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| Section | Weight | Objectives |
|---|---|---|
| Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Complex joins, subqueries, window functions - Semi-structured data processing |
| 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 Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion |
| Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis |
| Prepare and Present Data | 10–15% | - Align outputs with business requirements - Snowsight dashboards and sharing results - Data visualization and reporting |
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NEW QUESTION # 44
You have a CSV file loaded into a Snowflake table named 'raw data'. The file contains customer order data, but some rows have missing values in the 'order date' column. You need to create a new table, 'cleaned data' , that contains only valid records and handles missing 'order date' values by substituting them with the date '1900-01-01'. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowflake features?
Answer: A
Explanation:
Option E is the most efficient and correct. 'COALESCE' efficiently handles NULL replacement, and ensures the replacement value is the correct data type (DATE). It also explicitly selects all other columns. Option A only filters out rows with null order_date. Options B, C and D creates a new column, . It does not also implicitly take all columns, which would make this more appropriate.
NEW QUESTION # 45
How does the utilization of Regular views differ from Materialized views in data analysis?
Answer: C
Explanation:
Materialized views provide a persisted snapshot of data, differentiating them from Regular views.
NEW QUESTION # 46
You are tasked with identifying PII (Personally Identifiable Information) within several tables in your Snowflake data warehouse before granting access to a new analytics team. You have a database called 'CUSTOMER DATA with tables 'CUSTOMERS' , 'ADDRESSES' , and 'ORDERS'. Which of the following SQL queries, leveraging Snowflake's information schema, would be the most efficient and least intrusive method to discover potential PII columns, assuming you have a naming convention where PII columns often contain terms like 'EMAIL', 'PHONE', 'SSN', or 'NAME'?
Answer: B
Explanation:
The correct answer is E. It leverages the 'information_schema.columns' view, which is a standard and efficient way to retrieve metadata in Snowflake. using 'table_schema = 'CUSTOMER_DATA" and 'table_name IN ('CUSTOMERS', 'ADDRESSES', 'ORDERS')' filters the results to the specific database and tables of interest, making it more efficient than scanning all columns in the account or using table_catalog'. ensures case-insensitive matching. Options A and D uses either wrong schema or wrong like. Option B and C uses account_usage, that can introduce delay to data availability since that is for billing and monitoring and also does not filter by table names
NEW QUESTION # 47
You are examining sales data for an e-commerce company in Snowflake. You notice a recurring dip in sales every Wednesday. The 'SALES' table contains columns: 'order_id', 'order date' , 'product_id', 'quantity', 'price'. Which of the following SQL queries and analytical techniques would be the MOST effective for diagnosing the reasons behind this weekly sales dip, assuming no external promotional activity always happens on Wednesdays?
Answer: B,C,D,E
Explanation:
Options A, B, C, and E are the most relevant. Option A helps confirm the dip. Option B identifies product-specific patterns. Option C investigates a potential shipping-related cause. Option E explores potential software changes, while Option D focuses on a possible but less relevant cause of system maintenance.
NEW QUESTION # 48
When cleaning data, what role does using clones play in specific use-cases?
Answer: C
Explanation:
Clones are beneficial in isolating and resolving data anomalies without impacting the original dataset, facilitating safe data cleaning practices.
NEW QUESTION # 49
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