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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
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. Warehouse sizing and auto-suspend/auto-resume 2. Clustering and pruning techniques - Modeling approaches in Snowflake 1. Star and snowflake schemas 2. Data normalization vs denormalization
Snowflake Architecture and Data Platform Fundamentals	- Data platform fundamentals 1. Data lifecycle in Snowflake 2. Separation of storage and compute - Snowflake architecture concepts 1. Virtual warehouses and scaling 2. Cloud services layer, compute layer, storage layer
Data Transformation and Analysis	- Analytical workloads 1. Materialized views and caching 2. Query optimization for analytics - SQL-based transformations 1. Semi-structured data (VARIANT, JSON, XML) 2. Joins, aggregations, window functions
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 (Q66-Q71):

NEW QUESTION # 66
You are tasked with creating a Snowsight dashboard to monitor the daily sales performance of an e-commerce platform. The sales data is stored in a table named 'SALES_DATR with columns 'SALE_DATE' (DATE), 'PRODUCT_ID' ONT), 'SALES AMOUNT' (FLOAT), and 'CUSTOMER REGION' (VARCHAR). You need to display a trendline chart showing the total sales amount for each day over the last 30 days, and also a table showing the top 5 regions by total sales amount. Which of the following steps are most efficient to achieve this in Snowsight? (Select TWO)

A. Use two separate worksheets in Snowsight. In the first worksheet, create the time series chart. In the second worksheet, create the region-based sales table. Then, embed one worksheet inside the other to create a combined dashboard.
B. Use the 'Notebook' feature in Snowsight to first perform the aggregations required to obtain the data for the visualizations, and then create visualization tiles by referencing the calculated dataframes. No direct SQL is used in the tiles.
C. Create two separate tiles in the Snowsight dashboard. One tile should use a time series chart visualizing 'SELECT SALE_DATE, SUM(SALES_AMOUNT) FROM SALES_DATA WHERE SALE_DATE DATEADD(day, -30, GROUP BY SALE_DATE ORDER BY SALE_DATE;' and the other tile should use a table visualizing 'SELECT CUSTOMER REGION, SUM(SALES AMOUNT) FROM SALES DATA GROUP BY CUSTOMER REGION ORDER BY DESC LIMIT 5;'
D. Create a single tile with two tabs. The first tab displays the trendline chart using a time series and the second tab displays the region-based sales table. Both tabs use the same underlying SQL query but filter the results differently.
E. Create a single tile using a combination chart in Snowsight. Use 'SALE DATE as the x-axis and 'SUM(SALES AMOUNT)' as the y-axis, specifying a trendline. Then, add a second series to display region-based sales in a separate panel below.

Answer: B,C

Explanation:
Option A is correct because it directly and efficiently creates the desired visualizations in Snowsight using separate tiles. Option C is also correct. Using Snowsight's Notebook feature allows for pre-aggregation, which can be useful in complex scenarios where directly visualizing from the raw data would impact dashboard performance. Options B, D, and E are either not directly supported or less efficient methods within the current Snowsight dashboarding capabilities.

NEW QUESTION # 67
When maintaining reports and dashboards, why is it crucial to build automated and repeatable tasks?

A. They ensure inconsistency in reports and dashboards.
B. Automated tasks increase the complexity of dashboard management.
C. Repeatable tasks hinder data updates in dashboards.
D. Automated tasks reduce manual efforts, ensuring consistency.

Answer: D

Explanation:
Automated tasks reduce manual efforts, ensuring consistency in reports and dashboards.

NEW QUESTION # 68
A data analyst is experiencing slow query performance when joining two large tables, 'SALES' (1 billion rows) and 'CUSTOMERS' (10 million rows), on 'CUSTOMER ID. The 'SALES' table is frequently updated. The following query is used: SELECT s. , c. FROM SALES s JOIN CUSTOMERS c ON s.CUSTOMER_lD = c.CUSTOMER_lD WHERE s.SALE_DATE DATEADD(day, -30, CURRENT DATE()); Which of the following strategies would MOST effectively improve the query performance, assuming you have appropriate privileges to alter objects and cost is a concern?

A. Create a search optimization on 'SALES.CUSTOMER and 'CUSTOMERS.CUSTOMER ID.
B. Create a cluster key on 'SALES' table on 'CUSTOMER_ID' and re-cluster the table after data loading.
C. Create a materialized view joining the two tables and filter on 'SALE_DATE. Refresh the materialized view daily.
D. Create a standard index on 'SALES.CUSTOMER ID and 'CUSTOMERS.CUSTOMER ID.
E. Create a 'SALES LAST 30 DAYS' table containing only the last 30 days of sales, and join that table to the 'CUSTOMERS table. Refresh the 'SALES LAST 30 DAYS daily.

Answer: E

Explanation:
Creating 'SALES LAST 30 DAYS' table and joining is the most effective strategy because it reduces the size of the SALES table significantly before the join operation. Materialized views (A) are generally effective, but the high update frequency of the SALES table could lead to significant overhead in materialized view maintenance. Standard indexes (B) are not supported in Snowflake. Search optimization (C) may not be suitable as the could have high cardinality. Clustering (D) could improve performance, but the benefits might not outweigh the cost of reclustering after frequent data loading, and it does not limit the initial size of the join.

NEW QUESTION # 69
What distinguishes exploratory ad-hoc analyses from routine analysis?

A. Ad-hoc analyses focus solely on anomalies and established trends.
B. Ad-hoc analyses rely heavily on predefined queries.
C. They involve querying known patterns without further exploration.
D. Ad-hoc analyses explore patterns and anomalies beyond established routines.

Answer: D

Explanation:
Ad-hoc analyses explore patterns and anomalies beyond established routines.

NEW QUESTION # 70
How do secure views contribute to data analysis practices in terms of access control and data security?

A. They prevent the creation of materialized views.
B. Secure views enhance data security while allowing selective data access.
C. They restrict data access but don't impact data security.
D. Secure views limit data accessibility for improved security.

Answer: B

Explanation:
Secure views enhance data security while allowing selective data access.

NEW QUESTION # 71
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