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| Section | Weight | Objectives |
|---|---|---|
| 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% | - Query optimization and troubleshooting - Complex joins, subqueries, window functions - Semi-structured data processing |
| Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Align outputs with business requirements - Data visualization and reporting |
| Prepare and Load Data | 15–20% | - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro |
| Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| 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% | - Anomaly detection and root cause analysis - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis |
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NEW QUESTION # 27
A ride-sharing company wants to analyze the density of ride requests in different city areas. They have a table 'RIDES' with a 'LOCATION' (GEOGRAPHY) column representing the pickup location of each ride. They want to divide the city into a grid of hexagonal cells and count the number of rides originating in each cell. Which sequence of steps would achieve this, assuming the 'city_boundary' is defined and accessible as a GEOGRAPHY object?
Answer: A
Explanation:
'ST HEXGRID is the correct function for generating a hexagonal grid. is used to check if a hexagonal cell contains a ride's location. Aggregating the ride counts for each cell provides the density information.
NEW QUESTION # 28
When employing different data models (e.g., dimensional, Data Vault) in Snowflake, how does Data Vault modeling address complexities in data integration and changes?
Answer: C
Explanation:
Data Vault models simplify data integration and adapt well to changes, providing advantages in handling complexities and evolutions in data structures.
NEW QUESTION # 29
A marketing team requires a daily report showcasing website traffic, conversion rates, and cost per acquisition (CPA). They want to receive this report as a CSV file attached to an email. The data is stored in a Snowflake table 'WEB ACTIVITY with columns 'DATE , 'VISITS, , and SPEND. Which of the following steps, combined and executed in the correct order, would be the MOST efficient and secure way to automate this report delivery?
Answer: C
Explanation:
Option B is the most efficient and scalable. Using an external orchestrator provides better scheduling control and separation of concerns. Writing to S3 allows for easier integration with other external services. Python connector is preferred for data transformation and CSV generation compared to Snowflake scripting. Utilizing AWS SES for email delivery scales well and aligns with a cloud-native approach. Option A is viable but can be resource-intensive within Snowflake. Options C, D and E are less efficient due to relying on event-based triggers or third party tools for scheduling and email delivery. Option D exposes the data to a third-party tool. Option E requires complex setup and is not the best choice for a simple daily report.
NEW QUESTION # 30
When building a Snowsight dashboard that will allow users to filter data within a worksheet, which Snowflake system filters should be used?
Answer: D
Explanation:
Snowsight provides special System Keywords that allow analysts to create dynamic, interactive dashboards without hard-coding dates. Two of the most critical keywords are :daterange and :datebucket.
The :daterange keyword is designed to filter the volume of data based on a time period selected by the user in the dashboard UI (e.g., "Last 30 days" or "Current Year"). Because it acts as a filter on the underlying data, it must be placed in the WHERE clause of the SQL statement (e.g., WHERE created_at = :daterange). This ensures that only the records within the selected timeframe are processed by the query.
The :datebucket keyword is used for time-series aggregation. It allows the user to dynamically change the granularity of the data-for example, switching a chart from "Daily" to "Monthly" views without rewriting the query. To achieve this, :datebucket is used inside a date-truncation function in the SELECT list and, crucially, must be included in the GROUP BY clause to correctly aggregate the metrics (e.g., GROUP BY 1 or GROUP BY :datebucket).
Evaluating the Options:
* Option A is incorrect because :datebucket is for grouping/truncation, not for filtering in a WHERE clause.
* Option B is incorrect because :daterange is a filter (returning a range), not a scalar value suitable for a SELECT list.
* Option C is incorrect for the same reasons as A and B.
* Option D is the 100% correct answer. It follows the standard Snowsight design pattern: :daterange restricts the data rows in the WHERE clause, while :datebucket defines the temporal aggregation level in the GROUP BY clause.
NEW QUESTION # 31
You are tasked with cleaning and transforming a dataset containing customer information in Snowflake. This dataset includes columns such as 'CUSTOMER ID, 'NAME', 'EMAIL', 'PHONE NUMBER', and 'ADDRESS'. Your goal is to implement several data quality rules: 1) Ensure all phone numbers are in a consistent '+1-XXX-XXX-XXXX' format (where X is a digit). 2) Remove any leading or trailing whitespace from the 'NAME and 'ADDRESS' columns. 3) Replace invalid email addresses (identified by failing a specific regex pattern) with NULL. Which combination of Snowflake SQL statements would efficiently and accurately address these requirements?
Answer: B
Explanation:
Option D correctly addresses all requirements by performing all transformations within a single CREATE OR REPLACE TABLE statement. This ensures atomicity and avoids multiple update operations. It correctly trims NAME and ADDRESS, replaces invalid emails with NULL, and formats phone numbers if they have exactly 10 digits after removing non-numeric characters, otherwise assigning NULL. The correct option must format phone number as well as replace with NULL, while not replacing with NULL would mean keep original. Option E keeps original value not NULL, if phone number doesn't match criteria.
NEW QUESTION # 32
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