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| Section | Weight | Objectives |
|---|---|---|
| Data Loading & Unloading | 15% | - Continuous loading with Snowpipe - File formats (JSON, CSV, Parquet, AVRO) - Data ingestion best practices - Bulk loading with COPY command - Data unloading and staging |
| Time Travel & Cloning | 10% | - Fail-safe concepts - UNDROP and historical data access - Zero-copy cloning - Time travel concepts and retention |
| Databases, Tables & Views | 18% | - Views (standard, materialized, secure) - Stored procedures and UDFs - Clustering and micro-partitions - Data manipulation (DML operations) - Table types (permanent, transient, temporary) - Column types and sequences |
| Security & Access Control | 10% | - Authorization (roles, privileges) - Row-level and column-level security - Masking policies - Authentication (SSO, MFA) - Governance best practices |
| Account Objects | 8% | - Organizations and accounts structure - Resource monitors - Warehouses, databases, and schemas hierarchy |
| Semi-Structured Data | 10% | - Parsing and flattening semi-structured data - Array and object operations - VARIANT data type |
| Virtual Warehouses | 15% | - Multi-cluster warehouses - Warehouse monitoring and troubleshooting - Cache concepts (warehouse, result, metadata) - Warehouse sizing and scaling - Warehouse creation and configuration |
| Performance Optimization & Monitoring | 4% | - Query profiling and performance tuning - Query history and monitoring - Resource optimization techniques |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake architecture layers (storage, compute, services) - Snowflake editions and features - Virtual warehouse concepts and credit usage |
| Data Sharing | 12% | - Reader account management - Data listing marketplace - Shares and reader accounts - Private sharing vs. Data Exchange |
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NEW QUESTION # 108
What is the MINIMUM size requirement when creating a Snowpark-optimized virtual warehouse?
Answer: B
Explanation:
When creating a Snowpark-optimized virtual warehouse in Snowflake, the minimum size requirement is Small. Snowpark is designed to handle data processing workloads efficiently, and the Small size ensures adequate resources for such tasks.
Virtual Warehouse Sizes:
Snowflake offers different sizes for virtual warehouses, ranging from X-Small to 6X-Large.
Each size corresponds to a specific level of compute resources.
Minimum Size Requirement for Snowpark:
A Small virtual warehouse is the minimum size required to optimize performance and resource allocation for Snowpark workloads.
This ensures that the warehouse has sufficient capacity to handle data processing and transformation tasks efficiently.
References:
Snowflake Documentation: Virtual Warehouse Sizes
NEW QUESTION # 109
Which statement returns a NULL value instead of raising an error when an input cannot be converted to a date?
Answer: B
Explanation:
TRY_TO_DATE is the error-handling version of TO_DATE. It attempts to convert a supported string expression into a DATE value, but when the conversion cannot be performed, it returns SQL NULL instead of terminating the query with a conversion error. This behavior is useful when a column may contain occasional malformed or unexpected date strings and the query must continue processing valid records. DATE and TO_DATE perform normal conversions and can raise errors for invalid values.
The ::DATE operator is equivalent to an explicit cast and can also fail when the source value cannot be converted. TRY-prefixed conversion functions should generally be used when conversion failures are expected to be infrequent, because processing a large number of failed conversions can reduce performance.
NEW QUESTION # 110
What are the results of the NULL_COUNT Data Metric Function, or DMF, used to verify?
Answer: B
Explanation:
The correct answer is D. The volume of the data .
The NULL_COUNT Data Metric Function returns the number of NULL values in a column. This type of metric is used to verify data quality by measuring the amount, or volume, of missing values.
Why D is correct:
NULL_COUNT counts how many rows contain NULL in a specified column. Since it produces a count, it is used to evaluate the volume of missing data.
Example concept:
SELECT SNOWFLAKE.CORE.NULL_COUNT(
SELECT column_name FROM my_table
);
This returns the number of NULL values found in the selected column.
Why the other options are incorrect:
A). Accuracy verifies whether data values are correct, but NULL_COUNT only counts missing values.
B). Uniqueness is verified with metrics that check duplicate or distinct values, not NULL_COUNT.
C). Freshness measures how current or recent the data is, not how many nulls exist.
Official Snowflake documentation reference:
Snowflake documentation describes NULL_COUNT as a system Data Metric Function that returns the number of NULL values in a column.
Reference: Snowflake Documentation - Data Metric Functions; Snowflake Documentation - SNOWFLAKE.CORE.NULL_COUNT; SnowPro Core Study Guide - Data Protection and Governance.
==
NEW QUESTION # 111
Which common query problems are identified by the Query Profile? (Select TWO.)
Answer: A,B
Explanation:
The Query Profile in Snowflake can identify common query problems, including:
B . Inefficient pruning: This refers to the inability of a query to effectively limit the amount of data being scanned, potentially leading to suboptimal performance.
D . Queries too large to fit in memory: This indicates that a query requires more memory than is available in the virtual warehouse, which can lead to spilling to disk and degraded performance.
The Query Profile helps diagnose these issues by providing detailed execution statistics and visualizations, aiding in query optimization and troubleshooting.
References:
Snowflake Documentation: Query Profile
Top of Form
NEW QUESTION # 112
There are two Snowflake accounts in the same cloud provider region: one is production and the other is non-production. How can data be easily transferred from the production account to the non-production account?
Answer: B
Explanation:
To easily transfer data from a production account to a non-production account in Snowflake within the same cloud provider region, creating a data share is the most efficient approach. Data sharing allows for live, read-only access to selected data objects from the production account to the non-production account without the need to duplicate or move the actual data. This method facilitates seamless access to the data for development, testing, or analytics purposes in the non-production environment.
References:
Snowflake Documentation: Data Sharing
NEW QUESTION # 113
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