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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 2: Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets |
| Topic 3: Perform Descriptive and Diagnostic Analysis | 10–15% | - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis |
| Topic 4: Prepare and Present Data | 10–15% | - Align outputs with business requirements - Data visualization and reporting - Snowsight dashboards and sharing results |
| Topic 5: Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation |
| Topic 6: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Topic 7: 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 # 24
You need to create a UDF in Snowflake to mask personally identifiable information (PII) in a 'customers' table. The UDF should take a string as input (e.g., email address, phone number) and return a masked version. For email addresses, it should replace everything before the '@' symbol with ' For phone numbers (assuming a simple format like '123-456-7890'), it should mask all but the last four digits with 'X'. Which of the following UDF definitions is MOST efficient and correctly implements this logic using Snowflake's JavaScript UDF capabilities and appropriate error handling?
Answer: B
Explanation:
Option D is the most efficient and robust. It uses 'indexOf and regular expressions ('test()') for more reliable string matching, includes error handling using a 'try...catch' block, and correctly handles the different masking scenarios. The regex ensures that the phone number format is strictly validated before masking. Options A and B lack error handling and use less precise matching techniques (e.g., 'includes' which could lead to unintended behavior). Option C is similar to option D but utilizes javascript includes which is not reliable as indexof in finding exact position. Option E is a Python UDF, which, while valid, would be less performant than javascript UDF as specified in question.
NEW QUESTION # 25
You are tasked with analyzing website clickstream data stored in a Snowflake table named 'clickstream_data'. The table contains a 'variant' column named 'event data' that holds JSON data representing various events like 'page view', 'button click', and 'form submission'. You need to extract the 'page_url' from 'page_view' events, the 'button id' from 'button click' events, and the 'form id' from 'form submission' events. Design the most efficient Snowflake SQL query to achieve this, considering the performance implications of variant parsing and filtering. Assume the 'event_type' is directly available within the variant object as well. Which of the following queries would be most efficient?
Answer: C
Explanation:
Option C is the most efficient. Snowflake's native variant access is generally faster than using 'PARSE_JSON' Also, explicit casting to VARCHAR ensures consistency in the output data type. Option A although correct will produce an output that is of variant type for all fields. Option B is incorrect because if none of the when conditions matches, then it returns NULL. Option D uses PARSE JSON unneccessarily making it slower. Option E uses GET which is used to extract data from an object within VARIANT and not to directly evaluate the VARIANT column.
NEW QUESTION # 26
A data engineering team is implementing a complex data transformation pipeline using Snowflake tasks and streams. They need to monitor the execution of these tasks, track dependencies, and identify potential bottlenecks in near real-time. Which approach provides the MOST comprehensive solution for logging and monitoring the task execution and dependencies within Snowflake?
Answer: D
Explanation:
Option C provides the most comprehensive solution. Event tables provide a centralized and structured way to capture task-related events. Integrating with an external monitoring tool allows for visualization of task dependencies and near real-time monitoring capabilities. Option A is manual and time-consuming. Option B requires custom development and maintenance. Option D is overly complex. Option E only addresses failures, not overall monitoring.
NEW QUESTION # 27
How do row access policies and Dynamic Data Masking impact the creation of dashboards in terms of data visibility and security?
Answer: B
Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in dashboard creation.
NEW QUESTION # 28
You have a table named 'sales_data' with a column 'product_details' of type VARIANT containing JSON data for various products. The JSON structure is inconsistent; some products have a 'size' attribute as a string, while others have it as an integer, and some don't have the attribute at all. You need to extract the 'size' as a consistent numeric value (NULL if it's missing) for analysis. Which SQL statement using table functions and data type conversion techniques correctly and efficiently handles this data inconsistency?
Answer: C
Explanation:
TRY_TO_NUMBER() is the most robust and concise way to handle potential data type inconsistencies and missing values when extracting data from VARIANT columns. It attempts to convert the value to a NUMBER and returns NULL if the conversion fails. This elegantly handles both string and integer representations of 'size', as well as cases where 'size' is missing from the JSON. Using Case statements or IS_NUMBER requires more verbose logic and can be less efficient than using the built-in function. NVL replaces NULL with 0, which isn't suitable as the question asks for NULL if missing.
NEW QUESTION # 29
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