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| Section | Weight | Objectives |
|---|---|---|
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis - Statistical summarization and trend analysis |
| Prepare and Load Data | 15–20% | - External tables and data validation - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe |
| Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Query optimization and troubleshooting - Semi-structured data processing |
| Prepare and Present Data | 10–15% | - Data visualization and reporting - Align outputs with business requirements - Snowsight dashboards and sharing results |
| Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
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質問 # 51
How do statistics and built-in functions contribute to forecasting based on data?
正解：C
解説：
Statistics and built-in functions facilitate the creation of custom forecasting models, providing flexibility.
質問 # 52
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?
正解：B
解説：
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.
質問 # 53
Which action is crucial for identifying demographics and relationships during diagnostic analysis?
(Select all that apply)
正解：A、D
解説：
Analyzing demographic variations and considering relationships are crucial in identifying anomalies during diagnostic analysis.
質問 # 54
How do row access policies and Dynamic Data Masking affect the creation and maintenance of reports and dashboards?
正解：A
解説：
Row access policies restrict data visibility based on user privileges, ensuring better security in creation and maintenance of reports and dashboards.
質問 # 55
You have a Snowflake table 'CUSTOMER ORDERS with columns 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. You need to calculate the cumulative sum of 'ORDER AMOUNT' for each customer, ordered by 'ORDER DATE. However, due to potential late-arriving data, you also need to implement a windowing function that resets the cumulative sum if there's a gap of more than 30 days between consecutive orders for a customer. Which SQL query best accomplishes this?
正解：C
解説：
Option C correctly uses a conditional partitioning approach. UG(ORDER DATE, 1, ORDER DATE) OVER (PARTITION BY CUSTOMER_ID ORDER BY ORDER_DATE)' calculates the previous order date for each customer. (ORDER_DATE - 1, ORDER DATE) OVER (PARTITION BY CUSTOMER ID ORDER BY ORDER DATE) > 30)' creates a boolean expression that is true when the difference between consecutive order dates exceeds 30 days. This boolean expression is then used as a secondary partition key, effectively restarting the cumulative sum whenever a gap of more than 30 days occurs. The primary partition is still 'CUSTOMER ID' , ensuring sums are calculated within each customer's order history. The ordering of 'ORDER_DATE is essential for the cumulative sum to be calculated chronologically.
質問 # 56
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