partition pruning based on the monthly date truncations. Option B groups by the full 'order_date' , leading to daily aggregations instead of monthly. Option C uses , which is less efficient and doesn't preserve the year information for partition pruning. Option D uses "CONVERT_TIMEZONE' , which is incorrect for the purpose and will not efficiently leverage partition pruning.Option E using CAST(order_date AS VARCHAR(7)) as it change the date data type.

NEW QUESTION # 42
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