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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe |
| Topic 2: Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis |
| Topic 3: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Topic 4: Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs |
| Topic 5: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Semi-structured data processing - Complex joins, subqueries, window functions - Query optimization and troubleshooting |
| Topic 6: Prepare and Present Data | 10–15% | - Data visualization and reporting - Align outputs with business requirements - Snowsight dashboards and sharing results |
| Topic 7: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
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NEW QUESTION # 27
Which Snowflake feature allows users to encapsulate a series of SQL statements into a reusable database object, facilitating modular code development?
Answer: B
Explanation:
In Snowflake, a Stored Procedure is the primary object used for procedural logic and the encapsulation of multiple SQL statements. Unlike standard functions that are typically restricted to returning a single value or a table based on an input, Stored Procedures are designed to perform administrative tasks, wrap complex business logic, and execute a sequence of operations-such as DDL (Data Definition Language) and DML (Data Manipulation Language) commands-within a single callable object.
Modular code development is facilitated by Stored Procedures because they allow analysts and developers to write logic once and reuse it across various workflows. For example, a procedure can be written to truncate a staging table, call a COPY INTO command, and then execute several INSERT statements into a final fact table. This "wrapper" approach ensures consistency, as any change to the logic only needs to be updated in the procedure definition rather than in every script or task that uses it.
Evaluating the Options:
* Option B is incorrect because a Materialized View is a pre-computed result set used to improve query performance; it does not "encapsulate a series of statements" for procedural execution.
* Option C is incorrect because while User-Defined Functions (UDFs) provide reusability, they are generally intended for calculations or data transformations that return a result. They are much more restricted than procedures; for instance, a UDF cannot execute DDL commands like CREATE TABLE.
* Option D is incorrect because a Common Table Expression (CTE) is a temporary named result set defined within the execution scope of a single SELECT, INSERT, UPDATE, or DELETE statement. It is not a persistent database object and cannot be reused across different sessions or scripts.
* Option A is the correct answer. Snowflake Stored Procedures can be written in multiple languages (JavaScript, Snowflake Scripting/SQL, Python, Java, or Scala), providing the flexibility needed for sophisticated, modular automation in a data pipeline.
NEW QUESTION # 28
You are using Snowpipe to continuously load JSON data from an external stage. Occasionally, some JSON records are malformed and cause the pipe to fail. You want to configure the pipe to skip these invalid records and continue loading valid data, while also capturing the error details for later analysis. Which approach provides the most efficient and appropriate solution for this scenario?
Answer: C
Explanation:
Option E is the most efficient and complete solution. SON ERROR = 'CONTINUE" allows Snowpipe to skip bad records and continue processing. Using it in conjunction with the 'VALIDATE function within the 'COPY INTO' statement enables capturing error information for analysis. This combines error skipping with error logging. Options A, B, C, and D are either less efficient (requiring pre-processing or post- processing of data), or do not provide a comprehensive solution for both skipping and capturing error information. Using (option B) is too coarse-grained as it skips entire files, even with only a few errors. Using 'VALIDATION MODE without ERROR=CONTINUE will still stop the pipe on errors.
NEW QUESTION # 29
In Snowflake, how do window functions differ from table functions?
Answer: C
Explanation:
Table functions operate on windowed rows, distinguishing them from window functions in Snowflake.
NEW QUESTION # 30
You are tasked with performing a descriptive analysis of website traffic data stored in a Snowflake table named 'website traffic'. The table includes columns such as 'session_id', 'user id', 'page_url' , 'timestamp' , and 'device_type'. Which of the following SQL queries would be MOST efficient and accurate for calculating the daily active users (DAU) and their device distribution?
Answer: B
Explanation:
Option E is the most efficient and accurate. It correctly uses user_id)' to calculate DALI, groups by date and device type, and orders the results. Option A is missing aggregation to calculate DAU per device. Option B uses APPROX COUNT DISTINCT which is less accurate. Option C counts all user_id entries, not distinct users. Option D includes user_id in the GROUP BY, causing incorrect DAU calculation, and calculates total users incorrectly.
NEW QUESTION # 31
How do constraints, such as primary keys, impact table joins between parent/child tables in Snowflake?
Answer: D
Explanation:
Primary keys enforce data uniqueness, ensuring integrity and maintaining relationships between parent and child tables in Snowflake.
NEW QUESTION # 32
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