DAA-C01 Exam Sample Questions | DAA-C01 Valid Study Guide

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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Perform Descriptive and Diagnostic Analysis10–15%- Statistical summarization and trend analysis
- Anomaly detection and root cause analysis
- Exploratory and ad-hoc analysis
Topic 2: Perform Simple Data Transformations for Analysis15–20%- Views, materialized views, CTEs
- Handling NULLs and structuring datasets
- Data cleansing, standardization, type conversion
Topic 3: Use Built-in Functions and Create UDFs10–15%- Scalar, aggregate, table, system functions
- User-Defined Functions (UDFs)
Topic 4: Perform Predictive Analysis5–10%- Forecasting and predictive modeling
- Using Snowflake ML and built-in analytics
Topic 5: Build and Troubleshoot Advanced SQL Queries20–25%- Query optimization and troubleshooting
- Semi-structured data processing
- Complex joins, subqueries, window functions
Topic 6: Prepare and Present Data10–15%- Snowsight dashboards and sharing results
- Align outputs with business requirements
- Data visualization and reporting
Topic 7: Prepare and Load Data15–20%- File formats: CSV, JSON, Parquet, Avro
- External tables and data validation
- Data ingestion methods: COPY INTO, stages, Snowpipe

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
Which of the following statements are true regarding the use of user-defined functions (UDFs) in Snowflake to optimize query performance, especially when compared to equivalent SQL expressions? (Select all that apply)

Answer: A,B,D,E

Explanation:
UDFs can improve code readability but don't always translate to performance gains. Java and Python UDFs incur overhead because they run outside the Snowflake engine. SQL UDFs are generally faster. They are useful for code reuse. External functions and UDFs are not the same: UDFs reside inside snowflake.


NEW QUESTION # 17
You have created a forecasting model in Snowflake to predict customer churn based on historical data'. The model is named 'CHURN FORECAST MODEL'. After running the forecast, you need to evaluate the model's performance and understand its accuracy.
Which of the following methods or SQL commands can you use to accomplish this effectively?

Answer: A

Explanation:
Option E is the most comprehensive approach. The command 'SELECT FROM MODEL', EXPLAIN is the appropriate command to view model performance metrics. While snowflake can get summary stats. It's best to manually calculate error metrics by comparing the forecasted values with the actual historical data, that ensures a comprehensive evaluation. 'SHOW MODELS' command does not provide detailed accuracy metrics. 'SYSTEM$GET_MODEL' is not intended for model evaluation. 'DESCRIBE MODEL' provides details about model features but not the statistical performance. Snowflake supports built-in forecasting, so model calculation error is unnecessary.


NEW QUESTION # 18
How do user-defined functions (UDFs) contribute to data analysis in SQL?

Answer: D

Explanation:
UDFs allow customization and extension of SQL functionalities, enabling specific data operations beyond predefined limits.


NEW QUESTION # 19
You have a large dataset in Snowflake containing customer order information stored in a table named 'ORDERS' with columns 'ORDER_ID' ONT), 'CUSTOMER_ID' ONT), 'ORDER_DATE (DATE), 'TOTAL_AMOUNT' (FLOAT), and 'DISCOUNT_APPLIED' (BOOLEAN). You need to use Snowsight dashboards to analyze customer spending behavior and identify potential outliers. Which of the following visualizations, combined with appropriate SQL queries, would be MOST effective in identifying customers with unusually high or low order values? (Select TWO)

Answer: A,B

Explanation:
Options A and C are the most effective. A Box Plot (A) is ideal for identifying outliers in a distribution. By visualizing the distribution of total order amounts per customer, you can easily spot customers with unusually high or low spending. A Scatter Plot (C) directly shows the relationship between customer ID and total spending, making it easy to visually identify outliers based on their position relative to other data points. Option B is more suitable for trend analysis over time, and options D and E are useful but don't directly highlight individual customer outliers in terms of order value.


NEW QUESTION # 20
Identify the correct action involved in performing an exploratory ad-hoc analysis.

Answer: A

Explanation:
Ad-hoc analysis involves using queries to explore patterns and anomalies within data, deviating from predefined routines.


NEW QUESTION # 21
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