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最新的 SnowPro Advanced DEA-C02 免費考試真題:

1. You are designing a data warehouse for an e-commerce company. One of the requirements is to provide fast analytics on order fulfillment times by region. You have two tables: 'ORDERS: Contains order information, including ID, 'ORDER DATE, 'REGION ID, and 'FULFILLMENT DATE. 'REGIONS': Contains region information, including 'REGION ID' and Due to the large size of the 'ORDERS' table and the complexity of calculating fulfillment times, you decide to use materialized views.
Which of the following combinations of materialized view definition and Snowflake features would BEST optimize query performance and minimize data staleness for this scenario? Choose two options.

A) Create a materialized view that joins 'ORDERS and 'REGIONS', calculates 'FULFILLMENT TIME', and groups by 'REGION NAME'. Do not specify a clustering key.
B) create a materialized view that joins 'ORDERS' and 'REGIONS', calculates 'FULFILLMENT_TIME' grouped by 'REGION_NAME, and cluster by 'REGION NAM Configure incremental data refreshes.
C) Partition the 'ORDERS' table by 'ORDER_DATE and create a materialized view that calculates 'FULFILLMENT_TIME grouped by REGION_NAME , clustering by 'ORDER DATE'
D) Create a materialized view that joins 'ORDERS and 'REGIONS', calculates the difference between 'FULFILLMENT DATE' and 'ORDER DATE as , and groups by REGION_NAME. Cluster the view by ' REGION_NAME.
E) Use Snowflake's search optimization service on the 'ORDERS' table instead of creating a materialized view.

2. You have created an external table in Snowflake that points to a large dataset stored in Azure Blob Storage. The data consists of JSON files, and you've noticed that query performance is slow. Analyzing the query profile, you see that Snowflake is scanning a large number of unnecessary files. Which of the following strategies could you implement to significantly improve query performance against this external table?

A) Partition the data in Azure Blob Storage based on a relevant column (e.g., date) and define partitioning metadata in the external table definition using PARTITION BY.
B) Convert the JSON files to Parquet format and recreate the external table to point to the Parquet files.
C) Increase the size of the Snowflake virtual warehouse to provide more processing power.
D) Create an internal stage, copy all JSON Files, create and load the target table, and drop external table
E) Create a materialized view on top of the external table to pre-aggregate the data.

3. You are responsible for optimizing query performance on a Snowflake table called 'WEB EVENTS, which contains clickstream data'. The table has the following structure: CREATE TABLE WEB EVENTS ( event_id VARCHAR(36), user_id INT, event_time TIMESTAMP NTZ, event_type VARCHAR(50), page_url VARCHAR(255), device_type VARCHAR(50) Users frequently run queries that filter the 'WEB EVENTS table based on a combination of 'event_type', and a date range derived from 'event_time' You observe that these queries are consistently slow Which of the following strategies would be MOST effective in improving the performance of these frequently executed queries?

A) Create a materialized view that pre-aggregates data by 'event_type' , 'device_type' , and day (derived from 'event_time').
B) Create a search optimization service on the 'page_url' column.
C) Create a clustering key with the following order: 'event_type' , 'device_type' , 'event_time' .
D) Create a clustering key on 'event_time' .
E) Add a column to the 'WEB EVENTS' table for the date part of 'event_time' and create a clustering key using the new date column along with and device_type' .

4. You have configured a Snowpipe to load data from an AWS S3 bucket into a Snowflake table. The data in S3 is updated frequently. You've noticed that despite the Snowpipe being active and the S3 event notifications being configured correctly, some newly added files are not being picked up by the Snowpipe. You run 'SYSTEM$PIPE and see the 'executionstate' is 'RUNNING' but the 'pendingFileCount' remains at O, even after new files are placed in the S3 bucket. Choose all of the reasons that could explain the observations.

A) The file format specified in the Snowpipe definition does not match the actual format of the files being placed in the S3 bucket.
B) The IAM role associated with your Snowflake account does not have sufficient permissions to read from the S3 bucket. Specifically, it lacks the 's3:GetObject' permission.
C) There is an insufficient warehouse size configured for the Snowpipe. Increase the warehouse size for optimal performance.
D) The S3 event notification configuration is missing the 's3:ObjectCreated: event type, meaning that new file creation events are not being sent to the SQS queue or SNS topic.
E) The SQS queue or SNS topic associated with the S3 event notifications has a message retention period that is too short. Messages containing event details for new files are being deleted before Snowpipe can process them.

5. Which of the following statements are true regarding data masking policies in Snowflake? (Select all that apply)

A) Once a masking policy is applied to a column, the original data is permanently altered.
B) Data masking policies can be applied to both tables and views.
C) Data masking policies are supported on external tables.
D) Different masking policies cannot be applied to different columns within the same table.
E) The 'CURRENT_ROLE()' function can be used within a masking policy to implement role-based data masking.

問題與答案：