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| Section | Weight | Objectives |
|---|---|---|
| Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Query optimization and troubleshooting - Semi-structured data processing |
| Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs |
| Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Prepare and Present Data | 10–15% | - Align outputs with business requirements - Data visualization and reporting - Snowsight dashboards and sharing results |
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NEW QUESTION # 49
Why would an INFER_SCHEMA table function be used?
Answer: B
Explanation:
The INFER_SCHEMA function is a powerful tool in the Snowflake ingestion toolkit, specifically designed to bridge the gap between semi-structured data and relational tables. In modern data engineering, files like Parquet, Avro, and ORC are "self-describing," meaning they contain their own schema definitions (field names and data types) within the file header.
Before INFER_SCHEMA existed, a Data Analyst had to manually inspect a Parquet file, identify the keys, determine their data types, and then manually write a CREATE TABLE statement with the matching columns. INFER_SCHEMA automates this by scanning a set of files in a stage and returning a result set that describes the detected schema. This allows for the dynamic creation of tables that perfectly match the incoming data files.
Evaluating the Options:
* Option A is incorrect because "unstructured data" (like PDFs or Images) does not have a schema that can be "inferred" in a relational sense.
* Option C is incorrect because INFER_SCHEMA operates on files in a Stage, not on data already sitting in a table cell. To parse JSON in a table, you would use functions like FLATTEN or simple path notation.
* Option D is incorrect because the DESCRIBE TABLE or GET_DDL commands are used to retrieve definitions for existing tables.
* Option B is the Correct answer. It specifically identifies the role of the function: detecting metadata (schema) from staged semi-structured files. This function is often used in conjunction with GENERATE_COLUMN_DESCRIPTION and CREATE TABLE ... USING TEMPLATE to fully automate the schema evolution and ingestion process.
NEW QUESTION # 50
You have a Snowflake table named 'LOG DATA' with a "VARIANT' column called 'log_message" storing application logs in JSON format. The 'log_message' structure contains a nested JSON object called 'context' , which may or may not be present in every log entry. The 'context' object itself contains several key-value pairs, including potentially a 'user_id', a 'session_id' , and/or a 'request_id' You need to extract all the unique combination of 'user _ id' , and 'request_id' that exist across the dataset, accounting for instances where the 'context' object or any of the individual IDs are missing (represented as NULL). The goal is to ensure no errors are thrown due to missing fields. Select all the options which would achieve this outcome.
Answer: B,C
Explanation:
Options B and E are both valid solutions. Option B Uses 'TRY TO_STRING' function. If value is not present and is NULL, then automatically it will be converted to Null without any failure. Option E Uses 'COALESCE will handle any missing 'context object or missing IDs within the 'context' object without throwing an error. If any of the three values are NULL then it would be converted to string 'NULL'. Why other options are not entirely correct Option A This is not a correct answer, as a result in case NULL values encountered, this will result in failures. Option C & D This is a valid solution, however, since NVL and IFF functions are slower compared to TRY _ TO STRING and COALESCE, this result in slow processing. So, its not entirely correct answer.
NEW QUESTION # 51
A Data Analyst has a very large table with columns that contain country and city names. Which query will provide a very quick estimate of the total number of different values of these two columns?
Answer: C
Explanation:
When working with very large tables, calculating the exact number of unique combinations of columns (cardinality) using COUNT(DISTINCT ...) is a resource-intensive operation. It requires the Snowflake query engine to keep an exhaustive list of every unique pair encountered in memory, which can lead to high credit consumption and performance bottlenecks.
To provide a "very quick estimate," Snowflake utilizes the HyperLogLog (HLL) algorithm. The function HLL(column1, column2, ...) returns an HLL state (a binary representation) that can be used to estimate the number of distinct values with a high degree of accuracy and minimal computational overhead. This is part of Snowflake's suite of approximate aggregation functions, which are essential for Data Analysis on massive datasets where a 1% margin of error is acceptable in exchange for significantly faster results.
Evaluating the Options:
* Option A is syntactically incorrect; DISTINCT cannot be used in that position with COUNT.
* Option C is a valid query but will be significantly slower and more expensive than an HLL estimate on a "very large table".
* Option D simply counts the total number of non-null rows, which does not represent the "number of different values" (cardinality).
* Option B is the 100% correct answer. It specifically addresses the requirement for a "quick estimate" using the industry-standard probabilistic counting method built into Snowflake.
NEW QUESTION # 52
You are designing a data ingestion pipeline for IoT sensor data'. Sensors transmit data in JSON format every 5 seconds. The volume of data is expected to grow exponentially. The business requires both real-time dashboards and historical trend analysis. Which of the following strategies should you employ to address these requirements, particularly focusing on optimizing for both ingestion frequency and cost?
Answer: A,D
Explanation:
This question has multiple correct answers. Option B and E addresses the need for optimizing both ingestion frequency and cost. Option B involves using Snowflake tasks for transformations, which allows for separating data for real-time and historical analysis. This optimizes query performance for both use cases. Using only Snowpipe directly into a single table is simple but doesn't provide optimization for the different query patterns, while the other options are less efficient or don't fully address the dual requirements. Option E complements using Snowpipe by using Snowflake clustering features and employing stream processing engine to perform pre-aggregation. Snowflake clustering optimize the speed of historical analysis, reducing cost of scanning large datasets.
NEW QUESTION # 53
You are building a data pipeline to ingest customer data into Snowflake. You have identified a need to dynamically determine the data load timestamp during the ingestion process itself, without relying on external systems or pre-defined variables. Which system function(s) would be the MOST appropriate and efficient choice to accomplish this?
Answer: C
Explanation:
The function returns the current timestamp at the start of the statement. 'SYSDATE()' and 'GETDATE()' functions does not exists in Snowflake. is a synonym for CURRENT TIMESTAMP(). However, is not a standard documented function.
NEW QUESTION # 54
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