Title: DEA-C02 Demotesten & DEA-C02 Dumps Deutsch [Print This Page] Author: petekel210 Time: 5 hour before Title: DEA-C02 Demotesten & DEA-C02 Dumps Deutsch BONUS!!! Laden Sie die vollständige Version der ZertFragen DEA-C02 Pr¨¹fungsfragen kostenlos herunter: https://drive.google.com/open?id=1hvoPnfh6dNfa272w3RjC7mo7BXQbXivo
Aufgrund der großen Übereinstimmung mit den echten Snowflake DEA-C02 Pr¨¹fungsfragen und -antworten (SnowPro Advanced: Data Engineer (DEA-C02)) können wir Ihnen 100%-Pass-Garantie versprechen. Wir aktualisieren jeden Tag nach den Informationen von Pr¨¹fungsabsolventen oder Mitarbeitern aus dem Testcenter unsere Pr¨¹fungsfragen und Antworten zu Snowflake DEA-C02 Fragenpool (SnowPro Advanced: Data Engineer (DEA-C02)). Wir extrahieren jeden Tag die Informationen der tatsächlichen Pr¨¹fungen und integrieren in unsere Produkte.
Die Snowflake DEA-C02 Pr¨¹fungsfragen und Antworten (DEA-C02) von ZertFragen ist eine Garantie f¨¹r eine erfolgreiche Pr¨¹fung! Bisher fällt noch keiner unserer Kandidaten durch! Falls jemand bei der Zertifizierungspr¨¹fung durchfallen sollte, zahlen wir 100% Material-Geb¨¹hr zur¨¹ck. Wir ¨¹bernehmen die volle Geld-zur¨¹ck-Garantie auf Ihre Zertifizierungspr¨¹fungen! Unsere DEA-C02 Fragen und Antoworten (SnowPro Advanced: Data Engineer (DEA-C02)) sind aus dem Fragenpool, alle sind echt und original.
DEA-C02 Pr¨¹fungsfragen Pr¨¹fungsvorbereitungen, DEA-C02 Fragen und Antworten, SnowPro Advanced: Data Engineer (DEA-C02)Nun bieten viele Ausbildungsinstitute Ihnen die Schulungsunterlagen zur Snowflake DEA-C02 Zertifizierungspr¨¹fung. Meistens bekommen die Kandidaten per diese Websites keine ausf¨¹hrlichen Materialien. Denn ihre Materialien zur Snowflake DEA-C02 Zertifizierungspr¨¹fung sind breit gefächert und nicht zielgerichtet. So können sie keine Aufmerksamkeit der Kandidaten gewinnen. Snowflake SnowPro Advanced: Data Engineer (DEA-C02) DEA-C02 Pr¨¹fungsfragen mit Lösungen (Q328-Q333):328. Frage
A provider account is sharing a database named 'SHARED DB' through a share named 'MY SHARE. The consumer account has created a database named 'CONSUMER DB' from the share. The provider account revokes access to a table named 'SALES DATA within 'SHARED DB'. What will happen when a user in the consumer account attempts to query 'CONSUMER DB.SHARED SCHEMA.SALES DATA'?
A. The query will be cached based on the initial access, so users can continue query previous result based on same SQL
B. The query will be automatically re-routed to another available share containing 'SALES DATA'.
C. The query will fail with an error message indicating that the table does not exist or the user does not have privileges.
D. The query will execute successfully, but the user will receive an empty result set.
E. The query will execute successfully, but only return data that existed before the access was revoked.
Antwort: C
Begr¨¹ndung:
When access to a shared object is revoked in the provider account, the consumer account loses access to that object. Subsequent queries in the consumer account will fail with an error message indicating insufficient privileges or that the object does not exist. Snowflake does not automatically redirect queries to other shares or provide cached result once access is revoked.
329. Frage
You have a table 'CUSTOMERS' with columns 'CUSTOMER ID', 'FIRST NAME', 'LAST NAME, and 'EMAIL'. You need to transform this data into a semi-structured JSON format and store it in a VARIANT column named 'CUSTOMER DATA' in a table called 'CUSTOMER JSON'. The desired JSON structure should include a root element 'customer' containing 'id', 'name', and 'contact' fields. Which of the following SQL statements, used in conjunction with a CREATE TABLE and INSERT INTO statement for CUSTOMER JSON, correctly transforms the data?
A. Option B
B. Option A
C. Option D
D. Option C
E. Option E
Antwort: B
Begr¨¹ndung:
The correct answer constructs the JSON structure using nested 'OBJECT_CONSTRUCT functions. Option A directly creates a Snowflake VARIANT, which can be inserted into the 'CUSTOMER_DATR column. While many other approaches exist that involve parsing or converting to and from string values, those approaches are unnecessary because OBJECT_CONSTRUCT supports the correct desired behavior directly.
330. Frage
You are designing a data recovery strategy for a critical table 'CUSTOMER DATA' in your Snowflake environment. The data in this table is highly sensitive, and regulatory requirements mandate a retention period of at least 90 days for potential audits. You need to configure the Time Travel retention period to meet these requirements. What is the maximum supported Time Travel retention period, and how would you set it at the table level?
A. The maximum retention period is 90 days for Enterprise Edition or higher. You can set it using: 'ALTER TABLE CUSTOMER DATA SET DATA RETENTION TIME IN DAYS = 90;'
B. The maximum retention period is 365 days. You can set it using: ALTER TABLE CUSTOMER DATA SET DATA RETENTION TIME IN DAYS = 365;'
C. The maximum retention period is 90 days. You can set it using: 'ALTER TABLE CUSTOMER_DATA SET = 90;'
D. The maximum retention period depends on your Snowflake edition and can be set at the account level only.
E. The maximum retention period is 7 days. You can set it using: 'ALTER TABLE CUSTOMER_DATA SET = 7;'
Antwort: A
Begr¨¹ndung:
For Snowflake Enterprise Edition (or higher), the maximum Time Travel retention period is 90 days. The 'ALTER TABLE ... SET DATA RETENTION_TIME IN DAYS' command allows setting the retention period at the table level. Option D is partially correct about editions impacting limits, but incorrect about account-level settings only.
331. Frage
You are developing a Snowpark Python stored procedure that performs complex data transformations on a large dataset stored in a Snowflake table named 'RAW SALES'. The procedure needs to efficiently handle data skew and leverage Snowflake's distributed processing capabilities. You have the following code snippet:
Which of the following strategies would be MOST effective to optimize the performance of this Snowpark stored procedure, specifically addressing potential data skew in the 'product id' column, assuming 'product_id' is known to cause uneven data distribution across Snowflake's micro-partitions?
A. Use the 'pandas' API within the Snowpark stored procedure to perform the transformation, as 'pandas' automatically optimizes for data skew.
B. Implement a custom partitioning strategy using before the transformation logic to redistribute data evenly across the cluster.
C. Utilize Snowflake's automatic clustering on the 'TRANSFORMED_SALES table by specifying 'CLUSTER BY when creating or altering the table to ensure future data is efficiently accessed.
D. Increase the warehouse size significantly to compensate for the data skew and improve overall processing speed without modifying the partitioning strategy.
E. Combine salting with repartitioning by adding a random number to the 'product_id' before repartitioning, then removing the salt after the transformation to break up the skew. Then, enable automatic clustering on the 'TRANSFORMED SALES' table.
Antwort: E
Begr¨¹ndung:
Option E is the most effective solution. Salting breaks up data skew before repartitioning. Automatic clustering on the transformed table optimizes future queries. Repartitioning redistributes the data across Snowflake's processing nodes, and Automatic Clustering will help in maintaining performance as the data changes in TRANSFORMED_SALES table over time. Option A, without salting, may still be inefficient due to the initial skew. Option B improves query performance but doesn't address the initial transformation skew. Option C is incorrect because 'pandas' in Snowpark does not automatically handle data skew at the Snowflake level. Option D is a costly workaround that doesn't fundamentally solve the skew problem.
332. Frage
You've created a JavaScript UDF in Snowflake to perform complex string manipulation. You need to ensure this UDF can handle a large volume of data efficiently. The UDF is defined as follows:
When testing with a large dataset, you observe poor performance. Which of the following strategies, when applied independently or in combination, would MOST likely improve the performance of this UDF?
A. Convert the JavaScript UDF to a Java UDF, utilizing Java's more efficient string manipulation libraries and leveraging Snowflake's Java UDF execution environment.
B. Replace the JavaScript UDF with a SQL UDF that uses built-in Snowflake string functions like 'REGEXP REPLACE and 'REPLACE. SQL UDFs are generally more optimized within Snowflake's execution engine.
C. Pre-compile the regular expressions used within the JavaScript UDF outside of the function and pass them as constants into the function. JavaScript regex compilation is expensive, and pre-compilation can reduce overhead.
D. Increase the warehouse size to the largest available size (e.g., X-Large) to provide more resources for the UDF execution.
E. Ensure the input 'STRING' is defined with the maximum possible length to provide sufficient memory allocation for the JavaScript engine to manipulate the string.
Antwort: A,B,C
Begr¨¹ndung:
Options A, C and E can all contribute to better performance. SQL UDFs benefit from Snowflake's optimized execution engine for standard operations, making them often faster than JavaScript UDFs for string manipulation when possible. Pre-compiling regular expressions (Option C) avoids redundant compilation steps during each UDF invocation. Converting to a Java UDF (Option E) gives more control over efficiency compared to JS. The option D may help, but the performance gain is not guaranteed and is more related to resource availability than the UDF's efficiency. The option B is not valid since the size of input STRING won't matter the javascript engine.
333. Frage
......
Wenn Sie einige unserer Pr¨¹fungsfrage und Antworten f¨¹r Snowflake DEA-C02 Zertifizierungspr¨¹fung versucht haben, dann können Sie eine Wahl dar¨¹ber treffen, ZertFragen zu kaufen oder nicht. Wir werden Ihnen mit 100% Bequemlichkeit und Garantie bieten. Denken Sie bitte daran, dass nur ZertFragen Ihen zum Bestehen der Snowflake DEA-C02 Zertifizierungspr¨¹fung verhelfen kann. DEA-C02 Dumps Deutsch: https://www.zertfragen.com/DEA-C02_prufung.html
Snowflake DEA-C02 Demotesten Genießen Sie einjähriges kostenloses Update , Die Test Engine auf ZertFragen DEA-C02 Dumps Deutsch kann eine echte Pr¨¹fungsumgebung simulieren, auf diese Wiese können Sie die DEA-C02 Dumps Deutsch - SnowPro Advanced: Data Engineer (DEA-C02) Pr¨¹fung m¨¹hlos bestehen, Keine Angst vor DEA-C02, Snowflake DEA-C02 Demotesten Keine Geräte-spezifische Beschränkung f¨¹r App Version, Was wir am meisten garantieren ist, dass unsere Software vielen Pr¨¹fungsteilnehmern bei der Zertifizierung der Snowflake DEA-C02 geholfen hat.
Es behandelt die Gewohnheiten, die der Autor f¨¹r DEA-C02 ein erfolgreiches und unabhängiges Leben hält, Ebenso laufen wir als Liebende und Entdecker zwischen Menschen hin und her und zeigen DEA-C02 Demotesten sowohl Gut als auch Böse, eines in der Sonne, eines im Sturm und ein drittes in der Nacht. Kostenlos DEA-C02 Dumps Torrent & DEA-C02 exams4sure pdf & Snowflake DEA-C02 pdf vceGenießen Sie einjähriges kostenloses Update , Die Test Engine auf DEA-C02 Dumps Deutsch ZertFragen kann eine echte Pr¨¹fungsumgebung simulieren, auf diese Wiese können Sie die SnowPro Advanced: Data Engineer (DEA-C02) Pr¨¹fung m¨¹hlos bestehen.
Keine Angst vor DEA-C02, Keine Geräte-spezifische Beschränkung f¨¹r App Version, Was wir am meisten garantieren ist, dass unsere Software vielen Pr¨¹fungsteilnehmern bei der Zertifizierung der Snowflake DEA-C02 geholfen hat.
