Quiz 2026 Fantastic Snowflake DAA-C01 Valid Test Question

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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q36-Q41):

NEW QUESTION # 36
You have a Snowflake table named 'sensor_data' with a column 'reading' containing JSON data'. The JSON structure varies, but you want to extract a specific nested value, 'temperature', using a UDE The path to 'temperature' might be different depending on the 'sensor_type'. Some sensors have the temperature at '$.metrics.temperature' , others at '$.reading.temp_c'. The sensor type is stored in the 'sensor_type' column. You want to create a UDF named which takes the JSON 'reading' and the 'sensor_type' as input and extracts the temperature, returning NULL if the path does not exist in the JSON. How can you implement this using a JavaScript UDF and Snowflake's JSON parsing functions for optimal performance?

Answer: A

Explanation:
Option C is the MOST optimal. It uses 'VARIANT as the input data type for 'reading' , which avoids the unnecessary parsing of JSON using as Snowflake automatically parses JSON data into a VARIANT type. It also includes a 'try...catch' block to handle cases where the specified path does not exist within the JSON, returning 'NULL' as required. This prevents errors from halting the query. Using 'VARIANT directly and exception handling offers superior performance. Option A and D parse VARIANT as String, leading to parsing overhead. B misses the try catch block and is prone to failure when temp is not available for a given sensor. Option E is less efficient than option C due to using array notation.


NEW QUESTION # 37
How does leveraging Time Travel feature in Snowflake aid in query optimization and historical data analysis?

Answer: D

Explanation:
Time Travel feature allows querying historical data versions, facilitating historical data analysis and retrospective query optimizations based on past data states.


NEW QUESTION # 38
What functions should a Data Analyst use to run descriptive analytics on a data set? (Select TWO).

Answer: D,E

Explanation:
Descriptive analytics is the process of using historical data to understand "what happened." This typically involves summarizing large datasets into interpretable chunks using central tendency, dispersion, and frequency measures.
AVG (Average) is a cornerstone of descriptive statistics. It provides the arithmetic mean of a numeric column, allowing an analyst to understand the "typical" value within a dataset (e.g., Average Order Value).
APPROX_COUNT_DISTINCT is a descriptive tool used to understand the volume of unique entities within a dataset (e.g., "How many unique customers visited the site?"). Similar to HLL mentioned earlier, this function provides a fast summary of data volume and variety, which is a primary goal of the descriptive phase of analysis.
Evaluating the Options:
* Options A and B (REGR_INTERCEPT and REGR_SLOPE) are used for linear regression. These fall under Predictive Analytics, as they are used to model relationships and predict future outcomes, rather than just describing current data.
* Option C (ROW_NUMBER) is a window function used for data ranking and ordering, but it does not provide a descriptive summary of the dataset's characteristics.
* Options D and E are correct because they provide summary statistics (mean and cardinality) that define the "state" of the data, which is the definition of descriptive analytics.


NEW QUESTION # 39
A retail company is using Snowflake to store their daily sales data'. They want to predict sales for the next week, accounting for weekly seasonality and a promotional campaign running on specific days. The sales data is in a table called 'SALES DATA' with columns 'SALE DATE' (DATE) and 'SALES AMOUNT' (NUMBER). Which of the following SQL statements is the MOST efficient and accurate way to achieve this using Snowflake's forecasting features, assuming a confidence interval of 95%?

Answer: D

Explanation:
Option E is the most correct. Option A uses 'timeLimit' in generator which needs additional conditions to be satisfied. Option E utilizes 'TIMESTAMP_INPUT = TRUE to correctly interpret the 'SALE_DATE column. It accurately specifies the forecast horizon (7 days) and confidence interval (0.95), and generates the future dates for forecasting using 'TABLE(GENERATOR(rowcount => 7))' to ensure the generation of 7 days for forecasting. Option D has OVER (ORDER BY NULL)' which is generally less performant than seq4().


NEW QUESTION # 40
You are building a Snowsight dashboard that visualizes website traffic data'. The data includes a column 'visit_timestamp' (TIMESTAMP NTZ) and you need to display the number of unique visitors per hour for the last 24 hours. You also want to allow users to filter the data by country. You plan to use a chart to visualize the trend. Which of the following approaches are the MOST efficient and accurate for achieving this?

Answer: A,C

Explanation:
Option A is a good approach because 'DATE _ TRUNC is efficient for truncating timestamps, and applying the filter directly in the SQL query can optimize data retrieval. Option D is also very efficient, as it pre-aggregates the data, which improves dashboard performance. Option B is less efficient because the 'HOUR' function only returns the hour value without the date, making it harder to filter for last 24 hours. Also, dashboard-level filters can sometimes be less performant than SQL-level filters for large datasets. Option C introduces a calculated field, which is generally less efficient than performing the transformation directly in SQL. Option E is technically correct, it only applies to TEXT stored visit timestamp. Thus, pre-aggregation and 'DATE_TRUNC' are superior.


NEW QUESTION # 41
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