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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Data Presentation and Data Visualization28%-29%- Create dashboards
  • 1. Snowsight dashboards
  • 2. Present analytical results
- Integrate with BI tools
  • 1. Other partner visualization tools
  • 2. Power BI integration
  • 3. Tableau integration
Data Transformation and Data Modeling22%-30%- Transform data using SQL
  • 1. Common Table Expressions (CTEs)
  • 2. PIVOT/UNPIVOT operations
  • 3. Window functions
  • 4. QUALIFY clauses
- Design data models
  • 1. Data vault models
  • 2. Snowflake schema design
  • 3. Star schema design
Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Snowflake-specific analytical features
  • 2. Time-series analysis
  • 3. Aggregate functions
Data Ingestion and Data Preparation15%-20%- Use a collection system to retrieve data
  • 1. Retrieve data from unstructured sources
  • 2. Synthetic Data Generation
  • 3. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 4. Retrieve data from structured sources (CSV)
- Use best practice considerations relating to data integrity structures
  • 1. Perform table joins between parent/child tables
  • 2. Define primary keys for tables
  • 3. Implement constraints
- Implement data processing solutions
  • 1. Automate and implement data pipelines (scheduling)
  • 2. Respond to processing failures
  • 3. Cleanse, conform, and enrich data
  • 4. Use logging and monitoring solutions (auditing, data lineage)
- Perform data discovery to identify what is needed from available datasets
  • 1. Identify elements required for business goals using BI reports or SQL analysis
  • 2. Determine the level of data granularity required
  • 3. Query tables to assess data elements and statistics maintained by Snowflake
  • 4. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 5. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
- 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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q55-Q60):

NEW QUESTION # 55
How does leveraging window functions in Snowflake differ from using table functions for data manipulation?

Answer: D

Explanation:
Window functions process data within specified partitions or frames, while table functions generate tables as their output, differing in their scope and operation.


NEW QUESTION # 56
You are working with clickstream data in Snowflake, stored in a table named This table has the following columns: (INTEGER), 'user_id' (INTEGER), (TIMESTAMP NTZ), 'event_type' (VARCHAR, e.g., 'page_view', 'button_click'), and 'session_id' (VARCHAR). A session is defined as a series of events by a single user with no more than 30 minutes between consecutive events. You need to determine the duration of each session in seconds. Which of the following SQL queries, when executed in Snowflake, will correctly calculate the duration of each session?

Answer: C,E

Explanation:
Options B and E are correct. Option B directly calculates the duration using 'DATEDIFF between the minimum and maximum timestamp within each session, grouped by Option E first calculates the previous event timestamp for each event within a session and calculates 'session_start' and 'session_end' correctly. Option A will result in errors since arithmetic operators are not allowed on TIMESTAMP_NTZ values. Option C does not aggregate the data by before subtracting start/end values. Option D is overly complex and also uses LAST VALUE which could result in errors since arithmetic operators are not allowed on TIMESTAMP NTZ values.


NEW QUESTION # 57
Identifying demographics and relationships in a dataset is crucial for:

Answer: C


NEW QUESTION # 58
What challenges might arise when handling JSON data in Snowflake using built-in functions for traversing, flattening, and nesting? (Select all that apply)

Answer: C,D

Explanation:
Challenges include handling nested structures and complexities in parsing and querying nested JSON objects, which might arise when manipulating JSON data in Snowflake.


NEW QUESTION # 59
You are tasked with enriching a 'SALES DATA' table in Snowflake with geographic information based on IP addresses. You have access to an external function 'GEO LOOKUP(ip_address)' that returns a JSON object containing geographical details (city, region, country) for a given IP address. The 'SALES DATA' table contains 'SALE D', 'CUSTOMER D', ADDRESS', and 'SALE AMOUNT columns. You need to enrich the table with city and country information derived from the IP address. Which of the following statements will correctly add 'CITY' and 'COUNTRY columns to a new table 'ENRICHED SALES DATA based on the external function 'GEO LOOKUP , correctly handling potential NULL values and ensuring data type consistency?

Answer: B

Explanation:
Option D is the most robust solution. It extracts the city and country values from the JSON object returned by the function using the operator. The cast ensures the data is stored as strings. Critically, it uses to handle cases where the 'GEO_LOOKUP' function might return NULL (e.g., for invalid IP addresses), preventing errors and providing a default value ('Unknown'). Option A does not handle NULL, Option B's 'GET_PATH' is not a standard Snowflake function for JSON parsing, Options C parses the GEO LOOKUP output to json format if its not which can result in the 'CITY' and 'COUNTRY becoming 'NULL'. The option E 'PARSE_JSON' would throw errors on invalid json strings in the ip address.


NEW QUESTION # 60
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