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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: Prepare and Present Data	10–15%	- Data visualization and reporting - Align outputs with business requirements - Snowsight dashboards and sharing results
Topic 3: Build and Troubleshoot Advanced SQL Queries	20–25%	- Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting
Topic 4: Perform Simple Data Transformations for Analysis	15–20%	- Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs
Topic 5: Perform Descriptive and Diagnostic Analysis	10–15%	- Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause analysis
Topic 6: Prepare and Load Data	15–20%	- External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro
Topic 7: Use Built-in Functions and Create UDFs	10–15%	- Scalar, aggregate, table, system functions - User-Defined Functions (UDFs)

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q21-Q26):

NEW QUESTION # 21
How do statistics and built-in functions contribute to forecasting based on data?

A. Statistics and functions hinder the forecasting process by complicating data visualization.
B. They solely aid in data aggregation for forecasting purposes.
C. These functions enable the creation of custom forecasting models, offering flexibility.
D. They limit forecasting to predefined models without flexibility.

Answer: C

Explanation:
Statistics and built-in functions facilitate the creation of custom forecasting models, providing flexibility.

NEW QUESTION # 22
You are tasked with cleaning and transforming a dataset containing customer information in Snowflake. This dataset includes columns such as 'CUSTOMER ID, 'NAME', 'EMAIL', 'PHONE NUMBER', and 'ADDRESS'. Your goal is to implement several data quality rules: 1) Ensure all phone numbers are in a consistent '+1-XXX-XXX-XXXX' format (where X is a digit). 2) Remove any leading or trailing whitespace from the 'NAME and 'ADDRESS' columns. 3) Replace invalid email addresses (identified by failing a specific regex pattern) with NULL. Which combination of Snowflake SQL statements would efficiently and accurately address these requirements?

Answer: E

Explanation:
Option D correctly addresses all requirements by performing all transformations within a single CREATE OR REPLACE TABLE statement. This ensures atomicity and avoids multiple update operations. It correctly trims NAME and ADDRESS, replaces invalid emails with NULL, and formats phone numbers if they have exactly 10 digits after removing non-numeric characters, otherwise assigning NULL. The correct option must format phone number as well as replace with NULL, while not replacing with NULL would mean keep original. Option E keeps original value not NULL, if phone number doesn't match criteria.

NEW QUESTION # 23
You are using a Snowflake Marketplace data feed that provides daily stock prices. The data is updated daily, and you need to create a process to automatically load the new data into your existing 'STOCK PRICES' table. The Marketplace data feed provides a view called 'MARKETPLACE STOCK PRICES' with columns 'DATE' (DATE), 'SYMBOL' (VARCHAR), and 'PRICE (NUMBER). Your 'STOCK PRICES' table has the same columns. Which of the following Snowflake features or techniques would be BEST suited for automatically loading the new data each day, ensuring that duplicate entries for the same 'DATE and 'SYMBOL' are avoided?

A. Create a Stream on the 'MARKETPLACE STOCK PRICES view, and then create a Snowflake Task that runs daily and processes the stream, inserting only new or changed data into the table. Schedule the Stream refresh frequency to once every day to match the new data feed frequency.
B. Create a Snowflake Task that runs daily and executes a simple 'INSERT INTO STOCK_PRICES SELECT FROM statement.
C. Create a Snowflake Pipe that automatically ingests data from the view into the 'STOCK_PRICES table whenever new data is available.
D. Use Snowpipe Auto-Ingest with an external stage pointing to the Snowflake Marketplace data feed. Configure the pipe to copy new files from the Marketplace's internal stage to your table.
E. Create a Snowflake Task that runs daily and executes a 'MERGE statement to insert new data from into 'STOCK PRICES' , updating existing records if a match exists based on and 'SYMBOLS and inserting new records if there is no match. Also add a condition to remove the duplicate entries for the same 'DATE and ' SYMBOL'

Answer: E

Explanation:
Option C is the most appropriate. A 'MERGE statement allows for both inserting new records and updating existing records based on a matching condition (in this case, and 'SYMBOL). This ensures that new data is added, and existing data can be updated if necessary. It also allows for handling potential duplicate scenarios. Option A will simply insert all data, leading to duplicates. Option B is not applicable as Snowpipe ingests from stages not views. Option D is incorrect as Snowpipe is designed for external stages, not internal Marketplace feeds. Option E, while a valid approach for change data capture, is more complex and unnecessary for a simple daily load, also the marketplace feeds don't support change data capture through Streams

NEW QUESTION # 24
How do constraints contribute to ensuring data integrity in Snowflake?

A. They impose rules for maintaining data accuracy and consistency.
B. They enforce data security measures.
C. Constraints ensure redundant data storage.
D. Constraints restrict data import from external sources.

Answer: A

Explanation:
Constraints in Snowflake impose rules for maintaining data accuracy and consistency, ensuring data integrity.

NEW QUESTION # 25
You are a data analyst at a retail company. You need to calculate the total sales for each product category, but only for categories where the average unit price is above $50 and the number of sales transactions exceeds 1000. Which Snowflake SQL query would efficiently achieve this?

Answer: B

Explanation:
Option D correctly uses the HAVING clause to filter aggregated results based on both the average unit price and the count of transactions. Option A does not consider distinct transaction_id, Option B includes the aggregate functions in where clause which is not valid, option C uses subquery which adds overhead, option E uses QUALIFY that is not suitable to this scenario.

NEW QUESTION # 26
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