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| Section | Weight | Objectives |
|---|---|---|
| Prepare and Present Data | 10–15% | - Data visualization and reporting - Snowsight dashboards and sharing results - Align outputs with business requirements |
| Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis - Statistical summarization and trend analysis |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting |
| Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion |
| Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation |
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NEW QUESTION # 54
What actions are typically involved in working with and querying data in Snowflake? (Select all that apply)
Answer: A,B,C,D
Explanation:
Working with Snowflake data involves identifying anomalies, using randomization, employing time travel for historical data retrieval, and utilizing materialized views for enhanced query performance.
NEW QUESTION # 55
A Data Analyst runs a query in a Snowflake worksheet, and selects a numeric column from the result grid.
What automatically-generated contextual statistic can be visualized?
Answer: D
Explanation:
One of the standout features of the Snowsight interface is its ability to perform automatic Data Profiling.
When a Data Analyst executes a query, Snowflake doesn't just return a raw grid of data; it analyzes the result set to provide immediate visual insights.
When you click on a column header in the results pane, a summary statistics panel appears. For numeric, date, and time columns, Snowflake automatically generates a histogram (Option A). This histogram provides a visual representation of the data distribution, allowing the analyst to quickly identify patterns, concentrations of values, or significant outliers without writing additional SQL code.
Evaluating the Options:
* Option B: While a histogram is a type of frequency distribution, Option A is more accurate because Snowsight also provides these visualizations for date and time types, not just integers/floats.
* Option C: While MIN and MAX values are displayed in the summary panel, they are text-based statistics, not the "visualized" contextual statistic (the histogram) emphasized in the question.
* Option D: "Key distribution" is not a standard visualization term used in the Snowsight profiling tool.
* Option A: Is the 100% correct answer. It highlights the breadth of the profiling tool (covering numbers, dates, and times) and the specific visual element (the histogram) that makes exploratory data analysis significantly faster for a Data Analyst.
NEW QUESTION # 56
You are working with a Snowflake table 'raw_data' containing a column of type TEXT that stores log messages in various formats, including JSON and CSV. You need to extract specific data points from these log entries, treating JSON entries differently from CSV entries. Specifically: For JSON log entries (identified by starting with '{l), extract the value of the 'user_id' key. For CSV log entries (identified by starting with a number), extract the second field (assuming comma-separated values). Which of the following queries is the most efficient and correct way to achieve this data extraction?
Answer: B
Explanation:
Option B provides the most efficient and correct solution. 1, 1) = '{' ' efficiently checks if the log entry starts with '{' without needing the full power of STARTS_WITH or PARSE_JSON. For JSON entries, it correctly uses to extract the 'user_id' directly from the VARIANT representation. For CSV entries, it uses ',', 2)' which is specifically designed to extract a single part from a delimited string, and is more efficient than using SPLIT. Option A will not treat the JSON objects in the 'log_entry' column as valid variants and needs to be parsed using PARSE_JSON explicitly to get correct answer. Option C uses GET _ PATH which cannot be used without parsing the JSON first. Option D will error out because you cannot use JSON dot notation on a TEXT column directly. Option E uses SPLIT TO TABLE which is more costly than SPLIT PART.
NEW QUESTION # 57
In Snowsight, why is the utilization of different chart types crucial for effective data analysis and interpretation?
Answer: B
Explanation:
Different chart types offer varied data representation, aiding analysis and interpretation in Snowsight.
NEW QUESTION # 58
You are loading data into a Snowflake table 'PRODUCT PRICES' using the COPY INTO command from a Parquet file stored in Azure Blob Storage. The 'PRODUCT PRICES' table has the following schema: VARCHAR, PRICE CURRENCY VARCHAR, LAST UPDATED TIMESTAMP NTZ. The Parquet file contains all these columns, but the 'LAST UPDATED column is stored as a Unix epoch timestamp (seconds since 1970-01-01 00:00:00 UTC). You need to transform the Unix epoch timestamp to a Snowflake TIMESTAMP NTZ during the data load. Which of the following options correctly demonstrates how to achieve this using a transformation within the COPY INTO command?
Answer: B
Explanation:
The correct answer is C. When using transformations with COPY INTO and an external stage, you must explicitly select the columns using '$1', '$2, etc. The gazure_stage/data.parquet' path is used in the 'FROM' clause, and converts the fourth column (LAST_UPDATED) from the Parquet file to a Snowflake TIMESTAMP. Note: It assumes column ordering in the Parquet file matches the order in the select statement. Using a SELECT statement within the FROM' clause is the correct approach when applying transformations directly during the COPY. The other options are incorrect because they either use the deprecated 'TRANSFORMATION' parameter (now replaced by select statements in the FROM clause) or attempt to apply transformations incorrectly. This is the only answer that is correct and will work. Options A, B, D, and E will all fail during the COPY INTO statement.
NEW QUESTION # 59
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