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NEW QUESTION # 216
You are developing a data pipeline that uses Snowpipe Streaming to ingest JSON data into a Snowflake table. Some JSON documents contain nested arrays and complex structures. You need to flatten the JSON structure during ingestion to simplify querying. Consider the following JSON document: { "order id": 123, "customer": { "id": "cust123", "name": "John Doe", "address": { "street": "123 Main St", "city": "Anytown" } }, "items": [ {"product_id": "prodl", "quantity": 2}, {"product_id": "prod2", "quantity": 1} ] } Which approach would you use within the 'COPY INTO' statement of your Snowpipe to flatten this JSON structure during ingestion?
Answer: B
Explanation:
Snowflake's 'FLATTEN()' function, combined with dot notation for nested objects, provides the most efficient way to flatten JSON data during ingestion within the 'COPY INTO' statement. Options B, C, and D introduce unnecessary complexity and latency. Snowpipe does NOT automatically flatten JSON (E).
NEW QUESTION # 217
A data engineering team is responsible for an ELT pipeline that loads data into Snowflake. The pipeline has two distinct stages: a high- volume, low-complexity transformation stage using SQL on raw data, and a low-volume, high-complexity transformation stage using Python UDFs that leverages an external service for data enrichment. The team is experiencing significant queueing during peak hours, particularly impacting the high-volume stage. You need to optimize warehouse configuration to minimize queueing. Which combination of actions would be MOST effective?
Answer: E
Explanation:
Creating separate warehouses allows for independent scaling and resource allocation based on workload characteristics. Using a larger, multi-cluster warehouse with auto-scale for the high-volume stage ensures that sufficient resources are available to handle the load without queueing. A smaller warehouse is sufficient for the low-volume, high-complexity transformations. Options A, D and E are incorrect as they do not appropriately separate and size warehouses according to the workload profile. Option B sizes warehouses incorrectly.
NEW QUESTION # 218
You are configuring a Snowflake Data Clean Room for two healthcare providers, 'ProviderA' and 'ProviderB', to analyze patient overlap without revealing Personally Identifiable Information (PII). Both providers have patient data in their respective Snowflake accounts, including a 'PATIENT ID' column that uniquely identifies each patient. You need to create a secure join that allows the providers to determine the number of shared patients while protecting the raw 'PATIENT ID' values. Which of the following approaches is the most secure and efficient way to achieve this using Snowflake features? Select TWO options.
Answer: A,B
Explanation:
Options B and C represents valid approach. B provides good utility and is consistent. C does the same using a third-party service, which also works. Option A exposes the raw PII data which is not acceptable. Option D only gets an approximate, not an exact figure. While useful, the other solutions are much better. Option E is incorrect, it sounds good, but is not real. Therefore the correct answer is B and C.
NEW QUESTION # 219
You are a data engineer responsible for data governance in a Snowflake environment. Your company has implemented data classification using tags to identify sensitive data'. The compliance team has requested a report detailing all tables and columns that contain PII data, specifically including the tag name, tag value, the fully qualified name of the table, and the column name. You have the necessary privileges to access the Snowflake metadata views. Which of the following queries would provide the MOST comprehensive and accurate report, considering performance and ease of understanding?
Answer: B
Explanation:
Option D provides the MOST comprehensive and accurate report. It directly queries the view, filtering for 'TAG_NAME = 'PII" and 'object_domain = 'COLUMN" to specifically target tags applied to columns. It selects the database, schema, table name, column name, tag name, and tag value, providing all the necessary information. Option A requires a JOIN between "snowflake.account_usage.columns' and , which is unnecessary for this use case and less efficient. option B is missing the OBJECT_DATABASE and OBJECT_SCHEMA which is needed to fully qualify the table. option C attempts to use a table function, which is unnecessary complexity and potentially less performant. Option E does not filter for column-level tags, potentially including tags applied to other object types (e.g., tables, views), leading to inaccurate results. The fully qualified name can be easily constructed from OBJECT DATABASE, OBJECT SCHEMA and OBJECT NAME.
NEW QUESTION # 220
You are tasked with implementing data masking on a 'CUSTOMER' table. The requirement is to mask the 'EMAIL' column for all users except those with the 'DATA ADMIN' role. You have the following code snippet. What is wrong with it?
Answer: B
Explanation:
Without the masking policy code, it's impossible to determine if there are any errors. Option A is wrong without any data, Option B can be correct but we cannot know without code. Option C may be right but we do not know as well. Option D assumes there is no code provided, but we simply can't see it here. The correct answer is E, since we cannot determine the answer without code.
NEW QUESTION # 221
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