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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Data Loading and Unloading	- Data ingestion methods 1. Continuous ingestion and Snowpipe concepts 2. COPY INTO and bulk loading - Data export 1. UNLOAD and external stages
Data Modeling and Performance Optimization	- Modeling approaches in Snowflake 1. Data normalization vs denormalization 2. Star and snowflake schemas - Performance tuning 1. Warehouse sizing and auto-suspend/auto-resume 2. Clustering and pruning techniques
Security, Governance, and Data Sharing	- Data sharing and governance 1. Data masking and policies 2. Secure data sharing - Access control and security 1. Authentication and encryption concepts 2. Role-based access control (RBAC)
Snowflake Architecture and Data Platform Fundamentals	- Snowflake architecture concepts 1. Virtual warehouses and scaling 2. Cloud services layer, compute layer, storage layer - Data platform fundamentals 1. Separation of storage and compute 2. Data lifecycle in Snowflake
Data Transformation and Analysis	- Analytical workloads 1. Query optimization for analytics 2. Materialized views and caching - SQL-based transformations 1. Semi-structured data (VARIANT, JSON, XML) 2. Joins, aggregations, window functions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q24-Q29):

NEW QUESTION # 24
You are working with a dataset containing customer orders and their associated products. The data is stored in two tables: 'ORDERS' (order_id, customer_id, order_date) and 'ORDER ITEMS' (order_id, product_id, quantity, price). The Bl team needs to analyze the top- selling products and the average order value per customer. However, due to data quality issues, some 'order _ id' values in the 'ORDER ITEMS table do not exist in the 'ORDERS' table, leading to data inconsistencies. What is the most robust and efficient way to handle this data inconsistency issue while ensuring accurate reporting?

A. Create a flattened view that combines 'ORDERS' and 'ORDER_ITEMS' using a FULL OUTER JOIN. Handle missing values in the reporting layer.
B. Use a UNION ALL statement to combine the two tables, creating a single table with all order and item information. Handle inconsistencies during data extraction and loading (ETL) processes.
C. Use a RIGHT JOIN from 'ORDERS' to 'ORDER ITEMS' and filter out rows where order details are missing.
D. Use a LEFT JOIN from 'ORDERS' to 'ORDER ITEMS to include all orders, even if they don't have corresponding items. Filter out rows with NULL 'product_id' in the final result.
E. Use an INNER JOIN between 'ORDERS' and 'ORDER ITEMS'. Before joining, use a subquery or CTE to filter 'ORDER ITEMS' to include only 'order_id' values that exist in 'ORDERS'. After joining apply a filter for valid prices(Price > 0). This ensures correct analysis and avoids invalid orders. Select all the rows with valid prices (Price > 0).

Answer: E

Explanation:
Option B provides the most robust and efficient solution. By using an INNER JOIN after filtering the 'ORDER_ITEMS' table to include only valid 'order_id' values (those present in the 'ORDERS' table), you ensure that the analysis is based on consistent and reliable data. The subquery/CTE effectively cleans the data before joining, preventing inaccurate calculations. Filtering for valid prices ensures data quality. Option A would include orders without item details, which is not desired for this analysis. Options C and D might result in inaccurate results due to the incomplete relationship between orders and products. Option E is not suitable since it mixes the distinct column sets, resulting in inaccurate analyses. Always ensure referential integrity where business logic dictates.

NEW QUESTION # 25
You have a Snowflake table 'CUSTOMER DATA' containing customer information. You want to enrich this data using two separate data shares from the Snowflake Marketplace. Share A provides demographic information, and Share B provides credit risk scores. Both shares contain views named 'CUSTOMER ENRICHMENT with a common column 'CUSTOMER ID'. Due to compliance requirements, you need to ensure that only customers with a credit risk score above a certain threshold (e.g., 700) are enriched with demographic data'. Which of the following approaches ensures that the customer data is enriched securely, efficiently, and in compliance with the credit risk threshold?

A. Create a stored procedure that iterates through the 'CUSTOMER_DATA' table, retrieves demographic and credit risk information for each customer, applies the credit risk threshold, and inserts the enriched data into a new table.
B. Create a task that periodically runs a query that joins 'CUSTOMER DATA with both shared views (CUSTOMER ENRICHMENT from Share A and Share B), filtering based on the credit risk threshold. Insert the results into a new enriched table.
C. Create two separate views, one for each data share, and then join them based on in a final view, filtering for the credit risk threshold in the final view.
D. Replicate data from both data shares and perform enrichment and credit risk filtering on the replicated data.
E. Create a single view that joins 'CUSTOMER_DATA' with both shared views ('CUSTOMER_ENRICHMENT from Share A and Share B) using a common table expression (CTE) to filter records from Share B to include only customers with a credit risk score above the defined threshold.

Answer: E

Explanation:
Option C is the most secure, efficient, and compliant approach. Using a single view with a CTE allows you to encapsulate the credit risk filtering logic within the view definition, ensuring that only customers meeting the threshold are enriched. This avoids exposing sensitive credit risk information to unauthorized users. Views provide row-level security for the table. Options A requires two steps and is less efficient, Option B is less efficient and harder to maintain than a view, and Options D is not recommended for sharing scenarios. Option E will require an additional task creation. Using single view with CTE will simplify the query to implement it with minimum code.

NEW QUESTION # 26
What are the PRIMARY reasons for using integrity constraints on Snowflake tables? (Select TWO).

A. To enforce that a value in a foreign key must match the value of a primary or unique key in a parent table.
B. To allow for clustering on the primary and foreign keys by specifying them as clustering keys.
C. To enforce that records with a UNIQUE column must have all distinct values in that column.
D. To enforce that records with a NOT NULL column must have values in that column.
E. To enable team members to see how tables relate to one another.

Answer: D,E

Explanation:
Understanding how Snowflake handles integrity constraints is vital, as it differs significantly from traditional transactional databases like PostgreSQL or SQL Server. In Snowflake, most constraints are not enforced by the system, with one major exception.
* Enforcement vs. Documentation: Snowflake does not enforce PRIMARY KEY, FOREIGN KEY, or UNIQUE constraints during data loading or updates. If you define a primary key, Snowflake will still allow duplicate values to be inserted. The primary reason for including these is for documentation and metadata (Option C), allowing data analysts and BI tools to understand the intended relationships and schema design.
* The NOT NULL Exception: The only integrity constraint that Snowflake actively enforces is NOT NULL (Option B). If a column is defined as NOT NULL, any attempt to insert or update a record with a null value in that column will result in an error.
Evaluating the Options:
* Options A and D are incorrect because Snowflake does not actually enforce these constraints; it merely stores them as metadata.
* Option E is incorrect because while keys can be used for clustering, defining them as constraints is not a prerequisite for specifying them as clustering keys.
* Options B and C are the 100% correct reasons. They represent the practical application (enforcing data quality for nulls) and the architectural application (providing context for the data model).

NEW QUESTION # 27
How do Snowsight dashboards facilitate the presentation of data for business use analyses?

A. Snowsight doesn't support visual data representation.
B. Snowsight limits data representation options, hindering analyses.
C. They enable diverse data representation for effective analyses.
D. Snowsight dashboards are exclusively text-based, limiting analyses.

Answer: C

Explanation:
Snowsight dashboards enable diverse data representation for effective analyses in business use cases.

NEW QUESTION # 28
You're building a dashboard to monitor the performance of various marketing campaigns. The data resides in Snowflake, and you're using a BI tool that supports direct query The table has columns: 'CAMPAIGN ID, 'DATE, 'IMPRESSIONS', 'CLICKS, 'SPEND , and You need to create a calculated field in the BI tool representing the Cost Per Conversion (CPC), but you want to optimize query performance and avoid division by zero errors. Assume 'SPEND' and 'CONVERSIONS' are both numeric columns. Which SQL expression, suitable for use in a direct query BI tool, is the MOST performant and robust way to calculate CPC, avoiding zero conversion issues?

Answer: B

Explanation:
The ' DIV0' function is specifically designed by Snowflake to handle division by zero gracefully, returning NULL. It's the most concise and performant way to achieve the desired result. 'NULLIF(CONVERSIONS, 0)' is also correct way, but DIVO is more accurate for Snowflake environment. 'CASE WHEN' and 'IFF are functionally equivalent in this scenario, but is shorter to write. ZEROIFNULL' will return 0 when input is null which won't solve the zero conversion issues. Furthermore ZEROIFNULL' is not a valid Snowflake function.

NEW QUESTION # 29
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