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| Section | Weight | Objectives |
|---|---|---|
| Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting |
| Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis |
| Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs |
| 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 # 43
A data analyst is tasked with monitoring the performance of several Snowpipe data ingestion processes. They need to set up alerts based on the number of files failing to load within a specific timeframe. Which combination of Snowflake features and logging/monitoring practices would be MOST effective in achieving this?
Answer: C,D
Explanation:
Option A provides a robust solution by leveraging Snowflake's PIPE_USAGE_HISTORY to query load errors. An external monitoring tool allows for advanced alerting capabilities beyond Snowflake's native functionality. Option C offers another effective strategy using Snowflake's event tables for capturing errors and using a stored procedure for customizable alerting based on defined thresholds. Options B and D are less directly related to tracking file loading failures specifically. Option E is not a proactive monitoring approach.
NEW QUESTION # 44
You have two tables in Snowflake: 'ORDERS and 'CUSTOMERS. The 'ORDERS' table contains information about customer orders, including 'ORDER ID', 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. The 'CUSTOMERS table contains information about customers, including 'CUSTOMER ID', 'CUSTOMER NAME' , and 'CUSTOMER ADDRESS'. You need to create a view that joins these two tables based on 'CUSTOMER ID and includes only orders placed in the last 30 days. You also want to ensure that the view leverages the primary key information defined on the 'CUSTOMERS' table (even though Snowflake doesn't enforce it) for potential query optimizations. Which of the following SQL statements is the MOST efficient and best practice approach, considering Snowflake's optimizer?
Answer: B
Explanation:
Using 'CREATE OR REPLACE SECURE VIEW' is the best practice. 'CREATE OR REPLACE' allows you to update the view definition if needed without dropping and recreating it, minimizing disruption. A 'SECURE VIEW' prevents users from seeing the underlying query logic or accessing the base tables directly, enhancing security. The explicit JOIN syntax is preferred over the older implicit join syntax (option B). Snowflake's optimizer will leverage defined primary key relationships to optimize the join, even though it doesn't enforce them. Explicitly referencing schema isn't needed unless dealing with ambiguous names across schemas. The WHERE clause correctly filters for orders in the last 30 days.
NEW QUESTION # 45
You are tasked with cleaning a dataset containing customer information. One of the columns, 'PHONE NUMBER', contains inconsistent data: some entries have leading country codes (+1), some have dashes, and others have no separators at all. You need to create a function that standardizes all phone numbers to a consistent format (e.g., '1234567890'). Which of the following Snowflake UDFs (User- Defined Functions) effectively cleans and standardizes the phone numbers, removing all non-numeric characters and country codes?
Answer: B,C
Explanation:
Option A and E are correct. Both use REGEXP REPLACE to remove any non-numeric characters (to-9]+). Then, they use REGEXP_REPLACE again to remove the leading country code (+1 or 1). The other options are either too simplistic (only handling specific characters) or don't fully remove the country code reliably. Option A removes leading '1+' and Option E removes '+1'. The '+ sign needs to be escaped in the regex pattern.
NEW QUESTION # 46
How do Snowsight's data loading capabilities impact data ingestion?
Answer: D
Explanation:
Snowsight allows streamlined data import from various sources, enhancing data ingestion capabilities.
NEW QUESTION # 47
A Snowflake table 'transactions' stores data about financial transactions. The table includes the following columns: 'transaction_id' (INTEGER), 'account_id' (INTEGER), 'transaction_date' (DATE), and 'transaction_amount' (NUMBER). You need to analyze the moving average of transaction amounts for each account over a 7-day window. The moving average should be calculated for each transaction date, considering the 3 preceding days, the current day, and the 3 following days. You want to show the 'account_id' , 'transaction_date', 'transaction amount', and the calculated 'moving_average". What's the most appropriate and efficient Snowflake query to perform this calculation?
Answer: B
Explanation:
Option B is the correct answer. The 'PARTITION BY account_id' clause ensures that the moving average is calculated separately for each account. The 'ORDER BY transaction_date ASCS clause orders the transactions within each account by date. The 'ROWS BETWEEN 3 PRECEDING AND 3 FOLLOWING' clause defines the window frame as the current row, the 3 preceding rows, and the 3 following rows based on row number (not date ranges). Option A doesn't partition by account_id, so the moving average will be across all accounts which is not what we want. Option C uses 'RANGE instead of which relies on finding all entries within that date range, that may have many entries making the numbers incorrect. Option D only considers preceding rows, not following ones. Option E doesn't have any windowing and therefore the data is calculated to the current point, without consideration for following or previous days.
NEW QUESTION # 48
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