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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q126-Q131):NEW QUESTION # 126
You are tasked with building a data pipeline that ingests data from various sources into Snowflake, processes it, and then writes the final results back to a data lake in AWS S3, partitioned by date. The data in S3 should be queryable by other applications outside of Snowflake. You choose to use Snowflake Iceberg tables for this purpose. Which of the following is the correct SQL statement to create an Iceberg table 'analytics.public.daily_summary' in Snowflake, backed by an S3 bucket 's3://your-bucket/data/daily_summary/', partitioned by the column, and specifying 'parquet' as the file format?
- A. Option E
- B. Option C
- C. Option B
- D. Option D
- E. Option A
Answer: A
Explanation:
The correct syntax for creating an Iceberg table in Snowflake backed by an external location involves using 'USING ICEBERG' and 'EXTERNAL_LOCATION'. 'LOCATION' is used for standard external tables, not Iceberg tables. The 'DATA_SOURCE parameter is not valid in this context. The syntax is specifically designed for creating Iceberg tables and correctly utilizes EXTERNAL_LOCATION to point to the S3 bucket. Note that Iceberg tables requires EXTERNAL_LOCATION rather than LOCATION.
NEW QUESTION # 127
You have created a Snowflake Iceberg table that points to data in an AWS S3 bucket. After some initial data ingestion, you realize that the schema in the Iceberg table does not perfectly match the schema of the underlying Parquet files in S3. Specifically, one of the columns in the Iceberg table is defined as 'VARCHAR , while the corresponding column in the Parquet files is stored as 'INT. What will be the most likely behavior when you query this Iceberg table in Snowflake?
- A. Snowflake will attempt to cast the data, and if a cast fails (e.g., 'INT' value is too large to fit in 'VARCHAR), the query will return an error only for those specific rows. Other rows will be processed correctly.
- B. The query will fail with an error indicating a data type mismatch between the Iceberg table schema and the underlying Parquet file schema.
- C. The query will succeed, but the 'VARCHAR column will contain 'NULL' values for all rows where the underlying Parquet files contain 'INT' values.
- D. Snowflake will automatically cast the SINT' data in the Parquet files to 'VARCHAR during query execution, and the query will succeed without any errors or warnings.
- E. The query will succeed, but the result will be unpredictable and may vary depending on the specific data values in the Parquet files.
Answer: B
Explanation:
Snowflake enforces schema validation for Iceberg tables. If the data types in the Iceberg table schema do not match the data types in the underlying Parquet files, the query will fail with an error. This is because Snowflake relies on the Iceberg metadata to understand the data types and structure of the data in the Parquet files. A mismatch indicates a problem with the Iceberg table definition or the underlying data and should be corrected to ensure data integrity. While Snowflake is often flexible with implicit casting, in the context of Iceberg tables and schema enforcement, a type mismatch will lead to a query failure.
NEW QUESTION # 128
You have a data pipeline that loads data from an internal stage into a Snowflake table Craw_data'). The pipeline is experiencing intermittent failures with the error 'SQL compilation error: Stage 'MY INTERNAL STAGE' is immutable'. What are the potential causes of this error and how would you troubleshoot it?
- A. The internal stage is being used by multiple COPY INTO commands simultaneously, causing a resource contention issue. Implement queuing or throttling mechanisms to manage concurrent data loading.
- B. The user executing the COPY INTO command lacks the necessary privileges (USAGE on the stage). Grant the appropriate privileges to the user or role.
- C. This error is caused by insufficient warehouse size. Increase the warehouse size to accommodate the COPY INTO operation.
- D. Another concurrent process is attempting to drop or alter the internal stage while the COPY INTO command is running. Implement proper locking mechanisms to prevent concurrent modifications.
- E. The internal stage has been accidentally dropped and recreated with the same name during the COPY operation. Verify the stage's existence and creation timestamp.
Answer: D,E
Explanation:
The 'Stage is immutable' error typically indicates that the stage's definition has changed during the COPY operation. This can happen if the stage is dropped and recreated (option A) or if another process is altering the stage concurrently (option C). Privilege issues (option B) would usually result in a different error message. Resource contention (option D) is less likely to cause this specific error but could impact performance. Warehouse size (option E) is generally not directly related to this error.
NEW QUESTION # 129
You are tasked with creating an external function in Snowflake that calls a REST API. The API requires a bearer token for authentication, and the function needs to handle potential network errors and API rate limiting. Which of the following code snippets demonstrates the BEST practices for defining and securing this external function, including error handling?
- A. Option E
- B. Option C
- C. Option B
- D. Option D
- E. Option A
Answer: A
Explanation:
Option A uses SECURITY_INTEGRATION, which is suitable for cloud provider-managed security but doesn't directly handle the API key. Option B uses CREDENTIAL, which is deprecated. Option C and D use AUTH POLICY and SECRET, but C doesn't use SYSTEM$GET_SECRET within a 'USING' clause or CONTEXT_HEADERS. Option D uses the 'USING' clause but does not use 'CONTEXT HEADERS to pass the token correctly. Option E is the BEST approach because it utilizes 'SECURITY INTEGRATION' along with 'CONTEXT_HEADERS' to pass the Bearer token securely retrieved from the Snowflake secret, ensuring proper authentication. Using CONTEXT HEADERS allows setting the authorization header directly. Also, its importand to create the 'SECRET api_secret' for this code to work correctly and this options uses it.
NEW QUESTION # 130
A Snowflake data warehouse contains a table named 'SALES TRANSACTIONS' with the following columns: 'TRANSACTION ID', 'PRODUCT D', 'CUSTOMER D', 'TRANSACTION DATE, and 'SALES AMOUNT'. You need to optimize a query that calculates the total sales amount per product for a given month. The 'SALES TRANSACTIONS' table is very large (billions of rows), and queries are slow. Given the following initial query: SELECT PRODUCT ID, SUM(SALES AMOUNT) AS TOTAL SALES FROM SALES TRANSACTIONS WHERE TRANSACTION DATE BETWEEN '2023-01-07' AND '2023-01-31' GäOUP BY PRODUCT ID; Which of the following actions, when combined, would MOST effectively improve the performance of this query?
- A. Create a temporary table with the results of the query and query that table instead.
- B. Create a materialized view that pre-aggregates the total sales amount per product and month.
- C. Increase the virtual warehouse size to the largest available size.
- D. Convert the column to a VARCHAR data type.
- E. Create a clustering key on 'PRODUCT_ID and 'TRANSACTION_DATE columns in the 'SALES_TRANSACTIONS' table.
Answer: B,E
Explanation:
Creating a clustering key on 'PRODUCT ID and 'TRANSACTION DATE' allows Snowflake to efficiently prune micro-partitions based on the date range filter, and then quickly group by "PRODUCT_ID. A materialized view pre-aggregates the data, significantly reducing the amount of computation required at query time. While increasing the warehouse size might provide some improvement, it is not the most efficient solution. Converting 'TRANSACTION_DATE to VARCHAR is detrimental. Using a temporary table is not necessarily an optimization.
NEW QUESTION # 131
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