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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q59-Q64):
NEW QUESTION # 59
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. You can set it using: 'ALTER TABLE CUSTOMER_DATA SET = 90;'
- B. The maximum retention period depends on your Snowflake edition and can be set at the account level only.
- C. The maximum retention period is 7 days. You can set it using: 'ALTER TABLE CUSTOMER_DATA SET = 7;'
- D. 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;'
- E. The maximum retention period is 365 days. You can set it using: ALTER TABLE CUSTOMER DATA SET DATA RETENTION TIME IN DAYS = 365;'
Answer: D
Explanation:
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.
NEW QUESTION # 60
You are ingesting data from an external stage (AWS S3) into a Snowflake table using Snowpipe. Data files are continuously being uploaded to the stage. After several hours, you notice that some data files are not being loaded. You check the Snowpipe error notifications and see 'net.snowflake.ingest.errors.FileSizeLimitExceededError'. You have already verified that the Snowpipe is correctly configured and the user has the necessary permissions. What are the MOST LIKELY reasons for this error and how can you resolve them?
- A. The Snowpipe configuration is incorrect; specifically, the 'FILE FORMAT parameter is not correctly specified to handle the file type. Reconfigure the Snowpipe with the correct 'FILE FORMAT.
- B. The data files are being uploaded to the stage faster than Snowpipe can process them. Increase the value of the 'MAX CONCURRENCY parameter in the Snowpipe definition.
- C. The size of the data files in the stage exceeds the maximum allowed size for Snowpipe. Split the large files into smaller files before uploading to the stage.
- D. The Snowpipe is encountering a transient network error. Reset the pipe using ALTER PIPE REFRESH;
- E. Snowflake has reached its maximum allowable data storage capacity. Increase your Snowflake storage capacity to resolve this issue.
Answer: C
Explanation:
The 'net.snowflake.ingest.errors.FileSizeLimitExceededError' clearly indicates that the size of the data files being ingested is exceeding the maximum allowed size for Snowpipe. While Snowflake does have storage capacity limits, that is not the root cause of this specific error. Splitting the files into smaller sizes prior to uploading will allow Snowpipe to process the data without exceeding the size limit. While network errors can occur, the error message is specific to file size. Snowpipe does not use 'MAX CONCURRENCY parameter. It automatically adjusts the concurrency The file format issues will create different error.
NEW QUESTION # 61
A financial institution is using Snowflake to store transaction data for millions of customers. The data is stored in a table named 'TRANSACTIONS with columns such as 'TRANSACTION ID, 'CUSTOMER ID', 'TRANSACTION DATE, 'TRANSACTION_AMOUNT, and 'MERCHANT CATEGORY'. Analysts are running complex analytical queries that often involve filtering transactions by 'TRANSACTION_DATE, 'MERCHANT CATEGORY' , and 'TRANSACTION_AMOUNT ranges. These queries are experiencing performance bottlenecks. The data team wants to leverage query acceleration service to improve performance without significantly altering the existing query patterns. Which of the following actions or combination of actions would be MOST beneficial, considering the constraints and the nature of the queries? (Select TWO)
- A. Create materialized views pre-aggregating the transaction data by 'MERCHANT_CATEGORY and 'TRANSACTION_DATE, and enable query acceleration on the virtual warehouse.
- B. Create separate virtual warehouses dedicated to reporting queries and ad-hoc queries respectively. Enable query acceleration only for the warehouse running reporting queries.
- C. Enable Search Optimization Service for the 'TRANSACTIONS' table, specifically targeting the 'MERCHANT_CATEGORY column. Enable query acceleration on the virtual warehouse.
- D. Enable Automatic Clustering on the 'TRANSACTIONS' table, ordering the keys as 'TRANSACTION_DATE, 'MERCHANT_CATEGORY', 'CUSTOMER_ID. Then, enable query acceleration on the virtual warehouse.
- E. Increase the size of the virtual warehouse used for running the queries and enable query acceleration on the warehouse without further modifications.
Answer: C,D
Explanation:
Enabling Automatic Clustering on 'TRANSACTIONS with the specified key order ('TRANSACTION DATES, 'MERCHANT_CATEGORY , 'CUSTOMER_ID') aligns the data layout with common query patterns, allowing Snowflake to efficiently prune irrelevant data during query execution. This drastically improves query performance. Enabling Search Optimization on the 'MERCHANT_CATEGORY further enhances query performance by creating search access paths that enable faster lookups and filtering based on merchant category. Simply increasing the warehouse size (option A) may provide some improvement, but it's less targeted and potentially less cost-effective than optimizing the data organization. While dedicated warehouses (option C) can improve concurrency, they do not address the underlying performance bottleneck related to data access. Materialized views (option E) can be beneficial, but they require careful design and maintenance, and they might not be flexible enough for ad-hoc queries with varying filter conditions. Clustering and search optimization provide a more general and efficient solution in this scenario.
NEW QUESTION # 62
You're configuring a Kafka connector to load data into Snowflake. You need to ensure that only records with a specific 'event_type' field equal to 'payment_processed' are loaded into the 'PAYMENT TABLE in Snowflake. Given the following connector configuration snippet, which additional property must be added to the 'transforms' section to achieve this filtering?
- A. Option B
- B. Option E
- C. Option C
- D. Option A
- E. Option D
Answer: B
Explanation:
The correct answer is E. The Kafka Connect framework requires a specific syntax for predicate expressions. While option B may seem intuitive, 'equalslgnoreCase' ensures case-insensitive comparison, and this is a valid predicate function. Options A and D are incorrect because they either use the wrong transformation type or attempt to use regex in an unsupported way. Option C will error due to PredicateFilter not existing, and the syntax is not correct.
NEW QUESTION # 63
You are tasked with creating a development environment from a production database named 'PROD DB'. This database contains sensitive data, and you need to mask the data in the development environment. You decide to use cloning and a transformation function during the cloning process. What is the MOST efficient approach to clone 'PROD DB' into a development database 'DEV DB' and mask sensitive data in the process?
- A. Create a clone of 'PROD named 'DEV DB'. Create a warehouse for running masking policies. Then apply masking policies to the tables in 'DEV DB' Cloning masks the underlying data directly.
- B. Create a clone of 'PROD named 'DEV DB', then create views on 'DEV DB' using masking policies. Cloning the Views from 'PROD will automatically copy the masking policies.
- C. Create a clone of 'PROD named 'DEV DB'. Create stored procedures on 'DEV DB' which apply masking at the query level. Cloning databases does not preserve masking policies from the Source database
- D. Create a clone of 'PROD named 'DEV DB'. Define masking policies on the columns in 'PROD DB' before cloning. These policies will be automatically applied to the cloned tables in "DEV_DB' ensuring all data is masked during query time in the DEV environment.
- E. Clone 'PROD to ' DEV DB'. Export the data from 'DEV DB', transform it using a scripting language (e.g., Python), and then load the transformed data back into replacing the original data.
Answer: D
Explanation:
Masking policies defined on the original database will be applied to the cloned tables. Cloning does not directly mask data physically. Instead, the masking policies are applied to the cloned objects. Therefore, the most efficient approach is to define masking policies on PROD_DB before cloning to DEV DB, ensuring data masking from the start in the DEV environment. This approach provides data protection immediately after the clone is created.
NEW QUESTION # 64
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