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| Section | Weight | Objectives |
|---|---|---|
| Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe |
| Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Align outputs with business requirements - Data visualization and reporting |
| Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| 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% | - Semi-structured data processing - Complex joins, subqueries, window functions - Query optimization and troubleshooting |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis |
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NEW QUESTION # 62
Identify the correct action involved in performing an exploratory ad-hoc analysis.
Answer: B
Explanation:
Ad-hoc analysis involves using queries to explore patterns and anomalies within data, deviating from predefined routines.
NEW QUESTION # 63
A data analyst observes a sudden and significant drop in sales for a particular product category within a Snowflake database. Initial investigations point to a possible data quality issue. Which of the following steps provides the MOST effective and efficient diagnostic approach using Snowflake features to pinpoint the root cause of the anomaly, focusing on data integrity?
Answer: B,E
Explanation:
Options A and C are the most effective. A utilizes Time Travel for direct data comparison before and after the incident, allowing for focused analysis on changed records. C investigates DML operations, which could directly explain data changes. Option B is inefficient and disruptive. Option D, while helpful, might not pinpoint the cause of a data corruption issue as fast as A and C. Option E is a poor solution, as it doesn't identify the root cause of the issue and it leads to potential data lost from the transactions between the last successful load and when the ETL processes were disabled.
NEW QUESTION # 64
In Snowflake, how does leveraging Time Travel contribute to handling data anomalies and ensuring data integrity?
Answer: A
Explanation:
Time Travel in Snowflake allows querying data as of specific timestamps, aiding in addressing data anomalies and ensuring data integrity by accessing historical data versions.
NEW QUESTION # 65
Which statement accurately describes the usage of materialized views in data analysis?
Answer: D
Explanation:
Materialized views provide precomputed snapshots of data, enhancing query performance by reducing computation overhead.
NEW QUESTION # 66
You have a table named 'sales_data' with a column 'product_details' of type VARIANT containing JSON data for various products. The JSON structure is inconsistent; some products have a 'size' attribute as a string, while others have it as an integer, and some don't have the attribute at all. You need to extract the 'size' as a consistent numeric value (NULL if it's missing) for analysis. Which SQL statement using table functions and data type conversion techniques correctly and efficiently handles this data inconsistency?
Answer: C
Explanation:
TRY_TO_NUMBER() is the most robust and concise way to handle potential data type inconsistencies and missing values when extracting data from VARIANT columns. It attempts to convert the value to a NUMBER and returns NULL if the conversion fails. This elegantly handles both string and integer representations of 'size', as well as cases where 'size' is missing from the JSON. Using Case statements or IS_NUMBER requires more verbose logic and can be less efficient than using the built-in function. NVL replaces NULL with 0, which isn't suitable as the question asks for NULL if missing.
NEW QUESTION # 67
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