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| Section | Weight | Objectives |
|---|---|---|
| Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Anomaly detection and root cause analysis - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis |
| Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Prepare and Load Data | 15–20% | - External tables and data validation - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe |
| Prepare and Present Data | 10–15% | - Align outputs with business requirements - Snowsight dashboards and sharing results - Data visualization and reporting |
| Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting |
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NEW QUESTION # 62
What scheme is used by Snowflake to estimate the approximate similarity between two or more data sets?
Answer: B
Explanation:
Snowflake provides several "approximate" functions designed to handle massive scale with high efficiency.
While HyperLogLog (HLL) is the standard for estimating cardinality (unique counts), and APPROX_TOP_K is used for frequency estimation, the specific task of determining the similarity between two sets relies on a different probabilistic algorithm.
The MINHASH function is Snowflake's implementation for estimating the Jaccard similarity coefficient between two or more sets. Jaccard similarity is defined as the size of the intersection divided by the size of the union of the sample sets. Calculating an exact Jaccard similarity on billions of rows would be computationally expensive. MINHASH solves this by creating a "signature"-a small, fixed-size binary representation of the data. By comparing these signatures rather than the raw data, Snowflake can efficiently estimate how similar the original datasets are.
Evaluating the Options:
* Option B (APPROX_PERCENTILE) is used to estimate the value at a specific percentile (e.g., the
95th percentile of latency).
* Option C (HyperLogLog) is used for estimating cardinality (the number of unique elements), not the similarity between sets.
* Option D (APPROX_TOP_K) identifies the most frequent elements in a dataset.
* Option A is the 100% correct answer. It is the specific function built into Snowflake for similarity estimation using the MinHash scheme.
NEW QUESTION # 63
You have a large dataset of IoT sensor readings stored in compressed JSON files within an AWS S3 bucket. Each JSON file contains an array of sensor readings with the following structure:
You need to load this data into a Snowflake table named 'sensor data' with columns 'sensor id', 'timestamp', 'temperature', and 'humidity'. Which of the following Snowflake commands would be the MOST efficient and appropriate to ingest this data, assuming you have already created the table and a named stage pointing to the S3 bucket?
Answer: B
Explanation:
Option A is the most efficient and direct way to load the data. = TRUE' correctly handles the array of JSON objects within each file. Option B would fail because it doesn't strip the outer array. Option C is incorrect as it treats the JSON as CSV. Option D, although functional, involves creating a temporary table and flattening, making it less efficient. Option E is invalid syntax, and unnecessarily complex.
NEW QUESTION # 64
What considerations are essential when identifying the volume of data to be collected in a collection system? (Select all that apply)
Answer: C,D
Explanation:
Identifying the volume of data involves considering available storage capacity and the frequency of data analysis.
NEW QUESTION # 65
Which Snowflake feature or object can be used to dynamically create and execute SQL statements?
Answer: A
Explanation:
While Snowflake provides various ways to encapsulate logic, Stored Procedures are the primary tool for procedural programming and dynamic SQL execution. Unlike standard UDFs, which are generally designed to calculate and return values within a query, stored procedures can execute administrative commands (DDL) and data manipulation (DML) that are not possible in a simple SELECT statement.
Using Snowflake Scripting (or languages like Python, Java, or JavaScript), an analyst can write a procedure that builds a SQL string as a variable and then executes it using the EXECUTE IMMEDIATE command. This is demonstrated in procedural logic flows where inputs determine specific execution paths. While Tasks can be used to schedule the execution of these procedures, the task itself is a scheduling agent and does not provide the logic-driven dynamic string construction inherent to the procedure.
NEW QUESTION # 66
A Snowflake data analyst needs to identify the top 5 most common errors occurring in a data loading pipeline over the past week. The error messages are stored in a table named 'LOAD ERRORS' with columns 'error_timestamp' (TIMESTAMP NTZ) and 'error message' (VARCHAR). Which SQL query provides the most efficient and accurate way to retrieve this information, ordered by frequency?
Answer: B
Explanation:
Option A is the most efficient and accurate. It uses 'DATEADD with to ensure the correct time frame is considered (including the current time), and 'GROUP BY and 'ORDER BY are used correctly to find the top 5 most frequent errors. Option B excludes errors happening today by using 'CURRENT_DATE(Y. Option C casts error_timestamp to DATE which can impact performance and might cause issues if the index is available on error_timestamp, but not DATE(error_timestamp). Option D only shows errors from the current week. Option E is incorrect. It returns error_timestamp which is a date, therefore its incorrect
NEW QUESTION # 67
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