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| Section | Weight | Objectives |
|---|---|---|
| Data Collaboration & Protection | 10% | - Data Clean Rooms, Collaboration Features - Data Protection, Retention, Time Travel, Fail-safe - Secure Data Sharing, Listings, Marketplace |
| Account Management & Data Governance | 20% | - Access Control: RBAC, DAC, Roles, Privileges - Security: Network Policies, MFA, Encryption - Trust Center, Data Lineage, Compliance - Governance: Resource Monitors, Data Masking, Row/Column Security |
| Snowflake Architecture & Core Concepts | 31% | - Snowsight Interface, Core Platform Features - Databases, Schemas, Objects, Data Types - Cloud Services Layer, Compute Layer, Storage Layer - Virtual Warehouses, Clustering, Caching |
| Performance Optimization, Querying & Transformation | 21% | - Query Processing, Profiling, Optimization - SQL Transformations, Semi-structured/Unstructured Data - Materialized Views, Streams, Tasks, Pipelines - Cortex AI, Notebooks, Apache Iceberg Tables |
| Data Loading, Unloading & Connectivity | 18% | - Stages, File Formats, COPY INTO, Snowpipe - Bulk Load, Streaming, Dynamic Tables - Unloading Data, Export Options - External Integrations, Connectors, SnowSQL |
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NEW QUESTION # 244
The bulk data load history that is available upon completion of the COPY statement is stored where and for how long?
Answer: B
Explanation:
The bulk data load history available after a COPY statement is stored in the metadata of the pipe and is retained for 64 days1.
NEW QUESTION # 245
Which function should be used to insert JSON format string data inot a VARIANT field?
Answer: C
Explanation:
To insert JSON formatted string data into a VARIANT field in Snowflake, the correct function to use is PARSE_JSON. The PARSE_JSON function is specifically designed to interpret a JSON formatted string and convert it into a VARIANT type, which is Snowflake's flexible format for handling semi-structured data like JSON, XML, and Avro. This function is essential for loading and querying JSON data within Snowflake, allowing users to store and manage JSON data efficiently while preserving its structure for querying purposes. This function's usage and capabilities are detailed in the Snowflake documentation, providing users with guidance on how to handle semi-structured data effectively within their Snowflake environments.
References:
Snowflake Documentation: PARSE_JSON
NEW QUESTION # 246
Which Query Profile operator is considered a DML operator?
Answer: C
Explanation:
The Merge operator is considered a DML (Data Manipulation Language) operator in Snowflake. DML operators are used to modify data in tables, and the Merge operator specifically allows for conditional updates, inserts, or deletes in a target table based on the results of a join with a source table.
References:
Snowflake Documentation: Query Profile Overview
Snowflake Documentation: MERGE Statement
NEW QUESTION # 247
What do the terms scale up and scale out refer to in Snowflake? (Choose two.)
Answer: D,F
Explanation:
Scaling out in Snowflake involves adding clusters of the same size to a virtual warehouse, which allows for handling more concurrent queries without affecting the performance of individual queries. Scaling up refers to resizing a virtual warehouse to increase its compute resources, enabling it to handle more complex workloads and larger queries more efficiently.
NEW QUESTION # 248
A user needs to load Snowflake files from a stage that contains data from the past 5 years while using the least amount of operational overhead. The file names follow a specific format, such as files ending in _01.gz.
Which command should be used to load the data from 2024?
Answer: D
Explanation:
The correct answer is B. Use the COPY INTO command using the PATTERN parameter to define the needed files .
The PATTERN parameter in the COPY INTO < table > command allows Snowflake to load only files whose names match a specified regular expression. This is useful when a stage contains many files and the required files can be identified by a naming pattern, such as year or suffix.
Why B is correct:
The user needs to load a subset of staged files from a 5-year collection and wants the least operational overhead. Using PATTERN avoids manually listing files, removing files, or downloading and restaging data.
Example concept:
COPY INTO target_table
FROM @my_stage
PATTERN = ' .*2024.*_01[.]gz ' ;
Why the other options are incorrect:
A). Individually loading files creates more manual work and higher operational overhead.
C). Removing unwanted files is risky and unnecessary. It also changes the staged file set.
D). Downloading files with GET, creating a new stage, and reloading files adds unnecessary operational complexity.
Official Snowflake documentation reference:
Snowflake documentation explains that the PATTERN option in COPY INTO < table > specifies a regular expression pattern used to match staged file names for loading.
Reference: Snowflake Documentation - COPY INTO < table > ; Snowflake Documentation - PATTERN copy option; SnowPro Core Study Guide - Data Loading and Unloading.
NEW QUESTION # 249
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