Title: DEA-C02 Deutsche Pr¨¹fungsfragen - DEA-C02 Fragen Beantworten [Print This Page] Author: johngre504 Time: 12 hour before Title: DEA-C02 Deutsche Pr¨¹fungsfragen - DEA-C02 Fragen Beantworten Sind Sie noch besorgt ¨¹ber die Pr¨¹fung der Snowflake DEA-C02? Zögern Sie noch, ob es sich lohnt, unsere Softwaren zu kaufen? Dann was Sie jetzt tun m¨¹ssen ist, dass die Demo der Snowflake DEA-C02, die wir bieten, kostenlos herunterladen! Sie werden finden, dass diese Vorbereitungsunterlagen was Sie gerade brauchen sind! Die Belastung der Snowflake DEA-C02 Test zu erleichtern und die Leistung Ihrer Vorbereitung zu erhöhen sind unsere Pflicht!
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You are developing a data transformation pipeline in Snowpark Python to aggregate website traffic data'. The raw data is stored in a Snowflake table named 'website_events' , which includes columns like 'event_timestamp' , 'user_id', 'page_urr , and 'event_type'. Your goal is to calculate the number of unique users visiting each page daily and store the aggregated results in a new table named Considering performance and resource efficiency, select all the statements that are correct:
A. Using is the most efficient method for writing the aggregated results to Snowflake, regardless of data size.
B. Using followed by is an efficient approach to calculate unique users per page per day.
C. Applying a filter early in the pipeline to remove irrelevant 'event_type' values can significantly reduce the amount of data processed in subsequent aggregation steps.
D. Defining the schema for the table before writing the aggregated results is crucial for ensuring data type consistency and optimal storage.
E. Caching the 'website_eventS DataFrame using 'cache()' before performing the aggregation is always beneficial, especially if the data volume is large.
Antwort: B,C,D
Begr¨¹ndung:
Option A is correct: Grouping by page URL and the date part of the timestamp, followed by a distinct count of user IDs, accurately calculates unique users per page per day. Option C is correct: Defining the schema ensures data types are correctly mapped and enforced, preventing potential issues during data loading and improving storage efficiency. Option E is correct: Filtering early reduces the data volume for subsequent operations, improving performance.
212. Frage
A financial institution needs to tokenize sensitive customer data (credit card numbers) stored in a Snowflake table named 'CUSTOMER_DATA before it's consumed by a downstream reporting application. The institution uses an external tokenization service accessible via a REST API. Which of the following approaches is the MOST secure and scalable way to implement tokenization during data loading, minimizing exposure of the raw credit card data within Snowflake?
A. Use a Snowflake UDF (User-Defined Function) written in Java that calls the external tokenization API directly. Create a masking policy that utilizes the UDF and applies it to the credit card number column.
B. Load the raw data into a staging table, then create a Snowflake Task that executes a stored procedure. The stored procedure calls the external tokenization API using 'SYSTEM$EXTERNAL_FUNCTION_REQUEST' for each row and updates the original table with the tokenized values.
C. Utilize Snowflake's Snowpipe to ingest the data directly. Inside a COPY INTO statement, use an external function to call the tokenization service during the ingestion process to tokenize the data before it's loaded into the target table.
D. Load the raw data directly into the 'CUSTOMER DATA' table. Create a masking policy that utilizes a UDF that calls the external tokenization API directly to tokenize the credit card number values on read.
E. Use Snowflake's Data Sharing feature to securely share the raw data with the downstream application, instructing them to perform the tokenization within their own environment.
Antwort: C
Begr¨¹ndung:
Option E is the most secure and scalable approach. It tokenizes the data during the load process, minimizing the amount of time the raw data resides in Snowflake. Using a UDF in a masking policy (options A and C) tokenizes the data on read, meaning the raw data is stored in Snowflake. Option B, using a stored procedure and , can be less efficient for large datasets. Data sharing raw data (Option D) defeats the purpose of tokenization for the source environment.
213. Frage
You are using Snowflake Iceberg tables to manage a large dataset stored in AWS S3. Your team needs to perform several operations on this data, including updating existing records, deleting records, and performing time travel queries to analyze data at different points in time. Which of the following statements regarding the capabilities and limitations of Snowflake Iceberg tables are TRUE? (Select all that apply)
A. Snowflake Iceberg tables support both row-level and column-level security policies, allowing you to control access to sensitive data at a granular level.
B. Snowflake Iceberg tables support 'UPDATE, ' DELETE, and 'MERGE operations, allowing you to modify existing data directly in the data lake.
C. Snowflake automatically manages the Iceberg metadata, including snapshots and manifests, eliminating the need for manual metadata management tasks.
D. Snowflake Iceberg tables support time travel queries using the 'AT(timestamp => ...y syntax, allowing you to query the state of the data at a specific point in time.
E. Snowflake Iceberg tables do not support transaction isolation levels, so concurrent write operations may lead to data inconsistencies.
Antwort: B,C,D
Begr¨¹ndung:
Snowflake Iceberg tables do support 'UPDATE' , 'DELETE' , and 'MERGE operations to modify data directly in the data lake (A). They do support time travel using the 'AT(timestamp => ...y syntax (B). Snowflake does automatically manage the Iceberg metadata (D). Snowflake Iceberg tables provide ACID guarantees and transaction isolation, so concurrent writes are handled safely. Row and column level security can be applied using Snowflake's masking policies and row access policies, but it is not a feature directly built into the Iceberg specification; rather it is a feature of the Snowflake platform. Thus, choice E is incorrect.
214. Frage
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. Convert the column to a VARCHAR data type.
B. Create a materialized view that pre-aggregates the total sales amount per product and month.
C. Create a clustering key on 'PRODUCT_ID and 'TRANSACTION_DATE columns in the 'SALES_TRANSACTIONS' table.
D. Increase the virtual warehouse size to the largest available size.
E. Create a temporary table with the results of the query and query that table instead.
Antwort: B,C
Begr¨¹ndung:
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.
215. Frage
You are responsible for monitoring the performance of several data pipelines in Snowflake that heavily rely on streams. You notice that some streams consistently lag behind the base tables. You need to proactively identify the root cause and implement solutions. Which of the following metrics and monitoring techniques would be MOST helpful in diagnosing and resolving the stream lag issue? (Select all that apply)
A. Analyze the query history in Snowflake to identify any long-running queries that are consuming data from the streams and potentially blocking new changes from being processed.
B. Monitor resource consumption (CPU, memory, disk) of the virtual warehouse(s) used for processing data from the streams.
C. Regularly query the 'CURRENT_TIMESTAMP and columns of the stream to calculate the data latency.
D. Increase the 'DATA RETENTION TIME IN DAYS for the base tables to ensure that historical data is always available for the streams, even if they lag behind.
E. Monitor the 'SYSTEM$STREAM HAS DATA function's output for the affected streams to quickly determine if there are pending changes.
Antwort: A,B,C,E
Begr¨¹ndung:
Options A, B, C and E are all helpful for monitoring stream lag. 'SYSTEM$STREAM HAS DATA' confirms the presence of changes. 'CURRENT_TIMESTAMP' vs. directly measures latency. Analyzing query history identifies blocking consumers. Monitoring warehouse resources can reveal bottlenecks in processing stream data. Increasing 'DATA RETENTION_TIME IN_DAYS (D) for the base tables is irrelevant to stream lag and affects table history, not stream processing performance. It does not address the issue of why the stream is lagging.
216. Frage
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