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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Virtual Warehouses | 15% | - Warehouse monitoring and troubleshooting - Cache concepts (warehouse, result, metadata) - Multi-cluster warehouses - Warehouse sizing and scaling - Warehouse creation and configuration |
| Databases, Tables & Views | 18% | - Clustering and micro-partitions - Stored procedures and UDFs - Table types (permanent, transient, temporary) - Data manipulation (DML operations) - Column types and sequences - Views (standard, materialized, secure) |
| Performance Optimization & Monitoring | 4% | - Query profiling and performance tuning - Resource optimization techniques - Query history and monitoring |
| Time Travel & Cloning | 10% | - Zero-copy cloning - Time travel concepts and retention - UNDROP and historical data access - Fail-safe concepts |
| Account Objects | 8% | - Organizations and accounts structure - Resource monitors - Warehouses, databases, and schemas hierarchy |
| Semi-Structured Data | 10% | - VARIANT data type - Array and object operations - Parsing and flattening semi-structured data |
| Security & Access Control | 10% | - Row-level and column-level security - Authentication (SSO, MFA) - Governance best practices - Masking policies - Authorization (roles, privileges) |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake editions and features - Virtual warehouse concepts and credit usage - Snowflake architecture layers (storage, compute, services) |
| Data Sharing | 12% | - Reader account management - Shares and reader accounts - Data listing marketplace - Private sharing vs. Data Exchange |
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NEW QUESTION # 1049
Where can file formats be specified when unloading data from Snowflake? Select TWO.
Answer: C,E
Explanation:
The correct answers are B. In a CREATE STAGE statement and E. In a COPY INTO < location > statement .
When unloading data from Snowflake, file format options can be specified directly in the COPY INTO < location > command or associated with a named stage. Defining the file format in a stage is useful when repeated loads or unloads use the same format settings.
Why B is correct:
A stage can include a file format definition or reference. When data is unloaded to that stage, the file format associated with the stage can be used.
Example:
CREATE STAGE my_stage
FILE_FORMAT = (TYPE = CSV FIELD_DELIMITER = ' , ' );
COPY INTO @my_stage
FROM my_table;
Why E is correct:
The COPY INTO < location > command can specify file format options directly.
Example:
COPY INTO @my_stage/output/
FROM my_table
FILE_FORMAT = (TYPE = CSV COMPRESSION = GZIP);
Why the other options are incorrect:
A). A SELECT statement defines the query result to unload, but file format options are not specified as part of a normal SELECT.
C). A database definition does not define unload file format options.
D). An integration defines access to external services or storage, not the file format used for unloading.
Official Snowflake documentation reference:
Snowflake documentation explains that file format options can be specified in the COPY INTO < location > command or as part of a named stage definition.
Reference: Snowflake Documentation - COPY INTO < location > ; Snowflake Documentation - CREATE STAGE; Snowflake Documentation - File formats; SnowPro Core Study Guide - Data Loading and Unloading.
NEW QUESTION # 1050
What is the default format-type COMPRESSION option when unloading CSV data from Snowflake using the COPY INTO < location > command?
Answer: C
Explanation:
The correct answer is A. GZIP .
When unloading data from Snowflake to staged files using COPY INTO < location > , the default compression for CSV output is GZIP unless a different compression option is specified.
Why A is correct:
For CSV unloads, Snowflake compresses output files using GZIP by default.
Example:
COPY INTO @my_stage/output/
FROM my_table
FILE_FORMAT = (TYPE = CSV);
If no compression is specified, the unloaded CSV files are compressed with GZIP by default.
Why the other options are incorrect:
B). AUTO is commonly used when loading data so Snowflake can automatically detect compression. It is not the default unload compression for CSV.
C). NONE disables compression, but it is not the default.
D). DEFLATE is a supported compression type, but it is not the default for CSV unload.
Official Snowflake documentation reference:
Snowflake documentation for CSV file format options explains that the default COMPRESSION value for unloading CSV data is GZIP.
Reference: Snowflake Documentation - COPY INTO < location > ; Snowflake Documentation - CSV file format options; SnowPro Core Study Guide - Data Loading and Unloading.
NEW QUESTION # 1051
The Query Profile shows the metrics of a query that ran on a size Large virtual warehouse.
What steps will improve the query performance? (Select TWO).
Answer: C,D
NEW QUESTION # 1052
Which query creates a JSON-style object with the keys name and age and the corresponding values Alice and
30?
Answer: A
Explanation:
OBJECT_CONSTRUCT creates a Snowflake OBJECT value from alternating key and value arguments. In option D, name is associated with Alice, and age is associated with the numeric value 30, producing a JSON-style object containing both key-value pairs. OBJECT_INSERT requires an existing OBJECT as its first argument and then adds or updates a key-value pair, so option C does not use the required syntax. TO_OBJECT converts a compatible existing value into an OBJECT; it does not accept an alternating list of keys and values in the form shown. Snowflake also supports object constants using curly braces, but OBJECT_CONSTRUCT is the established function-based answer provided by the choices. OBJECT values can be stored directly in OBJECT columns or inside VARIANT values for semi-structured processing.
NEW QUESTION # 1053
Which data types can be used in a Snowflake table that holds semi-structured data? (Select TWO).
Answer: D,E
Explanation:
Snowflake supports semi-structured data types that can store complex data structures within a single column. The VARIANT data type can hold structured and semi-structured data formats such as JSON, Avro, ORC, Parquet, and XML. The ARRAY data type is also used for semi-structured data and can store an ordered list of elements. These data types enable users to work with semi-structured data directly in Snowflake without needing to flatten the data into a relational schema first.References: Snowflake Documentation on Semi-Structured Data Types
NEW QUESTION # 1054
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