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| Section | Weight | Objectives |
|---|---|---|
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Anomaly detection and root cause analysis - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis |
| Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe |
| Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements |
| Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting |
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NEW QUESTION # 61
You have a Snowsight dashboard that visualizes daily sales trends. Business users complain that the dashboard takes too long to load, especially when filtering by specific product categories. The underlying data resides in a large table partitioned by 'sale date'. Which of the following actions would BEST improve the dashboard's performance, assuming the filters are appropriately configured in the dashboard and the virtual warehouse size is already appropriately sized?
Answer: C
Explanation:
Creating a materialized view pre-aggregates the data, significantly reducing query execution time. The materialized view stores the result of a query, and Snowflake automatically refreshes it when the underlying data changes. Since the product categories are used as filters, pre- aggregating along these dimensions directly addresses the slow loading times. Increasing warehouse size (B) only helps if the compute resources are a bottleneck, which might not be the primary issue. Converting to Streamlit (C) changes the presentation layer but doesn't inherently improve data retrieval. Query Acceleration (D) can help, but only if it properly sized and configured. Session level caching (E) might only benefit the same user, but if multiple users are accessing the same dashboard, the best way would be through pre-aggregated results in a materialized view.
NEW QUESTION # 62
A marketing team wants to visualize customer engagement across different channels (email, website, social media). They have engagement data stored in a Snowflake table 'CUSTOMER ENGAGEMENT' with columns 'CUSTOMER ID, 'CHANNEL', 'EVENT TYPE', 'EVENT TIMESTAMP', and 'EVENT VALUE' (representing engagement score). They need a dashboard that efficiently displays the total engagement score per channel for the last 7 days, allowing for quick drill-down into individual customer behavior. Which approach provides the MOST optimized query and view structure for this requirement, prioritizing performance for a large dataset?
Answer: B
Explanation:
Materialized views (B) are the most performant option because they pre-compute and store the aggregated data. A daily refresh using a Snowflake task ensures the data is up-to-date. Option A requires the aggregation to be performed every time the dashboard is loaded. Option C is less efficient than B because it filters after aggregation. Option D is not scalable for large datasets. Option E adds unnecessary complexity and potential performance bottlenecks with the stored procedure and temporary table.
NEW QUESTION # 63
Which Snowflake feature or object can be used to dynamically create and execute SQL statements?
Answer: C
Explanation:
While Snowflake provides various ways to encapsulate logic, Stored Procedures are the primary tool for procedural programming and dynamic SQL execution. Unlike standard UDFs, which are generally designed to calculate and return values within a query, stored procedures can execute administrative commands (DDL) and data manipulation (DML) that are not possible in a simple SELECT statement.
Using Snowflake Scripting (or languages like Python, Java, or JavaScript), an analyst can write a procedure that builds a SQL string as a variable and then executes it using the EXECUTE IMMEDIATE command. This is demonstrated in procedural logic flows where inputs determine specific execution paths. While Tasks can be used to schedule the execution of these procedures, the task itself is a scheduling agent and does not provide the logic-driven dynamic string construction inherent to the procedure.
NEW QUESTION # 64
A data pipeline is failing intermittently, with the error logs indicating 'Insufficient compute resources'. You are tasked with collecting data to diagnose the root cause. What combination of Snowflake features and data collection strategies would be MOST effective in identifying if warehouse auto-scaling or query performance is the primary contributor to the issue?
Answer: B,D
Explanation:
Options A and C provide a targeted approach. A allows tracking warehouse resource usage during the pipeline failures, identifying if the warehouse is reaching its limits. C helps identify slow-running queries contributing to resource exhaustion and detects queries using excessive local disk, indicating potential optimization opportunities. Increasing the warehouse size without analysis (B & D) is not cost-effective. Collecting only error logs (E) is not sufficient for a comprehensive diagnostic approach. It is too narrow and may not expose warehouse load issues and bottlenecks.
NEW QUESTION # 65
A Snowflake data analyst needs to identify the top 5 most common errors occurring in a data loading pipeline over the past week. The error messages are stored in a table named 'LOAD ERRORS' with columns 'error_timestamp' (TIMESTAMP NTZ) and 'error message' (VARCHAR). Which SQL query provides the most efficient and accurate way to retrieve this information, ordered by frequency?
Answer: E
Explanation:
Option A is the most efficient and accurate. It uses 'DATEADD with to ensure the correct time frame is considered (including the current time), and 'GROUP BY and 'ORDER BY are used correctly to find the top 5 most frequent errors. Option B excludes errors happening today by using 'CURRENT_DATE(Y. Option C casts error_timestamp to DATE which can impact performance and might cause issues if the index is available on error_timestamp, but not DATE(error_timestamp). Option D only shows errors from the current week. Option E is incorrect. It returns error_timestamp which is a date, therefore its incorrect
NEW QUESTION # 66
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