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| Section | Weight | Objectives |
|---|---|---|
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause analysis |
| Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements |
| Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Prepare and Load Data | 15–20% | - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro - External tables and data validation |
| Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Semi-structured data processing - Complex joins, subqueries, window functions - Query optimization and troubleshooting |
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NEW QUESTION # 57
You have a Snowflake table 'CUSTOMER DATA' containing customer information. You want to enrich this data using two separate data shares from the Snowflake Marketplace. Share A provides demographic information, and Share B provides credit risk scores. Both shares contain views named 'CUSTOMER ENRICHMENT with a common column 'CUSTOMER ID'. Due to compliance requirements, you need to ensure that only customers with a credit risk score above a certain threshold (e.g., 700) are enriched with demographic data'. Which of the following approaches ensures that the customer data is enriched securely, efficiently, and in compliance with the credit risk threshold?
Answer: E
Explanation:
Option C is the most secure, efficient, and compliant approach. Using a single view with a CTE allows you to encapsulate the credit risk filtering logic within the view definition, ensuring that only customers meeting the threshold are enriched. This avoids exposing sensitive credit risk information to unauthorized users. Views provide row-level security for the table. Options A requires two steps and is less efficient, Option B is less efficient and harder to maintain than a view, and Options D is not recommended for sharing scenarios. Option E will require an additional task creation. Using single view with CTE will simplify the query to implement it with minimum code.
NEW QUESTION # 58
A large fact table is partitioned by and clustered by 'customer _ id'. The table has the following columns: 'customer_id', and 'transaction_amount'. You need to optimize queries that frequently filter on a specific range of 'transaction_date' and then aggregate by 'customer _ id'. Given the existing partitioning and clustering, which of the following strategies will BEST improve query performance related to partition pruning and clustering?
Answer: C
Explanation:
Option B is the best strategy. Creating a materialized view that pre-aggregates the data by and 'transaction_date' addresses both aspects:Partition pruning is naturally leveraged because the materialized view will store aggregated data, allowing queries filtering on 'transaction_date' to use partition pruning during refresh and query. Clustering helps because the data within each partition (date) is clustered by 'customer_id' , making aggregations by customer efficient. Option A might not provide sufficient performance improvement if the aggregation by customer is still slow. Option C will improve query performance marginally but is not a good option with partition pruning, because the data is already partition on date. Option D reclustering too frequently can be costly and may not always result in significant performance gains. Option E can be a costly operation and also data migration may be hectic. Thus the best is to have materialized view.
NEW QUESTION # 59
You are tasked with validating the 'SALES DATA' table containing sales records. One of the columns, 'SALE AMOUNT', is defined as VARCHAR, but it should be a NUMERIC. Some rows contain non-numeric characters and NULL values represented as the string 'NULL'. You need to identify rows that will cause errors when casting 'SALE AMOUNT to NUMERIC, and replace these rows with valid values using Snowflake SQL. Which of the following SQL statements, when executed in sequence, effectively identifies and corrects problematic 'SALE AMOUNT' values? Note: Assume the "REPLACE INVALID CHARACTERS' UDF correctly replaces non- numeric characters with empty strings.
Answer: C
Explanation:
Option D correctly addresses all aspects of the problem. First, it replaces 'NULL' string values with actual NULLs. Then, it creates a temporary table, 'INVALID SALES, containing rows where 'SALE AMOUNT, after removing invalid characters, cannot be converted to a DECIMAL. Finally, it updates these invalid rows in the original table with a default value of '0'. TRY_TO_DECIMAL is important for handling decimal values that cannot be converted
NEW QUESTION # 60
A logistics company needs to determine which warehouses are within a 50km radius of a new distribution center. The warehouse locations are stored in a table 'WAREHOUSES' with columns 'WAREHOUSE ID' ONT), (GEOGRAPHY) and the distribution center's location is stored in a variable of type GEOGRAPHY. Which query will efficiently identify all warehouses within the specified radius, returning the 'WAREHOUSE ID and distance in kilometers?
Answer: A
Explanation:
The correct answer uses 'ST DWITHIN' with the correct parameters and unit. 'ST DWITHIN(LOCATION, @distribution_center, 50000)' correctly filters the warehouses based on the 50km (50000 meters) radius. ST_DISTANCE calculates the distance in meters, which is then converted to kilometers by dividing by 1000. The warehouse location should come first followed by the distribution centre in the DWITHIN' Function.
NEW QUESTION # 61
You are tasked with preparing a large dataset of website clickstream data stored in a Snowflake table named This table contains a 'timestamp' column (TIMESTAMP NTZ), a column (VARCHAR), and a 'page_urr column (VARCHAR). You need to identify the most popular pages visited by users during specific hours of the day, but only for users who have visited at least 5 unique pages in the dataset. Which sequence of SQL operations, potentially including temporary tables or CTEs, would efficiently achieve this goal in Snowflake?
Answer: A,B
Explanation:
Option B and E are the most efficient solutions. Option B uses a CTE (Common Table Expression) to filter users who have visited at least 5 unique pages and then joins this result with the original table to calculate visit counts by hour and page. Option E creates a TEMP table for qualified users then joins against it to create a final aggregated set. A is functionally correct but may not be as optimized as using a CTE and uses two queries. C has an unnecessary subquery in the WHERE clause, which can degrade performance. D uses inline view which is less readable and less efficient than CTE. Both CTEs (B) and temporary tables (E) are common techniques in Snowflake for breaking down complex queries and can improve readability and maintainability. Snowflake's query optimizer is generally very good, and the CTE approach (B) is often preferred for its clarity.
NEW QUESTION # 62
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