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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion |
| Topic 2: Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis |
| Topic 3: Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Topic 4: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Query optimization and troubleshooting - Semi-structured data processing |
| Topic 5: Prepare and Present Data | 10–15% | - Align outputs with business requirements - Snowsight dashboards and sharing results - Data visualization and reporting |
| Topic 6: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 7: Prepare and Load Data | 15–20% | - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro |
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NEW QUESTION # 61
Consider the following Snowflake table schema and data: 'CREATE TABLE products (product_id INTEGER, product_name VARCHAR, properties VARIANT);' Data: 'INSERT INTO products VALUES (1, 'Laptop', "silver", "storage": "512GB", "price": 1200.00}'));' 'INSERT INTO products VALUES (2, 'Mouse', "wireless", "dpi": 1600, "price": 25.00}'));' 'INSERT INTO products VALUES (3, 'Keyboard', PARSE JSON('{"layout": "US", "backlit": true, "price": Which of the following SQL queries will return the 'product_name' and 'price' for all products where the 'price' is greater than 50, ensuring that the 'price' is treated as a numeric value for comparison? Select all that apply
Answer: A,E
Explanation:
Options B and E are correct. Option B explicitly casts 'properties:price' to a 'NUMBER data type before the comparison, ensuring that the comparison is performed numerically. Option E casts 'properties:price' to a data type and uses TRY_TO_NUMBER to handle potential errors gracefully. Option A is incorrect because Snowflake treats the value extracted from the VARIANT as a string and the string comparison will lead to incorrect results. Options C and D don't work without casting to VARCHAR
NEW QUESTION # 62
In Snowsight, what is the significance of creating diverse chart types (e.g., bar charts, scatter plots, heat grids) for data visualization?
Answer: D
Explanation:
Diverse chart types offer varied data representation, facilitating better analysis in Snowsight.
NEW QUESTION # 63
In data modeling for BI requirements, when is it preferable to use a flattened data set instead of a data model?
Answer: C
Explanation:
Flattened data sets are suitable for quick and simple data exploration due to their simplified structure, facilitating easy access and analysis.
NEW QUESTION # 64
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,C
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 # 65
How do automated and repeatable tasks contribute to maintaining reports and dashboards to meet business requirements?
Answer: B
Explanation:
Automated tasks ensure consistency and reduce manual effort in maintaining reports and dashboards.
NEW QUESTION # 66
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