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| Section | Weight | Objectives |
|---|---|---|
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Query optimization and troubleshooting - Semi-structured data processing |
| 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 - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion |
| Prepare and Present Data | 10–15% | - Align outputs with business requirements - Data visualization and reporting - Snowsight dashboards and sharing results |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis |
| 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 |
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NEW QUESTION # 27
A data analyst is tasked with optimizing a daily ETL pipeline that loads data from several external sources into a Snowflake data warehouse. One of the key transformations involves joining two large tables, 'ORDERS' (millions of rows) and 'CUSTOMERS' (hundreds of thousands of rows), based on 'CUSTOMER ID. The pipeline currently uses a standard 'JOIN' operation, but the transformation step is taking longer than expected. The analyst has explored various optimization techniques, including increasing virtual warehouse size, but the performance improvement is minimal. Assuming that the 'CUSTOMER ID column is appropriately indexed (or clustered, if applicable) in both tables, and you want to minimise data movement. Which of the following approaches would yield the MOST significant performance improvement for this transformation step, considering metadata caching and data distribution?
Answer: E
Explanation:
Ensuring consistent data types and collations (D) is crucial for optimal join performance in Snowflake. When data types and collations match, Snowflake can leverage its internal optimizations more effectively, including metadata caching and efficient data access patterns. Using a 'BROADCAST JOIN' hint without considering data distribution (A) might lead to unnecessary data movement and performance degradation. 'MAP JOIN' is unavailable in Snowflake (B). Redistributing data using 'DISTRIBUTION_TYPE = HASH' (C) is generally less efficient than leveraging Snowflake's automatic optimizations. Pre-sorting data (E) is unnecessary in Snowflake as it does not guarantee data colocation in the same way that other distributed systems might.
NEW QUESTION # 28
A data analyst is tasked with loading data into a Snowflake table 'ORDERS' with the following structure: 'CREATE TABLE ORDERS ( ORDER ID INT, CUSTOMER ID INT, ORDER DATE DATE, TOTAL_AMOUNT The data analyst needs to ensure that 'ORDER ID' is unique and not null, 'CUSTOMER ID' references a valid customer in the 'CUSTOMERS' table (column name 'CUSTOMER ID'), and 'ORDER DATE' is not in the future. Which of the following combination of constraints is the most efficient and appropriate way to enforce these Fules in Snowflake? 'CREATE TABLE CUSTOMERS ( CUSTOMER ID INT PRIMARY KEY, CUSTOMER_NAME VARCHAR(255));'.
Answer: A
Explanation:
Option E is the most appropriate and efficient. Using a PRIMARY KEY implies an index which while beneficial for joins isn't necessary if simple uniqueness and not null are the primary requirement. A CHECK constraint 'ORDER_DATE <= is the best way to prevent future dates as TRIGGER is not available and view doesn't prevent data from being ingested.
NEW QUESTION # 29
A data analyst needs to perform an exploratory ad-hoc analysis on a large customer order dataset in Snowflake to identify potential fraud. The dataset contains sensitive Personally Identifiable Information (PII). Which of the following approaches BEST balances the need for ad-hoc analysis with data security and compliance?
Answer: E
Explanation:
Dynamic Data Masking allows the analyst to perform ad-hoc analysis on the production data without exposing sensitive PII. Cloning with full access is risky. Exporting a sample may not be representative. Unrestricted access is a security violation. Data sharing with row-level security can be complex and might still inadvertently expose data.
NEW QUESTION # 30
In what way can regular views be advantageous in data analysis?
Answer: B
Explanation:
Regular views simplify complex data structures, aiding ease of analysis by providing a streamlined representation of data.
NEW QUESTION # 31
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 # 32
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