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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Time-series analysis 2. Aggregate functions 3. Snowflake-specific analytical features
Topic 2: 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. Window functions 3. Common Table Expressions (CTEs) 4. PIVOT/UNPIVOT operations
Topic 3: Data Ingestion and Data Preparation	15%-20%	- 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) - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) 2. Find external data sets that correlate with available data 3. Create tables and views - Use best practice considerations relating to data integrity structures 1. Define primary keys for tables 2. Implement constraints 3. Perform table joins between parent/child tables - Use a collection system to retrieve data 1. Retrieve data from structured sources (CSV) 2. Synthetic Data Generation 3. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 4. Retrieve data from unstructured sources - Prepare data and load into Snowflake 1. Load files using Snowsight 2. Load data from external/internal stages into a table - 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)
Topic 4: Data Presentation and Data Visualization	28%-29%	- Create dashboards 1. Snowsight dashboards 2. Present analytical results - Integrate with BI tools 1. Other partner visualization tools 2. Tableau integration 3. Power BI integration

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q64-Q69):

NEW QUESTION # 64
You are responsible for maintaining a dashboard that visualizes sales data'. The dashboard relies on a Snowflake view named 'SALES SUMMARY. Users have reported slow refresh times, especially when filtering by date. The 'SALES SUMMARY view is defined as follows:

Which of the following actions could significantly improve the dashboard's performance when filtering by date, assuming 'SALES_TRANSACTIONS' table is large?

A. Create a materialized view on top of
B. Create a search optimization policy on the DATE column of the SALES_TRANSACTIONS table.
C. Partition the 'SALES_TRANSACTIONS' table by the DATE column.
D. create a clustering key on the DATE column in the 'SALES_TRANSACTIONS' table.
E. Add a 'ORDER BY DATE clause to the 'SALES SUMMARY' view definition.

Answer: D

Explanation:
Clustering the 'SALES_TRANSACTIONS table by the DATE column is the most effective solution. Snowflake uses micro- partitioning, and clustering helps to logically group similar data together within micro-partitions. When filtering by DATE, Snowflake can then efficiently prune micro-partitions that do not contain relevant data, significantly reducing the amount of data scanned. Materialized views (A) can help but need to be maintained and add complexity. Search optimization on DATE can help but it's not the intended use case, as DATE is a range based column (B). Ordering the results (C) doesn't affect query performance significantly. Partitioning is not directly supported in Snowflake. Clustering is the correct approach.

NEW QUESTION # 65
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 secure view: 'CREATE OR REPLACE SECURE VIEW DAILY SALES SUMMARY AS SELECT TRANSACTION_DATE, FROM SALES TRANSACTIONS GROUP BY TRANSACTION DATE;'
C. Create a standard view: 'CREATE OR REPLACE VIEW DAILY SALES SUMMARY AS SELECT TRANSACTION_DATE, FROM SALES TRANSACTIONS GROUP BY TRANSACTION DATE;'
D. Create a materialized view: 'CREATE OR REPLACE MATERIALIZED VIEW DAILY SALES SUMMARY AS SELECT TRANSACTION DATE, FROM SALES_TRANSACTIONS GROUP BY TRANSACTION_DATE;'
E. 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;'

Answer: D

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 # 66
Consider a scenario where you have a table 'CUSTOMER ORDERS' with columns 'CUSTOMER ID', 'ORDER DATE' , 'ORDER TOTAL' , and 'PRODUCT CATEGORY'. You want to create a materialized view that calculates the sum of order totals for each customer, grouped by product category, and refreshed automatically on a daily basis. However, you are also concerned about minimizing the cost of materialized view maintenance. Which of the following strategies would be MOST cost-effective while still providing reasonably up-to-date data?

A. Create a standard view with the same aggregation logic and optimize the underlying table using clustering.
B. Create a materialized view and set the refresh schedule to 'ON CHANGE' with a clustering key on
C. Create a materialized view and schedule a daily refresh at a time of low system activity.
D. Create a materialized view and set the refresh schedule to 'ON CHANGE'.
E. Create a materialized view without specifying a refresh schedule, and manually refresh it whenever the report is run.

Answer: C

Explanation:
Scheduling a daily refresh allows the materialized view to be updated regularly without incurring the overhead of 'ON CHANGE' refreshes, which can be very costly if the underlying table is frequently updated. Manual refreshes would not provide up-to-date data automatically. 'ON CHANGE' without further optimization can be extremely expensive. A standard view wouldn't provide the performance benefits of a materialized view. Clustering on CUSTOMER ID might improve performance but would not address the refresh cost directly.

NEW QUESTION # 67
You have two Snowflake tables: 'transactions' (containing transaction details with columns 'transaction id', 'customer id', 'amount' , 'transaction_date') and 'customer_demographicS (containing customer demographic information with columns 'customer_id', 'age' , gender' , 'location'). You need to enrich the 'transactions' table with customer demographics to analyze transaction patterns based on customer segments. What is the most efficient and scalable way to achieve this data enrichment in Snowflake, considering the 'transactions' table contains billions of rows?

A. Using Snowflake Streams and Tasks to incrementally enrich the 'transactions' table as new data arrives.
B. Using a series of correlated subqueries to fetch the demographic information for each transaction record.
C. Using Snowflake's materialized views to pre-compute the enriched data and refresh it periodically.
D. Using a standard INNER JOIN between the 'transactions' and 'customer_demographics' tables in a CREATE TABLE AS SELECT (CTAS) statement.
E. Export the tables to a data lake, perform the join using a Spark cluster, and then import the enriched data back into Snowflake.

Answer: A,C,D

Explanation:
Options A, C, and D are efficient approaches. Option A (CTAS with INNER JOIN) is often the simplest and most performant for a one- time enrichment, leveraging Snowflake's query optimization. Option C (Materialized Views) provides pre-computed enriched data, ideal for frequent queries on the enriched dataset. Option D (Streams and Tasks) enables incremental enrichment, suitable for real-time or near real-time data updates. Option B (correlated subqueries) is generally inefficient and should be avoided with large datasets. Option E introduces unnecessary complexity by exporting data to a data lake.

NEW QUESTION # 68
A Data Analyst creates a dashboard showing the total credit consumption for each virtual warehouse as follows:

Why is the query failing?

A. DB1 must be authorized to have SELECT access to ACCOUNT_USAGE.
B. The query must be executed by a user with the ACCOUNTADMIN role.
C. INFORMATION_SCHEMA should be used instead of ACCOUNT_USAGE.
D. The current database context must be changed to SNOWFLAKE.

Answer: D

Explanation:
This error occurs due to a misunderstanding of how Snowflake resolves object names and the location of the Shared Snowflake Database. In Snowflake, the ACCOUNT_USAGE schema is a collection of views that provide comprehensive historical data about account activities, including credit consumption, storage, and query history. These views are stored within the system-defined database named SNOWFLAKE.
When the Data Analyst executes the query shown in the image, they are operating within the database context of DB1 and the schema context of PUBLIC. Because the query references ACCOUNT_USAGE.
WAREHOUSE_METERING_HISTORY without a fully qualified name, the Snowflake compiler attempts to resolve the object starting from the current database context. Consequently, it looks for a schema named ACCOUNT_USAGE inside DB1. As the error message correctly indicates, Schema 'DB1.
ACCOUNT_USAGE' does not exist.
To fix this, the Analyst has two options:
* Fully Qualify the Object Name: Modify the query to reference the full path: FROM SNOWFLAKE.
ACCOUNT_USAGE.WAREHOUSE_METERING_HISTORY. This is the most common best practice as it allows the query to run regardless of the user's current session context.
* Change the Context: Use the command USE DATABASE SNOWFLAKE; before running the query.
This changes the session's database context so that ACCOUNT_USAGE can be found.
It is a common misconception (represented in Option A) that only an ACCOUNTADMIN can access these views. While access is restricted by default, the ACCOUNTADMIN can grant IMPORTED PRIVILEGES on the SNOWFLAKE database to other roles, such as a Data Analyst role. However, the specific error shown is a namespace resolution error, not a permission-denied error (which would typically say "Insufficient privileges"). Therefore, changing the context or fully qualifying the name is the direct solution to this specific failure.

NEW QUESTION # 69
......

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