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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q58-Q63):

NEW QUESTION # 58
A data analyst needs to create a view named 'DAILY SALES SUMMARY that provides a daily summary of sales data from the 'SALES TRANSACTIONS' table. The 'SALES TRANSACTIONS' table contains columns like 'TRANSACTION , 'TRANSACTION DATE, 'PRODUCT ID', 'SALES_AMOUNT, and 'CUSTOMER ID. They want to optimize query performance for frequently run reports that use this summary Which of the following approaches provides the BEST performance for the reports while minimizing maintenance overhead?

A. Create a regular view and manually refresh it daily using a scheduled task.
B. create a table named and insert into it using a scheduled task: 'CREATE TABLE AS SELECT TRANSACTION_DATE, FROM SALES TRANSACTIONS GROUP BY TRANSACTION DATE;'
C. Create a secure view: 'CREATE OR REPLACE SECURE VIEW DAILY SALES SUMMARY AS SELECT TRANSACTION_DATE, FROM SALES TRANSACTIONS GROUP BY TRANSACTION DATE;'
D. Create a standard view: 'CREATE OR REPLACE VIEW DAILY SALES SUMMARY AS SELECT TRANSACTION_DATE, FROM SALES TRANSACTIONS GROUP BY TRANSACTION DATE;'
E. Create a materialized view: 'CREATE OR REPLACE MATERIALIZED VIEW DAILY SALES SUMMARY AS SELECT TRANSACTION DATE, FROM SALES_TRANSACTIONS GROUP BY TRANSACTION_DATE;'

Answer: E

Explanation:
Materialized views pre-compute and store the results of the query, providing the fastest query performance for read-heavy workloads. Snowflake automatically manages the refresh of materialized views, reducing maintenance overhead. Regular and secure views re-execute the query each time they are accessed. Manually refreshing a table adds significant overhead.

NEW QUESTION # 59
Your organization is migrating a large on-premise data warehouse (100 TB) to Snowflake. The existing data warehouse contains complex data transformations implemented in stored procedures. During the migration, you need to minimize downtime and accurately estimate the data volume transferred to Snowflake. You decide to use a hybrid approach with both batch and incremental loading. Which of the following strategies would be MOST appropriate?

A. Use Snowflake's Data Exchange to directly replicate the on-premise data warehouse to Snowflake. Rely on Data Exchange's built-in monitoring to track the data volume transferred.
B. Implement a custom ETL pipeline using Apache Spark to extract data from the on-premise data warehouse, perform transformations, and load the data into Snowflake using the Snowflake JDBC driver. Continuously run Spark jobs to keep the data synchronized.
C. Create external tables pointing to the on-premise data warehouse. Run queries directly against the external tables to transform and load the data into Snowflake. Once all data is loaded, drop the external tables. Use 'TABLE_SIZE function for sizing calculation.
D. Utilize Snowflake Connectors (e.g., Kafka connector) to stream data directly from the on-premise data warehouse to Snowflake. Implement a data replication tool to handle the initial data load and ongoing synchronization.
E. Perform a full batch load of the historical data using Snowpipe from a cloud storage location. After the initial load, implement a change data capture (CDC) solution using a third-party tool (e.g., Debezium, Qlik Replicate) to incrementally load changes into Snowflake.

Answer: E

Explanation:
Option B is the most appropriate strategy. It allows for a faster initial load using Snowpipe, leveraging the scalability of cloud storage. Implementing a CDC solution minimizes downtime and ensures data synchronization after the initial load. The combined approach addresses both the large data volume and the need for continuous updates.

NEW QUESTION # 60
How do constraints, such as primary keys, impact table joins between parent/child tables in Snowflake?

A. Constraints limit table join operations to specific data types
B. Constraints restrict data access to authorized user roles
C. Primary keys allow only one-to-one table joins
D. Primary keys ensure data uniqueness for table relationships

Answer: D

Explanation:
Primary keys enforce data uniqueness, ensuring integrity and maintaining relationships between parent and child tables in Snowflake.

NEW QUESTION # 61
A data analyst is investigating a decline in the conversion rate on an e-commerce website. They have access to the following tables in Snowflake: 'sessions': 'session id', 'user id', 'start time', 'end_time' 'page views': 'session id', 'page_urr, 'view time' 'transactions': 'session_id', 'transaction id', 'amount', 'transaction_time' Which of the following approaches, using Snowflake features, would be MOST effective for identifying potential bottlenecks or drop-off points in the user journey?

A. Use Snowflake's 'SHOW TABLES' command to identify the most frequently updated tables. Then, create a dashboard on these tables to monitor the rate of changes.
B. Use recursive SQL common table expressions (CTEs) to reconstruct the entire user journey for each session, from the entry page to either a successful transaction or session termination. Analyze path completion rates at each step to identify the pages where users are most likely to abandon the session.
C. Perform a cohort analysis by grouping users based on their sign-up date or initial website visit date. Track their conversion rates over time. Use a data visualization tool to see if any group exhibits an unusual drop in the conversion rate.
D. Implement a data lineage tool to trace the flow of data from raw sources to the transaction table. This will reveal any data quality issues that may be impacting conversion rates.
E. Create a funnel analysis by defining key stages in the user journey (e.g., homepage visit, product page view, add to cart, checkout, purchase). Use window functions to track users as they progress through the funnel, calculating conversion rates between each stage. Visualize the funnel using a BI tool for easy identification of drop-off points.

Answer: C,E

Explanation:
Options B and C provide useful diagnostic insights. B offers direct information about conversion at each stage of the funnel. Option C enables discovery of unusual drops over time. Option A might be a difficult, resource intensive solution for complex user journeys. Option D is a poor approach as it identifies the rate of change in tables instead of the main objective - bottlenecks or drop-off points. Option E, while helpful for data governance, doesn't directly pinpoint user journey issues.

NEW QUESTION # 62
You are working with a table 'ORDERS' containing order data, and a table 'CUSTOMER SEGMENTS containing customer segment information. The 'ORDERS' table has columns 'ORDER ID', 'CUSTOMER ID, and 'ORDER AMOUNT'. The 'CUSTOMER SEGMENTS' table has columns 'CUSTOMER ID', 'SEGMENT ID', and 'SEGMENT NAME'. You need to create a query that enriches the 'ORDERS' table with the customer segment information. However, a customer can belong to multiple segments. You want to include all segments a customer belongs to in the enriched data, resulting in potentially multiple rows per order if the customer is in multiple segments. The output should include 'ORDER ID, 'ORDER AMOUNT, 'SEGMENT ID', and 'SEGMENT NAMES. Which SQL statement would correctly enrich the ORDERS table without losing any order information, even if customers belong to multiple segments?

Answer: B

Explanation:
An 'INNER JOIN' is the correct choice here. It will return all matching rows between the 'ORDERS' and tables based on the 'CUSTOMER ID. If a customer belongs to multiple segments, each segment will be returned in a separate row associated with the order. "LEFT JOIN' includes rows from the left table even without a match in the right table which is not needed. 'GROUP BY' in the INNER JOIN' will combine multiple segments with 'ORDER_IDS into one row, which is not needed as all segment names are expected. The subquery approach would only return one segment name per order. 'RIGHT JOIN' include all customer segments on the right which is also not needed.

NEW QUESTION # 63
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