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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Presentation and Data Visualization28%-29%- Integrate with BI tools
  • 1. Power BI integration
  • 2. Tableau integration
  • 3. Other partner visualization tools
- Create dashboards
  • 1. Snowsight dashboards
  • 2. Present analytical results
Topic 2: Data Ingestion and Data Preparation15%-20%- 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
- Use a collection system to retrieve data
  • 1. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 2. Retrieve data from unstructured sources
  • 3. Synthetic Data Generation
  • 4. Retrieve data from structured sources (CSV)
- Implement data processing solutions
  • 1. Automate and implement data pipelines (scheduling)
  • 2. Cleanse, conform, and enrich data
  • 3. Use logging and monitoring solutions (auditing, data lineage)
  • 4. Respond to processing failures
- 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
- 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. Determine the level of data granularity required
  • 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. Query tables to assess data elements and statistics maintained by Snowflake
Topic 3: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Time-series analysis
  • 2. Snowflake-specific analytical features
  • 3. Aggregate functions
Topic 4: Data Transformation and Data Modeling22%-30%- Transform data using SQL
  • 1. Common Table Expressions (CTEs)
  • 2. QUALIFY clauses
  • 3. Window functions
  • 4. PIVOT/UNPIVOT operations
- Design data models
  • 1. Star schema design
  • 2. Data vault models
  • 3. Snowflake schema design

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
How can incorporating visualizations in reports and dashboards facilitate better data comprehension and analysis for business use scenarios?

Answer: C

Explanation:
Visualizations enhance data comprehension, aiding effective analysis in business use scenarios.


NEW QUESTION # 24
When working with semi-structured data in Snowflake, how do built-in functions for traversing, flattening, and nesting aid in data manipulation?

Answer: C

Explanation:
Built-in functions for semi-structured data in Snowflake simplify handling complex and nested structures, making data manipulation more manageable and enhancing flexibility in data transformation.


NEW QUESTION # 25
Which of the following is a key step in data preparation?

Answer: C


NEW QUESTION # 26
You have a table named USER ACTIVITY containing user interaction data'. The 'TIMESTAMP NTT column stores timestamps without time zone information, while the 'USER ID column stores IDs as VARCHAR. You need to identify users who have been active between a specific UTC time range, converting the 'TIMESTAMP NTT column to UTC. Furthermore, you want to categorize users based on the number of activities recorded. Which of the following SQL queries best achieves this, efficiently utilizing Snowflake's casting and data transformation capabilities?

Answer: A

Explanation:
Option D is best because: 1. It correctly addresses the time zone conversion. 'TIMESTAMP NTZ stores timestamps without time zone. Since the question asks for activities between a specific UTC time range, the 'TIMESTAMP_NTZ column needs to be converted to UTC for accurate comparison. 2. It correctly uses 'UTC', TIMESTAMP_NTZ)' to convert from current timezone to UTC, thus all the activities between given date range, that means all users' activity in current_timezone. It also considers Time Zone information is critical for date-related analysis. 3. It accurately categorizes users into 'Frequent' or 'Infrequent' based on the number of activities recorded through grouping by 'USER_ID. Option A converts from UTC to some other timezone, which means all dates and comparison will be in that TZ. Option B converts data that has to be in valid TIMESTAMP format which is redundant. Option C won't work because it does not convert data into TIMEZONE, so timezone conversion has to be done. Option E is incorrect because it is converting from UTC to the current timezone when we need to compare against a UTC range, so we should convert from current timezone to UTC.


NEW QUESTION # 27
A Data Analyst needs to write a query that will return all projects from a project table and all employees from an employee table. What type of join should be used in this query?

Answer: D

Explanation:
In Data Transformation and Data Modeling, selecting the correct join type determines how the query handles unmatched records from the participating tables. The requirement here is to return all records from both the projects table and the employee table.
A FULL OUTER JOIN (often shortened to FULL JOIN) is designed specifically for this purpose. It combines the results of both a LEFT OUTER JOIN and a RIGHT OUTER JOIN. It returns:
* Rows where there is a match between the project and the employee.
* Rows from the projects table that have no matching employees (padded with NULL in the employee columns).
* Rows from the employees table that are not assigned to any projects (padded with NULL in the project columns).
Evaluating the Options:
* Option A (INNER JOIN) only returns rows where there is a match in both tables. Any projects without employees or employees without projects would be excluded.
* Option C (LEFT OUTER JOIN) would return all projects, but would exclude employees who are not assigned to a project.
* Option D (CROSS JOIN) creates a Cartesian product, matching every single employee with every single project regardless of actual relationships. This would create a massive, redundant dataset and is not what is requested.
* Option B is the 100% correct answer. It ensures total data visibility from both entities, which is often required in data quality audits or comprehensive resource allocation reporting where the analyst needs to see "unlinked" data on both sides of the relationship.


NEW QUESTION # 28
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