Ian Green Ian Green's Profile Page

Ian Green Ian Green

0 Course Enrolled • 0 Course Completed

Biography

Reliable DEA-C02 training materials bring you the best DEA-C02 guide exam: SnowPro Advanced: Data Engineer (DEA-C02)

Wir versprechen Ihnen nicht nur eine 100%-Pass-Garantie, sondern bieten Ihnen einen einjährigen kostenlosen Update-Service. Wenn Sie unvorsichtigerweise die Snowflake DEA-C02 Prüfung nicht bestehen, erstatten wir Ihnen die gesammte Summe zurück. Aber das ist doch niemals passiert. Wir garantieren, dass Sie die Snowflake DEA-C02 Prüfung 100% bestehen können. Sie können teilweise im Internet die Snowflake DEA-C02 Prüfungsfragen und Antworten von Pass4Test als Probe umsonst herunterladen.

Die Prüfungsunterlagen zur Snowflake DEA-C02 Zertifizierungsprüfung werden nach dem Lehrkompendium und den echten Prüfungen bearbeitet. Wir aktualisieren auch ständig unsere Schulungsunterlagen, so dass Sie in erster Zeit die neuesten und besten Informationen bekommen. Wenn Sie unsere Schulungsunterlagen zur Snowflake DEA-C02 Zertifizierungsprüfung kaufen, können Sie einen einjährigen kostenlosen Update-Service bekommen. Sie können jederzeit Abonnmentszeit verlängern, so dass Sie mehr Zeit haben, sich auf die Snowflake DEA-C02 Prüfung vorzubereiten.

>> DEA-C02 Zertifizierung <<

DEA-C02 zu bestehen mit allseitigen Garantien

Man sollte die verlässliche Firma auswählen, wenn man etwas kaufen will. Was wir Pass4Test Ihnen garantieren können sind: zuerst, die höchste Bestehensquote der Snowflake DEA-C02 Prüfung, die Probe mit kostenfreier Demo der Snowflake DEA-C02 sowie der einjährige kostenlose Aktualisierungsdienst. Um mehr Ihre Sorgen zu entschlagen, garantieren wir noch, falls Sie die Snowflake DEA-C02 Prüfung leider nicht bestehen, geben wir Ihnen alle Ihre bezahlte Gebühren zurück. Pass4Test----Ihr bester Partner bei Ihrer Vorbereitung der Snowflake DEA-C02!

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) DEA-C02 Prüfungsfragen mit Lösungen (Q12-Q17):

12. Frage
You are tasked with implementing a Row Access Policy (RAP) on a table 'customer_data' that contains Personally Identifiable Information (PII). The policy must meet the following requirements: 1. Data analysts with the 'ANALYST role should only see anonymized customer data (e.g., masked email addresses, hashed names). 2. Data engineers with the 'ENGINEER role should see the full, unmasked customer data for data processing purposes. 3. No other roles should have access to the data'. You create the following UDFs: 'MASK EMAIL(email address VARCHAR)': Returns an anonymized version of the email address. 'HASH NAME(name VARCHAR): Returns a hash of the customer name. Which of the following is the most efficient and secure way to implement this RAP, assuming minimal performance impact is desired?

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

Antwort: A

Begründung:
Option D is the most efficient because it filters access based on roles in the RAP without applying expensive UDFs within the policy itself. This minimizes the performance impact of the RAP. The view 'analyst_view' then applies the masking/hashing for analysts. Options A and B apply the UDFs within the RAP, which will significantly degrade performance. The 'MASK EMAIL(email_address) IS NOT NULL' conditions are also incorrect as they are not validating the email. Option C doesn't implement the required masking/hashing for analysts at all, and also is not as effecient. Option E allows both roles to see all data which does not meet requirement 1.

13. Frage
A Snowflake table 'CUSTOMER ORDERS is clustered by 'ORDER DATE. You have observed the clustering depth increasing over time, impacting query performance. To improve performance, you decide to recluster the table. However, you need to minimize the impact on concurrent DML operations and cost. Which of the following strategies would be MOST effective in managing this reclustering process?

A. Recluster the entire table in a single transaction during off-peak hours.
B. Implement a continuous reclustering process using Snowpipe to automatically recluster new data as it arrives.
C. Create a new table clustered by 'ORDER_DATE, copy data in parallel, and then swap tables.
D. Leverage Snowflake's automatic reclustering feature, monitor its performance, and adjust warehouse size as needed.
E. Use 'CREATE OR REPLACE TABLE with SELECT FROM CUSTOMER ORDERS to rebuild the table with optimized clustering.

Antwort: D

Begründung:
Snowflake's automatic reclustering feature is designed specifically to address this scenario. It automatically reclusters data in the background, minimizing the impact on concurrent DML operations. Options A and E will lock the table and can cause performance impacts during peak hours. Option B is more complex and requires significant downtime during the swap. Option C describes Snowpipe data ingestion, which isn't reclustering. While creating clustered tables with Snowpipe is possible, this option does not address the already unclustered data. Adjusting the warehouse size is relevant to reclustering performance but is secondary to enabling the automatic reclustering itself.

14. Frage
You have a table named 'TRANSACTIONS which is frequently queried by 'TRANSACTION_DATE and 'CUSTOMER ID. You want to define a clustering strategy for this table. You are aware that defining multiple clustering keys is possible. Given the following considerations, which of the following clustering strategies would provide the BEST performance AND minimize reclustering costs, assuming both columns have similar cardinality and are equally used in WHERE clauses? (Assume cost optimization is the most critical factor if performance difference is minimal.)

A.
B. Create two separate tables: one clustered by 'TRANSACTION DATE and another clustered by 'CUSTOMER ID', and use appropriate views to redirect queries to the correct table.
C.
D.
E.

Antwort: C,E

Begründung:
Clustering by both and is beneficial when queries frequently filter on both columns. Either order, TRANSACTION_DATE followed by CUSTOMER_ID, or CUSTOMER_ID followed by TRANSACTION_DATE, could provide similar performance depending on query patterns. However, having both columns as clustering keys will allow better filtering of micro-partitions. Creating two separate tables as suggested in Option E introduces complexity in data maintenance (two copies of data) and query redirection logic, increasing overall cost. While it might provide optimal performance for specific query patterns, the cost is generally higher than using a composite clustering key when both keys are frequently used.

15. Frage
You are designing a Snowpark Python application to process streaming data from a Kafka topic and land it into a Snowflake table 'STREAMED DATA. Due to the nature of streaming data, you want to achieve the following: 1. Minimize latency between data arrival and data availability in Snowflake. 2. Ensure exactly-once processing semantics to prevent data duplication. 3. Handle potential schema evolution in the Kafka topic without breaking the pipeline. Which combination of Snowpark and Snowflake features, applied with the correct configuration, would BEST satisfy these requirements? Select all that apply.

A. Use Snowpipe with auto-ingest and configure it to trigger on Kafka topic events. Define a VARIANT column in 'STREAMED_DATX to handle schema evolution.
B. Implement a Snowpark Python UDF that consumes data directly from the Kafka topic using a Kafka client library. Write data into 'STREAMED_DATX within a single transaction. Use a structured data type for the 'STREAMED DATA'.
C. Use Snowflake's native Kafka connector to load data into a staging table. Then, use a Task and Stream combination, using a Snowpark Python UDF, to transform and load the data into 'STREAMED DATA' within a single transaction, handling schema evolution by casting columns to their new types or dropping missing column data.
D. Utilize Snowflake Streams on in conjunction with Snowpark to transform and cleanse the data after it has been ingested by Snowpipe. Apply a merge statement to update an external table of parquet files.
E. Use Snowflake Connector for Kafka to load data into a staging table. Then, use Snowpark Python to transform and load the data into 'STREAMED_DATR within a single transaction. Implement schema evolution logic in the Snowpark code to handle changes in the Kafka topic schema.

Antwort: C,E

Begründung:
Options D and E represent the most reliable solutions to this problem statement. Option D: The combination of the Snowflake Connector for Kafka and Snowpark offers a balanced approach. The connector efficiently loads the raw data, and Snowpark Python provides the flexibility to transform the data within a transaction and implement schema evolution logic. Option E: Snowflake's Kafka connector, combined with tasks, streams, and a Snowpark IJDF, provides a pipeline that continuously transforms data and is only triggered by new events in the staging table created by the Kafka connector. Implementing schema evolution in the IJDF itself handles small changes effectively. Option A does not provide exactly-once semantics. While VARIANT columns handle schema evolution, Snowpipe itself might deliver messages more than once. Option B is less scalable and harder to manage compared to using the Snowflake Connector for Kafka or Streams/Tasks. Option C, using Streams on 'STREAMED_DATA' , can lead to data duplication if not managed correctly and updating an external table negates a central table stream for change control.

16. Frage
A Snowflake data engineer is troubleshooting a slow-running query that joins two large tables, 'ORDERS' (1 billion rows) and 'CUSTOMER' (10 million rows), using the 'CUSTOMER ID' column. The query execution plan shows a significant amount of data spilling to local disk. The query is as follows:

Which of the following are the MOST likely root causes of the disk spilling and the best corresponding solutions? Select two options that directly address the disk spilling issue.

A. The 'CUSTOMER_ID column is not properly clustered in either the 'ORDERS' or 'CUSTOMER table. Define a clustering key on 'CUSTOMER_ID for both tables.
B. The query is performing a full table scan on the 'ORDERS' table. Add an index on the 'CUSTOMER ID column in the 'ORDERS table.
C. The virtual warehouse is undersized for the amount of data being processed. Increase the virtual warehouse size to provide more memory.
D. The statistics on the tables are outdated. Run 'ANALYZE TABLE ORDERS' and 'ANALYZE TABLE CUSTOMER to update the statistics.
E. The join operation is resulting in a large intermediate result set that exceeds the available memory. Apply a filter on the 'ORDERS' table to reduce the data volume before the join.

Antwort: C,E

Begründung:
Options A and D are the most direct solutions for disk spilling. A undersized warehouse directly impacts available memory, leading to disk spilling. Increasing the warehouse size (option A) provides more memory for the operation. When data spill happens increasing the warehouse size is the primary action to take. Option D correctly addresses the root cause of the spill an overly large intermediate result set. Reducing the data volume before the join minimizes the memory required. Option B could improve query performance overall, but doesn't directly address disk spilling. Option C is incorrect, as Snowflake does not support manual indexes. Option E would improve the accuracy of the query optimizer's decisions, which could indirectly improve performance, but is less direct than options A and D.

17. Frage
......

Unsere Prüfungsunterlage zu Snowflake DEA-C02（SnowPro Advanced: Data Engineer (DEA-C02)）enthältet alle echten, originalen und richtigen Fragen und Antworten. Die Abdeckungsrate unserer Unterlage (Fragen und Antworten) zu Snowflake DEA-C02（SnowPro Advanced: Data Engineer (DEA-C02)）ist normalerweise mehr als 98%.

DEA-C02 Lernressourcen: https://www.pass4test.de/DEA-C02.html

Snowflake DEA-C02 Zertifizierung Bitte überprüfen Sie regelmäßig Ihre E-Mail, Pass4Test DEA-C02 Lernressourcen wird Ihnen helfen, die Prüfung 100% zu bestehen, Snowflake DEA-C02 Zertifizierung Es bedeutet, dass Sie die Chance haben, die neueste Informationen zu halten, Snowflake DEA-C02 Zertifizierung Teil der Testdaten im Internet ist kostenlos, Snowflake DEA-C02 Zertifizierung Bei uns ist es auf jeden Fall gar kein Problem.

Ist der Titan der Gott von Braavos, Logik und gesunder Menschenverstand DEA-C02 sprachen dagegen, Bitte überprüfen Sie regelmäßig Ihre E-Mail, Pass4Test wird Ihnen helfen, die Prüfung 100% zu bestehen.

Neueste DEA-C02 Pass Guide & neue Prüfung DEA-C02 braindumps & 100% Erfolgsquote

Es bedeutet, dass Sie die Chance haben, die neueste Informationen DEA-C02 Lernressourcen zu halten, Teil der Testdaten im Internet ist kostenlos, Bei uns ist es auf jeden Fall gar kein Problem.

Ian Green Ian Green

Biography

Advance your career today!