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| Section | Weight | Objectives |
|---|---|---|
| Virtual Warehouses | 15% | - Multi-cluster warehouses - Cache concepts (warehouse, result, metadata) - Warehouse creation and configuration - Warehouse sizing and scaling - Warehouse monitoring and troubleshooting |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Virtual warehouse concepts and credit usage - Snowflake editions and features - Snowflake architecture layers (storage, compute, services) |
| Performance Optimization & Monitoring | 4% | - Resource optimization techniques - Query history and monitoring - Query profiling and performance tuning |
| Account Objects | 8% | - Resource monitors - Warehouses, databases, and schemas hierarchy - Organizations and accounts structure |
| Data Loading & Unloading | 15% | - Continuous loading with Snowpipe - Data unloading and staging - Data ingestion best practices - Bulk loading with COPY command - File formats (JSON, CSV, Parquet, AVRO) |
| Semi-Structured Data | 10% | - Parsing and flattening semi-structured data - Array and object operations - VARIANT data type |
| Databases, Tables & Views | 18% | - Data manipulation (DML operations) - Stored procedures and UDFs - Table types (permanent, transient, temporary) - Clustering and micro-partitions - Views (standard, materialized, secure) - Column types and sequences |
| Time Travel & Cloning | 10% | - Time travel concepts and retention - UNDROP and historical data access - Zero-copy cloning - Fail-safe concepts |
| Security & Access Control | 10% | - Governance best practices - Row-level and column-level security - Masking policies - Authorization (roles, privileges) - Authentication (SSO, MFA) |
| Data Sharing | 12% | - Shares and reader accounts - Data listing marketplace - Private sharing vs. Data Exchange - Reader account management |
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NEW QUESTION # 981
A Medium virtual warehouse processes scheduled reports for a team. More than 30 reports run simultaneously during business hours, and the team requires high availability, consistent performance, and minimal startup time.
What action should be taken to help avoid queuing when these reports are run?
Answer: B
Explanation:
A multi-cluster warehouse in Auto-scale mode automatically starts additional clusters when concurrency increases and queries begin to queue. The Standard scaling policy prioritizes responsiveness and throughput by favoring the rapid startup of additional clusters, whereas the Economy policy prioritizes credit conservation and can allow queries to remain queued longer. This makes option D appropriate for a reporting workload that experiences a significant concurrency increase during business hours. Maximized mode keeps a fixed number of clusters running and is better suited to consistently high workloads that do not fluctuate significantly. Increasing the warehouse size scales up each cluster and can accelerate individual complex queries, but it does not address concurrency as directly as scaling out with additional clusters.
NEW QUESTION # 982
A Snowflake table that is loaded using a Kafka connector has a schema consisting of which two variant columns? (Select TWO).
Answer: A,C
Explanation:
When using the Snowflake Kafka connector, the table schema includes two important variant columns:
RECORD_TIMESTAMP: This column stores the timestamp from the Kafka record, enabling time-based analysis of incoming data.
RECORDKEY: This captures the unique key of each Kafka message, useful for uniquely identifying records or managing deduplication.
These columns ensure that each message's metadata and key information are preserved, facilitating data analysis and real-time processing tasks in Snowflake.
NEW QUESTION # 983
Which of the following is the Snowflake Account_Usage.Metering_History view used for?
Answer: C
Explanation:
The Snowflake Account_Usage.Metering_History view is used to gather the hourly credit usage for an account. This view provides details on the credits consumed by various services within Snowflake for the last
365 days1.
NEW QUESTION # 984
How can a data provider ensure that a data consumer is going to have access to the required objects?
Answer: A
Explanation:
To ensure a data consumer has access to the required objects, a data provider can enable the data sharing feature and validate that the consumer can access the views or tables shared with them.References:Based on general data sharing practices in cloud services as of 2021.
NEW QUESTION # 985
What does the TableScan operator represent in the Query Profile?
Answer: D
Explanation:
In the Query Profile of Snowflake, the TableScan operator represents the access to a single table. This operator indicates that the query execution involved reading data from a table stored in Snowflake. TableScan is a fundamental operation in query execution plans, showing how the database engine retrieves data directly from tables as part of processing a query.
References:
Snowflake Documentation: Understanding the Query Profile
NEW QUESTION # 986
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