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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
What challenges might arise when handling JSON data in Snowflake using built-in functions for traversing, flattening, and nesting? (Select all that apply)

A. Difficulty in handling nested data structures
B. Complexities in parsing and querying nested JSON objects
C. Limitations in processing specific JSON file sizes
D. Restrictions in data type conversions

Answer: A,B

Explanation:
Challenges include handling nested structures and complexities in parsing and querying nested JSON objects, which might arise when manipulating JSON data in Snowflake.

NEW QUESTION # 13
You are designing a data warehouse for a retail company. The "SALES table stores transaction data and includes columns like 'TRANSACTION ID', 'PRODUCT ID', 'CUSTOMER ID, 'SALE DATE', and 'SALE AMOUNT'. The 'PRODUCT ID' references the 'PRODUCTS table, and 'CUSTOMER references the 'CUSTOMERS' table. Which of the following strategies represent the MOST optimal approach to define primary keys in this scenario, considering Snowflake's best practices and the need for efficient query performance, assuming 'TRANSACTION ID' is globally unique?

A. Define as the primary key on the 'SALES' table. Do not define primary keys on 'PRODUCTS' or 'CUSTOMERS'
B. Define ' TRANSACTION_ID' as the primary key on the 'SALES' table. Define 'PRODUCT_ID' as the primary key on the 'PRODUCTS' table, and 'CUSTOMER ID as the primary key on the 'CUSTOMERS table. Then, create unique indexes on 'PRODUCT ID in 'SALES referencing 'PRODUCTS and in referencing 'CUSTOMERS'.
C. Do not define primary keys on any of the tables. Rely solely on Snowflake's internal optimizations.
D. Define 'TRANSACTION as the primary key on the 'SALES' table, 'PRODUCT ID as the primary key on the 'PRODUCTS' table, and 'CUSTOMER ID' as the primary key on the 'CUSTOMERS' table.
E. Define a composite primary key on the 'SALES' table consisting of 'TRANSACTION D', 'PRODUCT ID, and 'CUSTOMER ID. Define 'PRODUCT ID' as the primary key on the 'PRODUCTS' table, and 'CUSTOMER_ID' as the primary key on the 'CUSTOMERS' table.

Answer: D

Explanation:
While Snowflake does not enforce primary key constraints, defining them provides valuable metadata for the query optimizer. Since TRANSACTION_ID' is unique in 'SALES', it is a suitable primary key. Defining primary keys on 'PRODUCTS' and 'CUSTOMERS' is also appropriate for their respective tables. Creating unique indexes is redundant if primary keys are defined. Skipping primary key definitions entirely can hinder optimization. A composite key in 'SALES' is unnecessary as 'TRANSACTION_ID is already globally unique.

NEW QUESTION # 14
In Snowflake, how does leveraging Time Travel contribute to handling data anomalies and ensuring data integrity?

A. Provides real-time data updates
B. Restricts data access for specific user roles
C. Enables querying data as of specific timestamps
D. Validates data types for consistency

Answer: C

Explanation:
Time Travel in Snowflake allows querying data as of specific timestamps, aiding in addressing data anomalies and ensuring data integrity by accessing historical data versions.

NEW QUESTION # 15
You have a table named 'sales_data' with a column 'product_details' of type VARIANT containing JSON data for various products. The JSON structure is inconsistent; some products have a 'size' attribute as a string, while others have it as an integer, and some don't have the attribute at all. You need to extract the 'size' as a consistent numeric value (NULL if it's missing) for analysis. Which SQL statement using table functions and data type conversion techniques correctly and efficiently handles this data inconsistency?

Answer: E

Explanation:
TRY_TO_NUMBER() is the most robust and concise way to handle potential data type inconsistencies and missing values when extracting data from VARIANT columns. It attempts to convert the value to a NUMBER and returns NULL if the conversion fails. This elegantly handles both string and integer representations of 'size', as well as cases where 'size' is missing from the JSON. Using Case statements or IS_NUMBER requires more verbose logic and can be less efficient than using the built-in function. NVL replaces NULL with 0, which isn't suitable as the question asks for NULL if missing.

NEW QUESTION # 16
You're building a Snowflake forecasting model to predict website traffic. Your dataset contains 'VISIT DATE (DATE), 'PAGE VIEWS (NUMBER), and 'PROMOTION FLAG' (BOOLEAN, indicating whether a promotion was active that day). You suspect that promotional periods significantly impact traffic, but need to account for days after a promotion that show residual impact. Which of the following strategies can you employ to improve your forecasting model to handle promotion and their lagging effects. Select two correct options.

A. Remove the 'PROMOTION FLAG' column entirely, as promotions introduce too much noise in the data and make accurate forecasting impossible.
B. Create multiple lagged features for 'PROMOTION FLAG'. For example, 'PROMOTION FLAG LAGI' would be the 'PROMOTION FLAG' value from the previous day, from two days ago, and so on. Include these lagged features in the model's INPUT.
C. Create a new feature called 'DAYS SINCE PROMOTION' that calculates the number of days since the last promotion. Include this feature in the model's INPUT.
D. Use a simple moving average on the 'PAGE VIEWS' column over a 7-day period, ignoring the 'PROMOTION FLAG' entirely, as Snowflake's forecasting will automatically learn the promotional effects through the averaged data.
E. Use the 'HOLIDAY_DETECTION' parameter in the model creation statement. Snowflake will automatically detect promotions as holidays and incorporate them into the forecast.

Answer: B,C

Explanation:
Options A and C are correct. Option A helps the model directly capture the time elapsed since a promotion, allowing it to learn the decaying effect. Option C captures the lagged effects of promotions by including ' PROMOTION_FLAG' values from previous days as separate features. Option B is incorrect because simple moving average is a bad approach that may not be able to learn complex patterns of promotion effects on forecasting data, moreover promotional periods will be ignored. Option D is incorrect as promotions are valuable signals, not noise. Option E is incorrect because Snowflake's 'HOLIDAY DETECTION' feature automatically deals with typical public holidays, not self defined promotional campaigns.

NEW QUESTION # 17
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