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Snowflake DAA-C01 Exam Overview:

Certification Vendor:Snowflake
Exam Name:SnowPro Advanced: Data Analyst Certification Exam
Exam Number:DAA-C01
Exam Duration:115 minutes
Certificate Validity Period:2 years
Real Exam Qty:65
Passing Score:750 out of 1000 (scaled score)
Related Certifications:SnowPro Core Certification
Available Languages:English
Exam Price:$375 USD
Exam Format:Multiple Select, True/False, Multiple Choice
Sample Questions:Snowflake DAA-C01 Sample Questions
Exam Way:Online via Pearson VUE (proctored)
Pre Condition:Active SnowPro Core Certified credential required. 1 or more years of hands-on practical experience with Snowflake as a Data Analyst in a production environment recommended. Fluency with advanced SQL and knowledge of an additional computer language preferred.
Official Syllabus URL:https://learn.snowflake.com/en/certifications/snowpro-advanced-dataanalyst

Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Data Presentation and Data Visualization28%-29%- Integrate with BI tools
  • 1. Other partner visualization tools
  • 2. Tableau integration
  • 3. Power BI integration
- Create dashboards
  • 1. Snowsight dashboards
  • 2. Present analytical results
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. Data vault models
  • 2. Snowflake schema design
  • 3. Star schema design
Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Aggregate functions
  • 2. Snowflake-specific analytical features
  • 3. Time-series analysis
Data Ingestion and Data Preparation15%-20%- Perform data discovery to identify what is needed from available datasets
  • 1. Query tables to assess data elements and statistics maintained by Snowflake
  • 2. Identify elements required for business goals using BI reports or SQL analysis
  • 3. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 4. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 5. Determine the level of data granularity required
- 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
- 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. Create tables and views
  • 2. Find external data sets that correlate with available data
  • 3. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings)
- Use a collection system to retrieve data
  • 1. Synthetic Data Generation
  • 2. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 3. Retrieve data from structured sources (CSV)
  • 4. Retrieve data from unstructured sources
- Implement data processing solutions
  • 1. Cleanse, conform, and enrich data
  • 2. Automate and implement data pipelines (scheduling)
  • 3. Use logging and monitoring solutions (auditing, data lineage)
  • 4. Respond to processing failures

Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

Question #1

This command was executed:
SQL
SELECT seq4(), uniform(1, 10, RANDOM(12))
FROM TABLE(GENERATOR(TIMELIMIT => NULL))
ORDER BY 1;
How many rows will be generated?

  • A. An infinite number
  • B. 10
  • C. 0
  • D. 12
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #2

There are two similarly-structured and sized tables, Table_a and Table_b, in a schema with data populated in both tables. A Data Analyst is running queries as part of a preliminary analysis of the data to check the MAX value of a numeric column named num which is present in both the tables:
* Query 1: SELECT MAX(num) FROM Table_a;
* Query 2: SELECT MAX(num) FROM Table_b;
After running the queries, the Analyst observed that Query 2 ran significantly slower than Query 1. Why is this occurring?

  • A. A multi-cluster warehouse was used to run Query 1.
  • B. The USE_CACHED_RESULT was set to FALSE before running Query 2.
  • C. Table_b has a row-access policy defined.
  • D. Table_b has more rows than Table_a.
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Correct Answer: C  🗳️

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Question #3

Which Snowflake SQL would a Data Analyst use in a trained Cortex model named forecast_model to retrieve the components that contribute to the predictions?

  • A. forecast_model!SHOW_TRAINING_LOGS()
  • B. forecast_model!EXPLAIN_FEATURE_IMPORTANCE()
  • C. forecast_model!SHOW_EVALUATION_METRICS()
  • D. forecast_model!FORECAST()
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #4

What will the following query return?
SELECT * FROM testtable SAMPLE BLOCK (0.012) REPEATABLE (99992);

  • A. A sample containing 99992 records of a table in which each block of rows has a 0.012% probability of being included in the sample.
  • B. A sample of a table in which each block of rows has a 0.012% probability of being included in the sample, with the seed set to 99992.
  • C. A sample of a table in which each block of rows has a 1.2% probability of being included in the sample where repeated elements are allowed.
  • D. A sample of a table in which each block of rows has a 1.2% probability of being included in the sample, with the seed set to 99992.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #5

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?

  • A. Cross join
  • B. Inner join
  • C. Left outer join
  • D. Full outer join
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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