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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Time-series analysis 2. Snowflake-specific analytical features 3. Aggregate functions
Data Ingestion and Data Preparation	15%-20%	- Perform data discovery to identify what is needed from available datasets 1. Identify elements required for business goals using BI reports or SQL analysis 2. Determine the level of data granularity required 3. Evaluate required transformations (table joins, set operations, ASOF JOINS) 4. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 5. Query tables to assess data elements and statistics maintained by Snowflake - Prepare data and load into Snowflake 1. Load files using Snowsight 2. Load data from external/internal stages into a table - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Create tables and views 2. Find external data sets that correlate with available data 3. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) - Use a collection system to retrieve data 1. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 2. Retrieve data from structured sources (CSV) 3. Retrieve data from unstructured sources 4. Synthetic Data Generation - Use best practice considerations relating to data integrity structures 1. Perform table joins between parent/child tables 2. Define primary keys for tables 3. Implement constraints - Implement data processing solutions 1. Cleanse, conform, and enrich data 2. Use logging and monitoring solutions (auditing, data lineage) 3. Automate and implement data pipelines (scheduling) 4. Respond to processing failures
Data Transformation and Data Modeling	22%-30%	- Transform data using SQL 1. Common Table Expressions (CTEs) 2. PIVOT/UNPIVOT operations 3. QUALIFY clauses 4. Window functions - Design data models 1. Star schema design 2. Data vault models 3. Snowflake schema design
Data Presentation and Data Visualization	28%-29%	- Integrate with BI tools 1. Power BI integration 2. Tableau integration 3. Other partner visualization tools - Create dashboards 1. Present analytical results 2. Snowsight dashboards

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
Your organization stores clickstream data in Parquet files in an external stage 's3://your-bucket/clickstreamP. The data includes nested JSON structures representing user activity. You need to create a Snowflake table to query this data efficiently, extracting specific fields from the nested JSON. The challenge is to optimize query performance by leveraging Parquet's columnar storage and schema evolution capabilities. Which of the following approaches offers the BEST combination of performance and flexibility for querying the data in Snowflake, considering potential schema changes in the Parquet files over time?

A. Create an external table with 'AUTO_REFRESH = TRUE and query the Parquet files directly using to access nested fields. Create a view on top of the external table to simplify queries.
B. Create a Snowflake table with clearly defined columns matching the Parquet schema. Use 'COPY INTO' with a transformation to load the data. Update the table definition whenever the Parquet schema changes.
C. Load the Parquet data into a VARIANT column in a Snowflake table using 'COPY INTO'. Create a view on top of this table, using JSON EXTRACT_PATH_TEXT to extract specific fields into separate columns.
D. Create an external table with 'AUTO_REFRESH = TRUE and specify a schema on read using 'FILE_FORMAT = (TYPE = PARQUET, STRIP OUTER ARRAY = TRUE, = TRUE)'. Query the external table directly.
E. Create an external table pointing to the Parquet files. Define a materialized view on top of the external table, extracting the required fields. Refresh the materialized view periodically.

Answer: E

Explanation:
Materialized Views offer the best performance as they pre-compute and store the results, leveraging Snowflake's caching. They also adapt to schema changes in underlying Parquet files (within limits). External tables alone can be slow because of on-the-fly processing. Loading into VARIANT loses the advantage of Parquet's columnar structure. Predefined columns are rigid and don't handle schema evolution well. Creating standard views on external tables also does not provide the pre-computed benefits of the materialized views.

NEW QUESTION # 66
A Data Analyst is working with a table that has 1 record per day, with sales information. Which window function would calculate a 7-day moving average of sales, where SALES_DATE represents the date column?

A. AVG(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 7 PRECEDING AND
CURRENT ROW)
B. AVG(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 6 PRECEDING AND
CURRENT ROW)
C. SUM(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 6 PRECEDING AND
CURRENT ROW)
D. SUM(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 7 PRECEDING AND
CURRENT ROW)

Answer: B

Explanation:
Calculating a moving average (or rolling average) is a standard time-series analysis technique used to smooth out short-term fluctuations and highlight longer-term trends. In Snowflake, this is accomplished using Window Functions and the ROWS framing clause.
To calculate a 7-day moving average when you have one record per day, the "window" or "frame" must encompass exactly 7 rows. In SQL windowing syntax, the CURRENT ROW counts as one of those days.
Therefore, to reach a total of 7, you need to look back at the 6 preceding rows ($6 + 1 = 7$).
Evaluating the Options:
* Options A and B use the SUM() function. While the sum is part of an average, the question specifically asks for the average itself.
* Option D is incorrect because 7 PRECEDING AND CURRENT ROW actually creates an 8-day window (the current day plus the seven days before it).
* Option C is the 100% correct answer. It uses the AVG() aggregate function and correctly defines the frame as 6 PRECEDING AND CURRENT ROW, ensuring the calculation reflects exactly one week of data including the current day.

NEW QUESTION # 67
When performing forecasting using statistics and built-in functions, what role do these functions play?

A. They solely aid in data visualization without impacting the forecasting process.
B. These functions only apply to limited data types for forecasting.
C. They enable the creation of custom forecasting models as needed.
D. They restrict forecasting to predefined models without flexibility.

Answer: C

Explanation:
Built-in functions in statistics facilitate the creation of custom forecasting models, allowing flexibility in the forecasting process.

NEW QUESTION # 68
Consider a 'customer_orders' table with 'customer_id' , 'order_date', and 'order_amount'. You need to identify customers who have placed orders consistently over the last 3 months, specifically, you need to find customers who have placed an order in each of the last 3 months (including the current month). Assume the current date is '2024-01-15'. Which of the following query snippets, when incorporated into a complete query, would be most efficient and accurate for identifying these customers?

Answer: E

Explanation:
Option E is the most precise and efficient. It explicitly checks if a customer has an order in each of the three specific months (November, December, January). It does this by truncating the 'order_date' to the beginning of the month using 'DATE TRUNC('MONTH', order_datey and then comparing against the truncated values for the last three months calculated using 'DATEADD. The 'SUM' will only be equal to 3 if the customer has at least one order in each of those months. Option A calculates the number of distinct months for each customer but doesn't guarantee they are the last 3 months. Option B checks if the customer has placed at least 3 orders in the last 3 months, but it might be that all 3 orders are in a single month. Option C doesn't count distinct months. Option D only returns 1 if the customer has placed an order in the last 3 months. It does not guarantee the customer placed an order in all the past 3 months.

NEW QUESTION # 69
When using Snowflake functions, how do scalar functions differ from aggregate functions?

A. Scalar functions work on groups of rows.
B. Aggregate functions return single values.
C. Aggregate functions modify table structures.
D. Scalar functions operate on entire tables.

Answer: B

Explanation:
Aggregate functions return single values from groups of rows, distinguishing them from scalar functions.

NEW QUESTION # 70
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