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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q156-Q161):

NEW QUESTION # 156
You are developing a data pipeline that extracts data from an on-premise PostgreSQL database, transforms it, and loads it into Snowflake. You want to use the Snowflake Python connector in conjunction with a secure method for accessing the PostgreSQL database. Which of the following approaches provides the MOST secure and manageable way to handle the PostgreSQL connection credentials in your Python script when deploying to a production environment?

A. Prompt the user for the PostgreSQL username and password each time the script is executed.
B. Store the PostgreSQL username and password in a dedicated secrets management service (e.g., AWS Secrets Manager, HashiCorp Vault, Azure Key Vault) and retrieve them in the Python script using the appropriate API.
C. Hardcode the PostgreSQL username and password directly into the Python script.
D. Store the PostgreSQL username and password in environment variables and retrieve them in the Python script using 'os.environ'
E. Store the PostgreSQL username and password in a configuration file (e.g., JSON or YAML) and load the file in the Python script.

Answer: B

Explanation:
Option D, using a dedicated secrets management service, provides the most secure and manageable approach. Secrets management services are designed to securely store and manage sensitive information like database credentials. They offer features like encryption, access control, auditing, and versioning, making them the best choice for production environments. Option A is highly insecure. Options B and C are better than A but still less secure than using a secrets management service, as environment variables and configuration files can be accidentally exposed or committed to version control. Option E is impractical and insecure for automated pipelines.

NEW QUESTION # 157
You have a Snowflake Stream named 'PRODUCT CHANGES' created on a table 'PRODUCTS'. A downstream task attempts to consume records from the stream, but occasionally fails with a 'Table PRODUCTS has been altered' error. The 'PRODUCTS' table undergoes DDL changes (e.g., adding/dropping columns) infrequently, but these changes are necessary for evolving business requirements. How can you design a more resilient data pipeline that minimizes disruptions caused by DDL changes to the 'PRODUCTS' table while still leveraging the 'PRODUCT CHANGES' stream?

A. Use a materialized view instead of a standard view as the source for the stream. Materialized views are less susceptible to issues when the underlying base table changes
B. Implement error handling in the downstream task to automatically retry consuming records from the 'PRODUCT CHANGES' stream after a delay, assuming the DDL changes will be completed quickly.
C. Create a task that monitors the 'PRODUCTS' table for DDL changes using 'INFORMATION SCHEMA. TABLES'. When a change is detected, pause the downstream task, execute the DDL change, and then resume the downstream task after a short delay.
D. Before executing any DDL changes on the 'PRODUCTS' table, drop and recreate the 'PRODUCT CHANGES' stream. This ensures the stream definition is always in sync with the table structure.
E. Create a new Stream on the 'PRODUCTS' table after each DDL change. The downstream task should dynamically switch to consuming from the new stream when the old stream encounters an error.

Answer: B

Explanation:
Option B (implementing retry logic) is the most practical and resilient approach. While DDL changes can temporarily disrupt the stream, retrying the task allows it to resume processing once the DDL changes are complete. Dropping and recreating the stream (Option A) would lose valuable change data. Creating a new stream (Option C) introduces complexity and requires significant code changes. Materialized views are not directly relevant to the stream issue when the underlying table changes. Pausing the task (Option E) might not be feasible in a production environment and doesn't guarantee data consistency during DDL changes. Streams are designed to continue working, albeit potentially with errors, after minor DDL changes to the underlying table, and a retry mechanism handles these temporary disruptions best.

NEW QUESTION # 158
You are designing a data pipeline that involves unloading large amounts of data (hundreds of terabytes) from Snowflake to AWS S3 for archival purposes. To optimize cost and performance, which of the following strategies should you consider? (Select ALL that apply)

A. Use a large Snowflake warehouse size to parallelize the unload operation and reduce the overall unload time.
B. Enable client-side encryption with KMS in S3 and specify the encryption key in the 'COPY INTO' command to enhance security.
C. Utilize the 'MAX FILE SIZE parameter in the 'COPY INTO' command to control the size of individual files unloaded to S3. Smaller files generally improve query performance in S3.
D. Partition the data during the unload operation based on a high-cardinality column to maximize parallelism in S3.
E. Choose a file format such as Parquet or ORC with compression enabled to reduce storage costs and improve query performance in S3.

Answer: A,B,E

Explanation:
Using a larger warehouse size allows Snowflake to parallelize the unload operation, reducing the time it takes to unload large datasets. Enabling client-side encryption with KMS ensures that the data is encrypted both in transit and at rest in S3, enhancing security. Choosing a columnar file format like Parquet or ORC with compression significantly reduces storage costs and improves query performance when the data is later accessed in S3. Partitioning based on a high-cardinality column can lead to a large number of small files, which can negatively impact query performance in S3. While 'MAX FILE_SIZE is useful, smaller files don't always improve query performance and can even be detrimental.

NEW QUESTION # 159
You have a Snowpark DataFrame 'df_products' with columns 'product id', 'category', and 'price'. You need to perform the following transformations in a single, optimized query using Snowpark Python: 1. Filter for products in the 'Electronics' or 'Clothing' categories. 2. Group the filtered data by category. 3. Calculate the average price for each category. 4. Rename the aggregated column to 'average_price'. Which of the following code snippets demonstrates the most efficient way to achieve this?

A. Option A
B. Option D
C. Option E
D. Option B
E. Option C

Answer: D

Explanation:
Option B is the most efficient and correct. It uses 'col()' from 'snowflake.snowpark.functions' to properly reference the 'category' and 'price' columns, uses 'isin()' for a more concise and efficient filtering of multiple category values, groups by the category using and calculates the average price with 'avg(col('price')).as_('average_price')'. Option A, C, and D are syntactically incorrect or less efficient ways to accomplish the same task within Snowpark. Option E is incorrect because it utilizes 'to_pandas()' which returns the result as a Pandas DataFrame rather than a Snowpark DataFrame, failing to adhere to the Snowpark environment. While Option D is very similar, it lacks the proper syntax for specifying column references with 'col('category')' in the groupBy and 'col('price')' in the avg function.

NEW QUESTION # 160
You have a directory table 'my_directory_table' pointing to a stage containing CSV files with headers. You need to query the directory table to find all files modified in the last 24 hours and load those CSV files using COPY INTO into a target table Assume the target table exists and has appropriate schema'. Which of the following SQL statements, or set of statements, will accomplish this efficiently? Note: Consider efficient file loading.

Answer: A

Explanation:
Option E is the most efficient way to load the files using 'COPY INTO' and the 'FILES' parameter. The 'FILES' parameter accepts a list of filenames to load. Option D is incorrect because the 'PATTERN' parameter expects a regular expression, not a concatenation of filenames. Options A, B, and C require more complex processing. Using subquery expression in the copy command is the best approach for filtering files based on directory table data.

NEW QUESTION # 161
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