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| Section | Weight | Objectives |
|---|---|---|
| Data Sharing | 12% | - Shares and reader accounts - Data listing marketplace - Private sharing vs. Data Exchange - Reader account management |
| Databases, Tables & Views | 18% | - Views (standard, materialized, secure) - Column types and sequences - Clustering and micro-partitions - Stored procedures and UDFs - Table types (permanent, transient, temporary) - Data manipulation (DML operations) |
| Account Objects | 8% | - Organizations and accounts structure - Warehouses, databases, and schemas hierarchy - Resource monitors |
| Semi-Structured Data | 10% | - Parsing and flattening semi-structured data - VARIANT data type - Array and object operations |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake architecture layers (storage, compute, services) - Snowflake editions and features - Virtual warehouse concepts and credit usage |
| Virtual Warehouses | 15% | - Warehouse monitoring and troubleshooting - Cache concepts (warehouse, result, metadata) - Multi-cluster warehouses - Warehouse sizing and scaling - Warehouse creation and configuration |
| Performance Optimization & Monitoring | 4% | - Resource optimization techniques - Query history and monitoring - Query profiling and performance tuning |
| Time Travel & Cloning | 10% | - Time travel concepts and retention - Fail-safe concepts - Zero-copy cloning - UNDROP and historical data access |
| Data Loading & Unloading | 15% | - Bulk loading with COPY command - Data ingestion best practices - Continuous loading with Snowpipe - Data unloading and staging - File formats (JSON, CSV, Parquet, AVRO) |
| Security & Access Control | 10% | - Authorization (roles, privileges) - Row-level and column-level security - Governance best practices - Masking policies - Authentication (SSO, MFA) |
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NEW QUESTION # 336
A user has 10 files in a stage containing new customer data. The ingest operation completes with no errors, using the following command:
COPY INTO my__table FROM @my__stage;
The next day the user adds 10 files to the stage so that now the stage contains a mixture of new customer data and updates to the previous data. The user did not remove the 10 original files.
If the user runs the same copy into command what will happen?
Answer: C
Explanation:
When the COPY INTO command is executed in Snowflake, it processes all files present in the specified stage that have not been ingested before or marked as already loaded. Since the user did not remove the original 10 files after the first load, running the same COPY INTO command again will result in all 20 files being processed. This means that the data from the original 10 files will be appended to the table again, along with the data from the new 10 files, potentially leading to duplicate records for the original data set.
References:
Snowflake Documentation on Data Loading
SnowPro Core Certification Study Guide
NEW QUESTION # 337
Which Snowflake data governance feature can support auditing when a user query reads column data?
Answer: D
Explanation:
Access History in Snowflake is a feature designed to support auditing by tracking access to data within Snowflake, including when a user ' s query reads column data. It provides detailed information on queries executed, including the user who ran the query, the query text, and the objects (e.g., tables, views) accessed by the query. This feature is instrumental for auditing purposes, helping organizations to monitor and audit data access for security and compliance.
Reference: Snowflake Documentation on Access History which details its use for auditing access to data:
https://docs.snowflake.com/en/user-guide/access-history.html
NEW QUESTION # 338
What will happen if multiple identical COPY INTO < table > statements, using the default settings, are executed in parallel on the same data file?
Answer: D
Explanation:
The correct answer is A. The statements will load the data only once in the table .
By default, Snowflake tracks metadata for files that have already been loaded into a table. This load metadata helps prevent the same staged file from being loaded again into the same table when using the default COPY INTO < table > behavior.
Why A is correct:
Snowflake's default copy behavior prevents duplicate loading of the same file into the same table. Even if multiple identical COPY INTO statements run against the same file, Snowflake uses load metadata to avoid loading the same file multiple times.
Important default behavior:
The default value of FORCE is FALSE. This means Snowflake does not reload files that have already been loaded successfully into the table.
Why the other options are incorrect:
B). Data is not duplicated under the default settings because Snowflake tracks loaded files.
C). Running parallel copy statements does not automatically fail just because they reference the same file.
D). COPY INTO < table > does not overwrite table data by default. It appends loaded rows, but duplicate file loading is prevented by load metadata.
Official Snowflake documentation reference:
Snowflake documentation explains that Snowflake maintains load metadata for tables and prevents reloading the same files by default unless FORCE = TRUE is specified.
Reference: Snowflake Documentation - COPY INTO < table > ; Snowflake Documentation - Load metadata; Snowflake Documentation - FORCE copy option; SnowPro Core Study Guide - Data Loading and Unloading.
NEW QUESTION # 339
How can staged files be removed during data loading once the files have loaded successfully?
Answer: A
Explanation:
To remove staged files during data loading after they have been successfully loaded, the PURGE copy option is used in Snowflake.
PURGE Option: This option automatically deletes files from the stage after they have been successfully copied into the target table.
Usage:
FROM @my_stage
FILE_FORMAT = (type = 'csv')
PURGE = TRUE;
References:
Snowflake Documentation on COPY INTO
NEW QUESTION # 340
A column named " Data " contains VARIANT data and stores values as follows:
How will Snowflake extract the employee ' s name from the column data?
Answer: C
Explanation:
In Snowflake, to extract a specific value from a VARIANT column, you use the column name followed by a colon and then the key. The keys are case-sensitive. Therefore, to extract the employee's name from the
"Data" column, the correct syntax is data:employee.name.
NEW QUESTION # 341
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