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100% Pass Snowflake First-grade DEA-C02 SnowPro Advanced: Data Engineer (DEA-C02) Trustworthy Exam TorrentThe crucial thing when it comes to appearing a competitive exam like DEA-C02 knowing your problem-solving skills. And to do that you are going to need help from a DEA-C02 practice questions or braindumps. This is exactly what is delivered by our DEA-C02 test materials. The DEA-C02 Exam Dumps cover every topic of the actual Snowflake certification exam. The DEA-C02 exam questions are divided into various groups and the candidate can solve these questions to test his skills and knowledge. Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q284-Q289):NEW QUESTION # 284
Which of the following statements are true regarding data masking policies in Snowflake? (Select all that apply)
A. The 'CURRENT_ROLE()' function can be used within a masking policy to implement role-based data masking.
B. Data masking policies can be applied to both tables and views.
C. Different masking policies cannot be applied to different columns within the same table.
D. Once a masking policy is applied to a column, the original data is permanently altered.
E. Data masking policies are supported on external tables.
Answer: A,B,E
Explanation:
A and D are correct. Masking policies can be applied to tables and views, and the function is essential for implementing role-based masking. B is incorrect because masking policies apply dynamically at query time and don't alter the underlying data. C is incorrect; different policies can be applied to different columns. E is correct, Data masking policies are also supported on external tables.
NEW QUESTION # 285
A data engineering team is using Snowflake's data lineage features, and they need to audit changes to data masking policies applied to a table named 'EMPLOYEES'. They want to identify when a masking policy was added, modified, or removed from specific columns.
What are the recommended Snowflake features or audit logs that the data engineering team could use to get these requirements?
A. The Account Usage view 'POLICY REFERENCES coupled with 'QUERY HISTORY, filtering for 'ALTER TABLE MODIFY COLUMN SET MASKING POLICY statements and also comparing snapshots of the 'POLICY_REFERENCES' view over time.
B. The 'OBJECT DEPENDENCIES' view in the ACCOUNT USAGE schema will directly track changes related to masking policies applied to tables since that is the best place for lineage information.
C. Snowflake's native Data Lineage feature automatically captures all changes to data masking policies without any additional configuration, and those changes are then available to the data steward through the user interface.
D. The 'INFORMATION SCHEMA.POLICY REFERENCES view to determine what masking policies are currently in place. Then, combine that with the use of Snowflake's Alerting framework to get notified on the creation/removal of tables, and also on changes on the masking policies via SYSTEM$GET_PRIVILEGES() function.
E. Snowflake event tables provide complete audit trail capabilities. These tables capture all the events including policies.
Answer: A
Explanation:
The most effective method to audit changes is to combine , showing the current policy assignments, with the 'QUERY HISTORY to identify when those assignments were changed. Specifically, looking for 'ALTER TABLE ... MODIFY COLUMN ... SET MASKING POLICY statements in the 'QUERY_HISTORY will pinpoint the exact changes. Periodically comparing snapshots of 'POLICY_REFERENCES allows you to see which policies were added or removed. Account Usage views track object dependencies, but don't directly show historical changes to policy assignments. Data Lineage focuses on data flow, not policy change auditing. Though has value, it is more appropriate to combine with'QUERY_HlSTORY' since it displays the current masking policies, but will not capture the audit information required without 'QUERY HISTORY and snapshots.
NEW QUESTION # 286
You are tasked with implementing a data loading process for a table 'CUSTOMER DATA' in Snowflake. The source data is in Parquet format on Azure Blob Storage and contains personally identifiable information (PII). You must ensure that the data is loaded securely, masked during the loading process, and that only authorized users can access the unmasked data after the load. Assume you have already created a stage pointing to the Azure Blob Storage. Which of the following steps should you take to achieve this?
A. Load the data directly into a 'VARIANT column. Use a SQL transformation with 'FLATTEN' and masking policies on the extracted columns.
B. Use a 'COPY command with 'ON ERROR = SKIP FILE'. Use a Task to monitor load failures and trigger alerts.
C. Use a 'COPY command with the 'TRANSFORM' clause and JavaScript UDFs to mask the PII data during the load process. Implement masking policies on the 'CUSTOMER DATA' table to restrict access to the unmasked data.
D. Load the data without masking. Implement dynamic data masking policies on the table's PII columns using Snowflake's Enterprise edition features. Use a 'COPY' command with ERROR = CONTINUE
E. Use a 'COPY command with the 'ENCRYPTION = (TYPE = 'AZURE CSE', KEY = option to encrypt the data during load. Implement role-based access control to restrict access to the table.
Answer: C
Explanation:
Option B is the most comprehensive solution for secure data loading and PII protection. 'TRANSFORM' and JavaScript UDF masking during load prevent PII from being stored unmasked, enhancing security. Implementing masking policies provides granular control over access to the sensitive data post-load. It's more secure to avoid storing sensitive information even temporarily. Option A encrypts the data in transit but doesn't address masking. Option C requires dynamic data masking, but masking during copy is optimal. The correct way to copy and mask PII data is using a JavaScript UDF to mask the PII data during the load process, and then implement Masking Policies on the table.
NEW QUESTION # 287
You are tasked with loading Parquet files into Snowflake from an AWS S3 bucket. The Parquet files are compressed using Snappy compression and contain a complex nested schem a. Some of the columns contain timestamps with nanosecond precision. You want to create a Snowflake table that preserves the timestamp precision. Which COPY INTO statement options and table definition are MOST appropriate?
A. Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ, other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE';
B. Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ(9), other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE';
C. Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ(9), other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE' VALIDATION_MODE = RETURN_ERRORS;
D. Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ(9), other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE - - pARQUET COMPRESSION = AUTO) ON_ERROR = 'SKIP_FILE';
E. Table Definition: CREATE TABLE my_table (ts VARCHAR, other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE' = PARSE TIMESTAMP(ts));
Answer: D
Explanation:
The correct approach is to define the timestamp column with TIMESTAMP NTZ(9) to preserve nanosecond precision. Also, setting COMPRESSION = AUTO is a good practice to let Snowflake automatically detect and handle the compression type, even though Snappy is explicitly mentioned. Option A is close, but AUTO compression is preferred for robustness. B would lose precision as timestamp_ntz defaults to (0), C converts TIMESTAMP to VARCHAR which causes issues with ordering. E will throw errors but does not solve the problem.
NEW QUESTION # 288
You are tasked with migrating data from a legacy SQL Server database to Snowflake. One of the tables, 'ORDERS' , contains a column 'ORDER DETAILS that holds concatenated string data representing multiple order items. The data is formatted as 'iteml :qtyl ;item2:qty2;...'. You need to transform this string data into a JSON array of objects, where each object represents an item with 'name' and 'quantity' fields. Which of the following steps and functions would you use in Snowflake to achieve this transformation, in addition to loading the data?
A. Utilize a Java UDF to parse the string and directly generate the JSON array.
B. Use to split the string into rows, then use 'SPLIT to separate item name and quantity, and finally use 'OBJECT_CONSTRUCT and to create the JSON array.
C. Use 'SPLIT with ';' as delimiter, then apply 'SPLIT again with ':' as delimiter. Finally, construct the JSON array using 'ARRAY_AGG' and 'OBJECT CONSTRUCT
D. Use ' to extract item names and quantities, then use 'ARRAY_CONSTRUCT and 'OBJECT_CONSTRUCT to create the JSON array.
E. Use ' STRTOK TO ARRAY' to split the string into an array, then iterate through the array using a JavaScript UDF to create the JSON objects.
Answer: B,C
Explanation:
Options A and D correctly outline the process. (A) and multiple 'SPLIT calls (D) are valid approaches to break down the concatenated string. Then, 'OBJECT_CONSTRUCT builds the individual JSON objects, and aggregates them into a JSON array. While Javascript or Java UDFs (C, E) could solve the problem, they are generally less efficient than Snowflake's built-in functions. (B) might work but is overkill for this simple splitting task, also you would still need to combine the extracted arrays for items and quantities.
NEW QUESTION # 289
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