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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions:

1. Consider the following Snowflake Javascript UDF designed to convert temperatures between Celsius and Fahrenheit:

Which statement regarding the UDF's security and behavior is MOST accurate?

A) The UDF is vulnerable to SQL injection because the 'unit' parameter is not validated against a predefined list of acceptable values.
B) The UDF will always return a value, even if the input 'unit is invalid, due to the implicit type conversion in JavaScript.
C) While the ' SECURE keyword hides the UDF's source code from users without sufficient privileges, users with the ACCOUNTADMIN role can still view the source code.
D) The 'SECURE' keyword ensures that the UDF's code is completely hidden from all users, including those with the ACCOUNTADMIN role.
E) The UDF is not vulnerable to SQL injection because Javascript UDFs in Snowflake are sandboxed and prevent direct SQL execution.

2. You are loading data from an S3 bucket into a Snowflake table using the COPY INTO command. The source data contains dates in various formats (e.g., 'YYYY-MM-DD', 'MM/DD/YYYY', 'DD-Mon-YYYY'). You want to ensure that all dates are loaded correctly and consistently into a DATE column in Snowflake. Which of the following COPY INTO options and commands is the MOST appropriate to handle this?

A) Use the 'VALIDATE(O)' command before the COPY INTO command to identify files with invalid date formats and then process them separately.
B) Utilize the 'DATE' function with explicit format strings inside a Snowpipe transformation pipeline. This involves pattern matching using 'CASE WHEN' statements to identify date formats before converting to the DATE data type.
C) Use the 'STRTOK TO DATE function within a SELECT statement in a Snowpipe transformation to dynamically parse the dates based on different patterns.
D) Use the 'ON_ERROR = 'SKIP FILE" option to skip files with invalid date formats.
E) Use the 'DATE FORMAT option in the COPY INTO command with a single format string that covers all possible date formats.

3. You are tasked with loading a large dataset (50TB) of JSON files into Snowflake. The JSON files are complex, deeply nested, and irregularly structured. You want to maximize loading performance while minimizing storage costs and ensuring data integrity. You have a dedicated Snowflake virtual warehouse (X-Large).
Which combination of approaches would be MOST effective?

A) Load the JSON data using the COPY INTO command with no pre-processing. Create a VIEW on top of the raw VARIANT column to flatten the data for querying.
B) Use Snowpipe with auto-ingest, create a single VARIANT column in your target table, and rely solely on Snowflake's automatic schema detection.
C) Use Snowpipe with auto-ingest, create a raw VARIANT column alongside projected relational columns for frequently accessed fields, and use search optimization on those projected columns.
D) Load the JSON data using the COPY INTO command with gzip compression. Create a raw VARIANT column alongside projected relational columns for frequently accessed fields, and use materialized views to improve query performance.
E) Pre-process the JSON data using a Python script with Pandas to flatten the structure and convert it into a relational format like CSV. Then, load the CSV files using the COPY INTO command with gzip compression.

4. A data engineering team is building a data pipeline in Snowflake. They are using tasks and streams to incrementally load data into a fact table. The team needs to monitor the pipeline's performance and ensure data lineage. What are the valid and most effective techniques to ensure that this pipeline adheres to compliance and governance rules?

A) Leverage Snowflake's replication features for disaster recovery, monitor only the replication lag, and disable all security policies to improve performance since those tasks have already been validated during the initial deployment of the software.
B) Use Account Usage views like 'TASK HISTORY and 'STREAM_LAG' to track task execution and stream latency, create stored procedures to log metadata about each pipeline run to a separate metadata table, and rely on developers to manually document the pipeline's data flow and policy enforcement.
C) Use a third-party data catalog to track lineage, monitor task performance via 'TASK_HISTORY, and ignore data masking and row-level security policies for simplicity in the initial implementation.
D) Implement Snowflake's Data Lineage and Object Dependencies features to track data flow automatically, create Alerts based on 'TASK HISTORY to monitor task failures, and enforce data masking and row-level security policies at the table level. Use Snowflake's tags to categorise and classify objects.
E) Enable Snowflake Horizon features, which include Data Lineage, Object Dependencies and Discovery and integrate it with the data lake and also tag the data pipeline.

5. You are tasked with building a data pipeline that ingests JSON data from a series of publically accessible URLs. These URLs are provided as a list within a Snowflake table 'metadata_table', containing columns 'file_name' and 'file url'. Each JSON file contains information about products. You need to create a view that extracts product name, price, and a flag indicating whether the product description contains the word 'discount'. Which of the following approaches correctly implements this, optimizing for both performance and minimal code duplication, using external functions for text processing?

A) Create an external function that takes a URL as input and returns a BOOLEAN indicating if any error occured while processing the URL and the data. Create a stored procedure that iterates through 'metadata_table' , calls external function for each URL, reports error and then processes the data. A stage must also be created to host external function code.
B) Create a stored procedure that iterates through 'metadata_table', downloads each JSON file using 'SYSTEM$URL GET, parses the JSON, extracts the required fields, and inserts the data into a target table. Then, create a view on top of the target table. Use 'LIKE '%discount%' to identify if a product description contains the word 'discount'.
C) Create an external function that takes a URL as input and returns a JSON variant containing the extracted product name, price, and discount flag (using 'LIKE Then, create a view that selects from calls the external function with 'SYSTEM$URL as input, and extracts the desired attributes from the returned JSON variant. A stage must also be created to host external function code.
D) Create a pipe using 'COPY INTO' statement with 'FILE FORMAT = (TYPE = JSON)' and 'ON_ERROR = CONTINUE that loads the JSON files directly into a staging table. Create a view on top of the staging table to extract the required fields. The must have = TRUE' configured if JSON files are nested array. Use ' ILIKE in your view for the discount flag.
E) Create an external function that takes a string as input and returns a BOOLEAN whether that string contains 'discount. Create a view on top of metadata_table', and using 'SYSTEM$URL_GET' fetch the content from 'file_url'. The JSON can then be parsed and the fields like price, name and description can be fetched. Use within the view to flag the presence of discount.

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