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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
You have a Snowflake table named 'CUSTOMER DATA' with a VARIANT column called 'PROFILE. This 'PROFILE column contains nested JSON objects with customer attributes. You need to extract the customer's first name from the 'PROFILE and handle cases where the 'firstName' field might be missing (NULL). Which of the following methods is the most efficient and concise way to achieve this?

Answer: E

Explanation:
Option E is the most efficient and concise. 'PROFILE:firstName' directly accesses the field, casts it to a string and 'IFNI-ILL' handles the potential NULL values, replacing them with 'Unknown'. Options A and C might return NULL if the field is missing. Option B is less efficient than direct path access. While option D works, casting to string is essential to ensure the correct datatype and to avoid any unexpected behaviors, making option E the ideal answer.

NEW QUESTION # 66
Your company uses Snowflake to store sales data'. A dashboard reporting weekly sales trends is performing poorly. The underlying table, 'SALES DATA, contains billions of rows with columns like 'SALE DATE, 'PRODUCT ID', 'CUSTOMER D', and 'SALE AMOUNT. The dashboard queries use 'SALE DATE for filtering and grouping. The query execution plan shows full table scans. You need to optimize the dashboard's performance with minimal impact on data loading processes. Which of the following strategies should you implement FIRST to improve query performance?

A. Create a search optimization on 'SALES DATA' table for 'SALE DATE.
B. Increase the warehouse size to X-LARGE.
C. Create a materialized view that aggregates sales data weekly by 'PRODUCT_ID and 'CUSTOMER ID
D. Create a clustered table on 'SALE DATE' in the 'SALES DATA' table.
E. Partition the 'SALES_DATX table by 'SALE_DAT

Answer: D

Explanation:
Clustering the table on 'SALE DATE' is the most effective initial strategy. It physically organizes the data based on 'SALE DATE, which the dashboard queries use for filtering, thus reducing the amount of data scanned during query execution. Materialized views require ongoing maintenance and may not be the most efficient starting point. Increasing the warehouse size will increase the resource, but doesn't solve the underlying problem of full table scans, and search optimization is less efficient than clustering for date-based filtering. Snowflake does not support user-defined partitioning. Hence option A is the most appropriate choice.

NEW QUESTION # 67
You are a data analyst for an e-commerce company. You need to create a dashboard visualizing sales performance. The dashboard requires two key features: 1) users should be able to filter the data by product category dynamically using a dropdown list. 2) The dashboard should efficiently handle large datasets (millions of rows) without performance degradation. Which Snowflake features and techniques would you use to achieve these requirements most effectively?

A. Create a stream on the sales data and use a task to continuously update a summary table with aggregated data for each product category. Use the summary table as the data source for the dashboard.
B. Utilize Snowflake's caching mechanisms and create materialized views to pre-compute aggregated data for the dashboard, along with using dashboard-level filtering widgets connected to the product category column.
C. Use Snowflake's dynamic data masking to hide sensitive sales data based on user roles, and create a view with pre-aggregated data for each product category to improve dashboard performance.
D. Implement user-defined functions (IJDFs) in Python to perform custom filtering logic, and store the results in a temporary table for the dashboard to consume.
E. Extract all data into a pandas DataFrame and create a Dash application for the front end, as Snowflake struggles to handle real-time filtering effectively.

Answer: B

Explanation:
Using materialized views pre-computes aggregates and leverages Snowflake's caching. Dashboard-level filtering (option C) allows for dynamic filtering without querying the base data directly, providing the best performance and scalability. Options A and D are less ideal for real-time filtering; B is an anti-pattern in Snowflake, and E moves data out of Snowflake unnecessarily.

NEW QUESTION # 68
When maintaining reports and dashboards, why is it essential to build automated and repeatable tasks?

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

Answer: C

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

NEW QUESTION # 69
Which aspects are crucial for making predictions based on data for forecasting purposes? (Select all that apply)

A. Relying solely on historical data without considering external factors
B. Incorporating statistical methods for accurate predictions
C. Using only basic arithmetic functions for forecasting
D. Considering trends and anomalies in historical data

Answer: B,D

Explanation:
Incorporating statistical methods and considering trends/anomalies are crucial for accurate predictions in forecasting.

NEW QUESTION # 70
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