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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Semi-Structured Data | 10% | - Array and object operations - VARIANT data type - Parsing and flattening semi-structured data |
| Topic 2: Security & Access Control | 10% | - Row-level and column-level security - Authorization (roles, privileges) - Governance best practices - Masking policies - Authentication (SSO, MFA) |
| Topic 3: Performance Optimization & Monitoring | 4% | - Query profiling and performance tuning - Resource optimization techniques - Query history and monitoring |
| Topic 4: Data Loading & Unloading | 15% | - File formats (JSON, CSV, Parquet, AVRO) - Continuous loading with Snowpipe - Data ingestion best practices - Bulk loading with COPY command - Data unloading and staging |
| Topic 5: Time Travel & Cloning | 10% | - Time travel concepts and retention - Zero-copy cloning - UNDROP and historical data access - Fail-safe concepts |
| Topic 6: Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake architecture layers (storage, compute, services) - Virtual warehouse concepts and credit usage - Snowflake editions and features |
| Topic 7: Data Sharing | 12% | - Private sharing vs. Data Exchange - Shares and reader accounts - Reader account management - Data listing marketplace |
| Topic 8: Account Objects | 8% | - Warehouses, databases, and schemas hierarchy - Resource monitors - Organizations and accounts structure |
| Topic 9: Databases, Tables & Views | 18% | - Data manipulation (DML operations) - Stored procedures and UDFs - Column types and sequences - Clustering and micro-partitions - Views (standard, materialized, secure) - Table types (permanent, transient, temporary) |
| Topic 10: Virtual Warehouses | 15% | - Cache concepts (warehouse, result, metadata) - Warehouse monitoring and troubleshooting - Multi-cluster warehouses - Warehouse creation and configuration - Warehouse sizing and scaling |
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NEW QUESTION # 818
What are common issues found by using the Query Profile? (Choose two.)
Answer: A,C
Explanation:
The Query Profile in Snowflake is used to identify performance issues with queries. Common issues that can be found using the Query Profile include identifying inefficient micro-partition pruning (D) and data spilling to a local or remote disk (E).Micro-partition pruning is related to the efficiency of query execution, and data spilling occurs when the memory is insufficient, causing the query to write data to disk, which can slow down the query performance1.
NEW QUESTION # 819
A department uses a multi-cluster virtual warehouse to run ad hoc reports for multiple business analysts. The warehouse load history shows that many report queries were queuing before they were run. How can this issue be solved?
Answer: D
Explanation:
The correct answer is B. Add more clusters to the warehouse .
Queued queries on a multi-cluster warehouse usually indicate a concurrency problem. Multiple analysts are submitting queries at the same time, and the warehouse does not have enough active cluster capacity to process them concurrently.
Why B is correct:
Adding more clusters to a multi-cluster warehouse increases concurrency capacity. In Auto-scale mode, Snowflake can start additional clusters as needed, up to the configured maximum, to reduce queuing.
Why the other options are incorrect:
A). Scheduling or limiting reports may reduce concurrency, but it adds operational overhead and does not use Snowflake's built-in multi-cluster scaling capability.
C). Increasing warehouse size can make individual queries faster, but it does not directly improve concurrency as effectively as adding clusters.
D). A Snowpark-optimized warehouse is intended for memory-intensive Snowpark workloads, not for reducing query queues from many concurrent BI/ad hoc users.
Official Snowflake documentation reference:
Snowflake documentation explains that multi-cluster warehouses are designed to handle concurrency by automatically scaling out with additional clusters.
Reference: Snowflake Documentation - Multi-cluster warehouses; Warehouse load monitoring; SnowPro Core Study Guide - Snowflake Account and Resource Management.
NEW QUESTION # 820
How should clustering be used to optimize the performance of queries that run on a very large table?
Answer: C
Explanation:
For optimizing the performance of queries that run on a very large table, it is recommended to choose one high cardinality column as the clustering key.This helps to co-locate similar rows in the same micro-partitions, improving scan efficiency in queries by skipping data that does not match filtering predicates4.
References:[COF-C02] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 821
Which feature or service should be used to identify data about a department that was responsible for a recent revenue shortfall?
Answer: D
Explanation:
Snowflake ML Functions include Top Insights, which performs key-driver analysis to explain why a business metric changed. It can compare groups or time periods and rank dimensions that contributed most strongly to a result. Snowflake documentation uses the example of identifying locations, salespeople, customers, verticals, and other factors responsible for a recent revenue shortfall. A department can be evaluated as one of those dimensions, allowing the analysis to identify which department contributed to the decline. Snowpark is a developer framework for processing data with languages such as Python, Java, and Scala. Snowpipe loads files continuously, while AI_EXTRACT extracts structured information from documents. Those features do not directly perform the requested driver analysis. Therefore, ML Functions is the correct answer.
NEW QUESTION # 822
A global company needs to share financial reports with managers who are in different regions. Managers should only see the data from their respective regions. How can these requirements be met with the least amount of operational overhead?
Answer: A
Explanation:
The correct answer is C. Use row-level security on the source data table used to generate the reports .
The requirement is to let managers see only rows for their respective regions. This is a row-filtering requirement, so Snowflake row-level security is the best fit.
Why C is correct:
Row-level security in Snowflake is implemented using row access policies. A row access policy can dynamically filter table rows based on the current user, current role, or mapping table logic.
Example concept:
CREATE ROW ACCESS POLICY region_policy
AS (region STRING) RETURNS BOOLEAN - >
region IN (
SELECT allowed_region
FROM manager_region_map
WHERE manager_role = CURRENT_ROLE()
);
This allows one shared table or report to serve multiple managers while enforcing region-specific visibility.
Why the other options are incorrect:
A). Creating unique views for each region increases operational overhead as regions or access rules change.
B). Building individual reports for each manager creates the highest operational overhead and is difficult to maintain.
D). Dynamic Data Masking hides or transforms column values. It does not filter rows by region.
Official Snowflake documentation reference:
Snowflake documentation describes row access policies as schema-level objects that determine whether a row is visible in a query result. They are used to implement row-level security.
Reference: Snowflake Documentation - Row access policies; Snowflake Documentation - Dynamic Data Masking; SnowPro Core Study Guide - Data Protection and Governance.
NEW QUESTION # 823
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