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Snowflake SOL-C01 Exam Syllabus Topics:
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NEW QUESTION # 90
A data scientist is working on a machine learning project using Snowflake Notebooks. They have a dataset stored in Snowflake and need to perform feature engineering. They want to write a Python function that takes a Snowflake table name and a list of SQL expressions as input, executes these expressions against the table, and returns a Pandas DataFrame containing the new features. Which approach is MOST suitable for creating and executing this function within a Snowflake Notebook, minimizing data transfer outside of Snowflake?
- A. Utilize the `sqlalchemy' library to establish a connection to Snowflake, construct the SQL query dynamically using SQLAlchemy's expression language, and fetch the results into a Pandas DataFrame.
- B. Create a Snowflake User-Defined Function (UDF) that encapsulates the feature engineering logic, and then call this UDF from the notebook using Snowpark or standard SQL.
- C. Use the Snowflake web UI to create a view containing the feature engineered data. Load that view into the notebook as a Pandas DataFrame.
- D. Use the `snowflake.connectors library to connect to Snowflake, execute each SQL expression using the method, and append the results to a Pandas DataFrame.
- E. Use the `snowflake.snowpark.functions.call_udf to call a UDF from Snowflake notebooks and create a dataframe.
Answer: B
Explanation:
Creating a UDF is the most efficient way to perform feature engineering because the computation happens within Snowflake's compute engine. Snowpark's or standard SQL can then be used to call and retrieve data from UDFs. This minimizes data transfer. Option A still fetches intermediate data. Options D create extra steps and extra objects. Option E isn't the standard approach when working with Snowpark in Notebooks.
NEW QUESTION # 91
You are working with a Snowflake table named 'transactions' that contains a 'transaction_time' column of data type 'TIMESTAMP NTZ'. You need to retrieve all transactions that occurred within the last 24 hours, and you want to optimize this query for performance. Which of the following approaches would be the MOST efficient?
- A. SELECT FROM transactions WHERE transaction_time SYSDATE() - (24/24);
- B. SELECT FROM transactions WHERE transaction_time CONVERT TIMEZONE('UTC',
'America/Los_Angeles', CURRENT TIMESTAMP()) - INTERVAL '24 hours'; - C. SELECT FROM transactions WHERE transaction_time DATEADD(hour, -24,
- D. SELECT FROM transactions WHERE transaction_time BETWEEN - INTERVAL '24 hours' AND
- E. SELECT FROM transactions WHERE transaction_time - INTERVAL '24 hours'
Answer: E
Explanation:
Option B is the most efficient and recommended approach. Using 'CURRENT TIMESTAMP() - INTERVAL '24 hours" directly leverages Snowflake's internal time handling and avoids unnecessary function calls. Option A will work, but 'DATEADD& function might be slower compared to subtracting the interval directly- Option C is less performant because BETWEEN requires calculation of both upper and lower bounds, adding unnecessary computatiom Option D includes timezone conversion, which introduces overhead and is not necessary if the
`transaction_time' is already in UTC or if the timezone doesn't matter. Option E, SYSDATE() - (24/24)' will produce same results since it gets the current timestamp and subtract one day's worth of data from it As the value is still relative to the timezone of the system, not UTC, and the subtraction might not be optimized for performance like 'INTERVAL' , making it less ideal.
NEW QUESTION # 92
You need to implement a data governance strategy that ensures data stewards can only access specific columns within a table to perform data quality checks. The table is `RAW DATA.PUBLIC.CUSTOMER DETAILS' and you want to allow the data stewards to only see the
'CUSTOMER ID', `EMAIL', and 'PHONE NUMBER columns. Which is the most efficient and secure method to grant access?
- A. Grant SELECT privilege on the table to the 'PUBLIC' role. Revoke SELECT privilege on the columns they should not see.
- B. Create a role hierarchy where a data steward role inherits from a higher-level role that has SELECT access on the entire table.
- C. Grant SELECT privilege on the table to a custom role used by the data stewards. Use masking policies to hide the columns they should not see.
- D. Create a secure view that only includes the `EMAIL ` , and 'PHONE_NUMBER columns. Grant SELECT privilege on the secure view to a custom role used by the data stewards.
- E. Create a standard view that only includes the 'CUSTOMER ID', 'EMAIL', and 'PHONE NUMBER columns.Grant SELECT privilege on the view to a custom role used by the data stewards.
Answer: D
Explanation:
Option B is the most secure and efficient. Secure views provide enhanced security compared to regular views, preventing potential information leakage through side-channel attacks. Secure views guarantee that data stewards only have access to the specified columns, enforcing the principle of least privilege. Masking policies (Option A) can introduce performance overhead.
Granting access to the 'PUBLIC' role (Option C) is generally not recommended for security reasons. Role hierarchy by inheriting privileges on the entire table breaks down security, as the user could switch to the parent role.
NEW QUESTION # 93
How can you query semi-structured data (e.g., JSON) stored in a Snowflake VARIANT column?
- A. By converting the VARIANT column to a relational table first.
- B. Using path notation to access specific elements within the VARIANT
- C. Only through stored procedures.
- D. Using standard SQL WHERE clauses on the entire VARIANT column.
Answer: B
Explanation:
Semi-structured data like JSON stored in a VARIANT column can be queried directly usingpath notation, either dot notation or bracket notation.
Examples:
* Dot notation:SELECT data:customer.name FROM table;
* Bracket notation:SELECT data['customer']['name'] FROM table;
Snowflake automatically interprets the JSON structure, making relational extraction unnecessary. Complex fields can be accessed through functions such as FLATTEN, OBJECT_KEYS, ARRAY_SIZE, and TYPEOF.
Incorrect options:
* Converting JSON to a relational table is optional, not required.
* Stored procedures are not necessary for JSON querying.
* WHERE on the full VARIANT cannot precisely extract fields.
Thus, path notation is the native, efficient method.
NEW QUESTION # 94
You are loading JSON data from an external stage into a Snowflake table called 'events'. The JSON files contain a top-level array of event objects. Some event objects are malformed and cause the 'COPY INTO' command to fail. You need to load as much valid data as possible while logging the errors caused by the malformed objects. Which TWO `COPY INTO' options, when used together, are MOST suitable for achieving this?
- A.
- B.
- C.
- D.
- E.
Answer: A,B
Explanation:
Using ERROR = CONTINUE' allows the COPY command to continue processing files even when encountering errors in individual records. 'VALIDATION_MODE = RETURN_ERRORS' captures the errors encountered during loading, allowing you to log and analyze them without stopping the load process. Skipping the entire file CSKIP_FILE) is less desirable than continuing to load valid data from the file. Purging the files has no effect on the load process or error handling. SON ERROR = ABORT_STATEMENT will abort the whole COPY operation
NEW QUESTION # 95
What is a "provider" in the context of the Snowflake Marketplace or Data Exchange?
- A. A user who manages Snowflake infrastructure
- B. A user who is a Snowflake employee
- C. A user who only consumes data
- D. A user or organization that shares data products or data listings
Answer: D
Explanation:
In Snowflake Marketplace and Data Exchange, aprovideris any Snowflake user or organization that publishes and shares datasets, data products, or applications with other Snowflake consumers. Providers control access to their listings, configure data sharing models, and can offer either free or paid data products. They manage listing descriptions, update schedules, and refresh logic, ensuring customers or subscribers receive up-to-date and reliable information. Providers can be enterprises, analytics vendors, public data contributors, or research institutions. They are distinct from consumers, who access or use the shared data. Providers also do not handle Snowflake platform infrastructure-that is Snowflake's responsibility. Snowflake employees are not categorized as providers unless they publish official Snowflake datasets. Thus, the primary purpose of a provider is to share curated data offerings on the Marketplace or within private exchanges.
NEW QUESTION # 96
A data engineer needs to grant SELECT privileges on all tables within a newly created schema named 'SALES DATA' to a role called 'ANALYST ROLE'. However, new tables may be added to this schema in the future. What is the most efficient and secure way to achieve this, ensuring that future tables automatically inherit the necessary permissions?
- A. Grant the USAGE privilege on the database containing the schema and SELECT privilege on
`FUTURE GRANTS for tables in 'SALES DATA' to the 'ANALYST ROLE using 'GRANT USAGE ON DATABASE TO ROLE ANALYST ROLE; GRANT SELECT ON FUTURE TABLES IN SCHEMA SALES DATA TO ROLE ANALYST ROLE;' - B. Use a stored procedure to automatically grant the SELECT privilege to 'ANALYST ROLE whenever a new table is created in the 'SALES DATA' schema.
- C. Create a custom role that inherits from 'ANALYST ROLE and grant 'SELECT privilege on all tables to this new role.
- D. Grant SELECT privilege to `ANALYST ROLE directly on each table using `GRANT SELECT ON TABLE ... TO ROLE ANALYST ROLE;' after table creation.
- E. Grant 'SELECT privilege to the PUBLIC role on all tables within the 'SALES_DATX schema.
Answer: A
Explanation:
Using GRANT SELECT ON FUTURE TABLES' ensures that any new tables created in the schema automatically inherit the SELECT privilege for the specified role. Option A requires manual intervention for each new table. Options C and E are unnecessarily complex. Option D poses a security risk by granting privileges to the PUBLIC role.
NEW QUESTION # 97
You are using the 'COPY INTO' command to load data from an Amazon S3 bucket into a Snowflake table named 'employees'. The data in S3 is in CSV format, compressed with gzip, and contains a header row. You want to skip the header row during the load and handle any data conversion errors by continuing the load operation. Which of the following 'COPY INTO' commands is most appropriate?
- A. Option C
- B. Option A
- C. Option E
- D. Option D
- E. Option B
Answer: B
Explanation:
Option A is the most appropriate. It correctly specifies the S3 bucket and file, CSV file format, skips the header row CSKIP_HEADER = and handles data conversion errors by continuing the load CON_ERROR = 'CONTINUE"). While other options might partially address the requirements, option A is complete and adheres to correct syntax. Option B uses 'SKIP_FILE' which would skip the whole file on error. Option C is closer, but adding `ERROR = FALSE is not directly related to handling data conversion errors. option D is incorrect because 'ON_ERROR = CONTINUE is not a valid syntax, it requires to be in single quotes 'ON ERROR = 'CONTINUE".
Option E is also good but 'FIELD DELIMITER = is not required here and is not adding value.
NEW QUESTION # 98
Where is unstructured data stored in Snowflake?
- A. In internal or external stages
- B. In the Cloud Services layer
- C. In external tables
- D. In tables with a single VARCHAR column
Answer: A
Explanation:
Unstructured data such as PDF files, images, and other binary documents is stored in stages in Snowflake.
These stages may be internal stages, which Snowflake manages directly, or external stages, which reference external cloud storage such as Amazon S3, Azure Blob Storage, or Google Cloud Storage. Stages are the designed mechanism for storing and accessing unstructured files so that they can be processed with functions like PARSE_DOCUMENT or accessed via directory tables.
External tables are used to query structured or semi-structured data (for example, Parquet or JSON) stored in external locations, not to store raw unstructured binary content. The Cloud Services layer coordinates metadata, security, and query services; it does not store user data. Tables with a single VARCHAR column might be used as an improvised approach for small text blobs, but this is not the native or recommended method for managing unstructured data at scale.
NEW QUESTION # 99
What is the purpose of using the SHOW GRANTS ON SQL command for a specific object?
- A. To list all roles in the account
- B. To list all privileges granted on the object
- C. To list all databases that have access to the object
- D. To list all users in the account
Answer: B
NEW QUESTION # 100
What options are available under the settings for Python worksheets? (Choose any 2 options)
- A. Handler
- B. Return Type
- C. Output
- D. Return Value
Answer: A,B
Explanation:
In Snowflake Python worksheets, settings include theHandlerandReturn Type. The Handler identifies the specific Python function that serves as the entry point for execution-essential when deploying code as stored procedures or user-defined functions. The Return Type defines how Snowflake interprets the output, such as STRING, VARIANT, or TABLE, ensuring proper result rendering. "Return Value" is not a worksheet setting but rather the outcome produced by the code itself, and "Output" is not a configuration option.
NEW QUESTION # 101
You are tasked with using the 'PARSE DOCUMENT' function in Snowflake to extract key information (name, address, phone number) from a large collection of scanned invoices stored as PDF files in an AWS S3 bucket. The invoices have varying formats and quality. Which of the following approaches would be MOST effective to structure the extracted data for analysis?
- A. Directly load PDF files into a relational table's TEXT column and write SQL queries utilizing LIKE and SUBSTRING functions to extract invoice details. Use 'MAX FILE SIZE-16777216' while loading pdf
- B. Create a custom UDF (User-Defined Function) that calls 'PARSE_DOCUMENT and then uses regular expressions or other text processing techniques to extract the desired information from the raw text output. Load the extracted data into a relational table.
- C. Use `PARSE DOCUMENT with a pre-defined JSON schema to enforce a rigid structure on the extracted data, and load the output directly into a relational table with predefined columns for name, address, and phone number.
- D. Use `PARSE DOCUMENT with default settings and load the raw JSON output into a VARIANT column in a Snowflake table. Implement custom SQL queries to extract the desired information from the VARIANT data.
- E. Employ a combination of 'PARSE DOCUMENT and Snowflake's external functions to integrate with a third-party OCR service for enhanced text extraction. Post-process the results using SQL to load structured data into a table. Use 'MAX FILE SIZE=16777216' while calling parse document
Answer: B
Explanation:
Option C provides the most robust and flexible approach. Given the varying formats and quality of the invoices, a pre-defined JSON schema (option B) is unlikely to work effectively. Loading raw JSON into a VARIANT column (option A) requires extensive post-processing. Option D, while potentially effective, introduces the complexity and cost of a third-party OCR service. And MAX_FILE_SIZE parameter controls the maximum size, in bytes, of a single uncompressed file that can be loaded from the stage. Option E is not a scalable and efficient approach.
NEW QUESTION # 102
You're using Snowflake Cortex's CLASSIFY TEXT function to analyze customer survey responses. A particular survey question asks respondents to list their favorite features of a product. You want to use CLASSIFY TEXT to categorize the responses, but many responses contain multiple distinct features. How can you best leverage CLASSIFY TEXT to classify each feature mentioned in a single response individually?
- A. Use a Python UDF that leverages an NLP library (e.g., spaCy) to extract individual features from each response, and then apply CLASSIFY TEXT to each extracted feature.
- B. Apply CLASSIFY _ TEXT directly to each full survey response. The function is designed to handle multiple features within a single text and return a combined classification.
- C. Concatenate all survey responses into a single large string and apply CLASSIFY_TEXT to this combined string. The function will automatically identify and classify individual features within the larger text.
- D. Use a regular expression UDF to split each survey response into individual sentences (assuming each sentence discusses one feature). Then apply CLASSIFY TEXT to each sentence.
- E. There is no way to classify multiple features within a single response using CLASSIFY TEXT. The function is designed for single-topic classification only.
Answer: A
Explanation:
Option E is the most effective approach. Using a Python UDF with NLP libraries like spacy allows for accurate extraction of individual features, enabling CLASSIFY TEXT to categorize each one separately. Option A might work in some cases but is unreliable as not all features are discussed in separate sentences. Option B is incorrect because CLASSIFY _ TEXT is meant to classify the sentiment behind the text and not extract features from it. Option C isn't best because the function isn't made to determine multiple classifications for multiple topics. Option D isn't correct because if the features can be extracted, the TEXT_CLASSIFY function can determine categories.
NEW QUESTION # 103
What are the steps to create a new stage in Snowsight?
- A. Data >> add data >> Create Schema
- B. Data Product >> Create Schema
- C. Data >> Database >> Schema >> Create Stage
- D. Data >> Create Schema
Answer: C
Explanation:
To create a new stage in Snowsight, users navigate through:Data # Database # Schema # Create Stage. Within the schema, selecting the "+" icon opens the option to create a stage. Users may configure internal or external stages, encryption settings, and directory table options. Other paths listed reference creating schemas rather than creating stages and are therefore incorrect.
NEW QUESTION # 104
You have a Snowflake environment with several databases and schemas. You need to identify all schemas within a specific database named 'CUSTOMER DATA that are currently owned by the role 'SECURITY ADMIN'. You also want to retrieve the creation date and time of each identified schema. Which of the following queries will efficiently accomplish this task?
- A.
- B.
- C.
- D.
- E.
Answer: E
Explanation:
The `snowflake.account_usage.schemas' view provides metadata about schemas, including the database name, schema name, creation date, and owner role. The correct column name for the owner role is 'owner_role' and not just `owner". 'database_name' and `name' are also correct column names. option B and D uses 'SCHEMA OWNER from 'INFORMATION_SCHEMX , and
'SCHEMA_OWNER is deprecated and less reliable for role ownership. It's important to use the
`account_usage' views for comprehensive and accurate metadata retrieval. Option A requires parsing the output of 'SHOW SCHEMAS", which is less efficient than a direct query.
NEW QUESTION # 105
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