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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and Present Data | 10–15% | - Data visualization and reporting - Snowsight dashboards and sharing results - Align outputs with business requirements |
| Topic 2: Prepare and Load Data | 15–20% | - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro |
| Topic 3: Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis - Statistical summarization and trend analysis |
| Topic 4: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Topic 5: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 6: Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion |
| Topic 7: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Semi-structured data processing - Query optimization and troubleshooting - Complex joins, subqueries, window functions |
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NEW QUESTION # 60
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: A
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 # 61
A healthcare provider is investigating patient readmission rates within 30 days of discharge. They suspect a correlation between patient demographics (age, gender, location) and readmission. You have the following tables: 'PATIENTS': 'patient_id', 'age', 'gender' , 'zip_code' 'ADMISSIONS': 'admission_id' , 'patient_id' , 'admission_date', 'discharge_date' Which of the following approaches would be MOST effective to identify patient demographics significantly correlated with higher readmission rates within 30 days? (Select TWO)
Answer: A,D
Explanation:
Options A and D are the most effective. A suggests using statistical tests (Chi-square) which can identify statistically significant differences in readmission rates across demographic groups. D proposes cohort analysis, enabling the tracking and visualization of readmission trends for different demographics over time, allowing for the identification of segments with consistently high readmission rates. Option B is not optimal as it jumps directly into complex modeling without initial EDA. Option C, while valid, doesn't handle categorical variables (gender, zip_code) well and might miss non-linear relationships. Option E, identifying high-frequency readmitters, is helpful but doesn't directly address the relationship with demographics.
NEW QUESTION # 62
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,E
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 # 63
How does operationalizing data contribute to maintaining reports and dashboards for business requirements?
Answer: D
Explanation:
Operationalizing data ensures consistent and efficient usage of reports and dashboards.
NEW QUESTION # 64
In Snowflake, how does the implementation of primary keys for tables contribute to data integrity?
Answer: B
Explanation:
Primary keys enforce uniqueness in table rows, ensuring data integrity by preventing duplicate entries within a table.
NEW QUESTION # 65
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