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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Ingestion and Data Preparation	15%-20%	- Use best practice considerations relating to data integrity structures 1. Implement constraints 2. Perform table joins between parent/child tables 3. Define primary keys for tables - Implement data processing solutions 1. Cleanse, conform, and enrich data 2. Respond to processing failures 3. Use logging and monitoring solutions (auditing, data lineage) 4. Automate and implement data pipelines (scheduling) - Perform data discovery to identify what is needed from available datasets 1. Query tables to assess data elements and statistics maintained by Snowflake 2. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 3. Identify elements required for business goals using BI reports or SQL analysis 4. Evaluate required transformations (table joins, set operations, ASOF JOINS) 5. Determine the level of data granularity required - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Find external data sets that correlate with available data 2. Create tables and views 3. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) - Use a collection system to retrieve data 1. Retrieve data from structured sources (CSV) 2. Synthetic Data Generation 3. Retrieve data from unstructured sources 4. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) - Prepare data and load into Snowflake 1. Load data from external/internal stages into a table 2. Load files using Snowsight
Topic 2: Data Presentation and Data Visualization	28%-29%	- Integrate with BI tools 1. Other partner visualization tools 2. Tableau integration 3. Power BI integration - Create dashboards 1. Present analytical results 2. Snowsight dashboards
Topic 3: Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Snowflake-specific analytical features 2. Aggregate functions 3. Time-series analysis
Topic 4: Data Transformation and Data Modeling	22%-30%	- Design data models 1. Data vault models 2. Star schema design 3. Snowflake schema design - Transform data using SQL 1. PIVOT/UNPIVOT operations 2. Common Table Expressions (CTEs) 3. QUALIFY clauses 4. Window functions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q18-Q23):

NEW QUESTION # 18
You're working with time series data in Snowflake, specifically website traffic data with timestamps and page views. You need to calculate the cumulative page views for each day. However, the data contains missing timestamps, and you want to fill those gaps with a default page view count of 0 before calculating the cumulative sum. Which of the following approaches, used in conjunction with a cumulative SUM aggregate function, is MOST efficient?

A. Use a LATERAL FLATTEN function to generate a list of dates, LEFT JOIN it with the website traffic data, and use ZEROIFNULL to fill missing page views with 0 before the cumulative SUM.
B. Use a recursive CTE (Common Table Expression) to generate the missing dates and then UNION ALL with the existing data, filling missing page views with 0 using NVL, prior to applying the cumulative SUM.
C. Use a stored procedure to iterate through the date range, inserting missing dates with a page view count of O before applying the cumulative SUM.
D. Use the 'GENERATE_SERIES' function (if available via UDF) to create a series of dates, LEFT JOIN with the website traffic data, and use COALESCE to fill missing page views with 0 before the cumulative SUM.
E. Create a separate table containing all dates in the range and LEFT JOIN it with the website traffic data, using COALESCE to fill missing page views with 0 before applying the cumulative SUM.

Answer: E

Explanation:
Creating a separate date table and using a LEFT JOIN with COALESCE is the most efficient approach. Stored procedures (A) are generally slower for large datasets. Recursive CTEs (C) can be resource-intensive. Using UDFs or Lateral Flatten (D & E) may also increase complexity and overhead compared to a simple JOIN. The key is to leverage Snowflake's ability to handle joins efficiently.

NEW QUESTION # 19
Your company is using Snowflake to store customer transaction data'. You want to enrich this data with demographic information from a Snowflake Marketplace data provider. The provider offers a secure data share with a view called 'CUSTOMER DEMOGRAPHICS. You need to join the customer transaction data in your 'TRANSACTIONS' table with the demographic data from the 'CUSTOMER DEMOGRAPHICS' view. Which of the following SQL queries is the MOST efficient and secure way to achieve this, assuming you have already created a database from the share?

A. Option C
B. Option E
C. Option A
D. Option B
E. Option D

Answer: D

Explanation:
Option B is the most efficient and secure because it explicitly uses an INNER JOIN, ensuring that only matching records between the TRANSACTION table and the Customer Demographics view are included. Using INNER JOIN improves performance compared to implicit joins (Option A). Options C and E uses LEFT and FULL OUTER joins which might result in unnecessary nulls and impacting the perfromance. Option D will not work because the schema name is missing.

NEW QUESTION # 20
What role does operationalizing data play in maintaining reports and dashboards for business requirements?

A. It limits the usability of reports by narrowing down access.
B. It restricts data updates, affecting dashboard accuracy.
C. Operationalizing data complicates dashboard management.
D. Operationalizing data ensures consistent and efficient usage.

Answer: D

Explanation:
Operationalizing data ensures consistent and efficient usage of reports and dashboards.

NEW QUESTION # 21
When optimizing query performance in Snowflake, what benefits does result caching provide?

A. Limits data access for specific user roles
B. Speeds up query execution by storing intermediate results
C. Improves schema changes management
D. Restricts query optimization

Answer: B

Explanation:
Result caching accelerates query execution by storing intermediate results, reducing processing time for repetitive or commonly accessed queries.

NEW QUESTION # 22
Which actions are typically part of responding to data import errors in Snowflake? (Select all that apply)

A. Resolving data inconsistencies
B. Resuming data import immediately
C. Analyzing error logs for resolution
D. Identifying error sources

Answer: A,C,D

Explanation:
Responding to data import errors involves identifying error sources, resolving inconsistencies, and analyzing logs for resolution.

NEW QUESTION # 23
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