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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis |
| Topic 2: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Topic 3: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Semi-structured data processing - Query optimization and troubleshooting - Complex joins, subqueries, window functions |
| Topic 4: Prepare and Present Data | 10–15% | - Align outputs with business requirements - Snowsight dashboards and sharing results - Data visualization and reporting |
| Topic 5: Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs |
| Topic 6: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 7: Prepare and Load Data | 15–20% | - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation - File formats: CSV, JSON, Parquet, Avro |
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NEW QUESTION # 37
How does leveraging partition pruning enhance query performance in Snowflake?
Answer: B
Explanation:
Partition pruning optimizes query planning by excluding unnecessary partitions from query execution, improving query performance by focusing on relevant data subsets.
NEW QUESTION # 38
You have a Snowflake table 'customer data' containing personally identifiable information (PII) such as customer names and addresses. You need to create a de-identified version of this table for analytical purposes, replacing the names and addresses with synthetic data, while preserving referential integrity with other tables that use the original customer IDs. Which combination of Snowflake features and techniques would you use to achieve this securely and efficiently?
Answer: C,D
Explanation:
Options B and D are the best choices. Option B directly addresses the need for a de-identified table by generating synthetic data and using HASH to preserve referential integrity. Secure data sharing provides controlled access. Option D uses external functions to leverage specialized de-identification services. Options A and E are useful for dynamic masking, but do not create a separate de-identified dataset and may still expose PII to authorized users. Option C involves exporting data, which increases the risk of data leakage and is less efficient.
NEW QUESTION # 39
Your company is using Snowflake to store customer transaction data'. You want to enrich this data with demographic information from a Snowflake Marketplace data provider. The provider offers a secure data share with a view called 'CUSTOMER DEMOGRAPHICS. You need to join the customer transaction data in your 'TRANSACTIONS' table with the demographic data from the 'CUSTOMER DEMOGRAPHICS' view. Which of the following SQL queries is the MOST efficient and secure way to achieve this, assuming you have already created a database from the share?
Answer: B
Explanation:
Option B is the most efficient and secure because it explicitly uses an INNER JOIN, ensuring that only matching records between the TRANSACTION table and the Customer Demographics view are included. Using INNER JOIN improves performance compared to implicit joins (Option A). Options C and E uses LEFT and FULL OUTER joins which might result in unnecessary nulls and impacting the perfromance. Option D will not work because the schema name is missing.
NEW QUESTION # 40
In the context of data processing solutions, how does handling processing failures contribute to ensuring data reliability?
Answer: B
Explanation:
Handling processing failures ensures data reliability by preventing data loss or corruption, maintaining the integrity of processed data.
NEW QUESTION # 41
What role does operationalizing data play in maintaining reports and dashboards for business requirements?
Answer: D
Explanation:
Operationalizing data ensures consistent and efficient usage of reports and dashboards.
NEW QUESTION # 42
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