DEA-C02最も有効な質問と解答で勉強

You are tasked with loading data from a set of highly nested JSON files into Snowflake. Some files contain an inconsistent structure where a particular field might be a string in some records and an object in others. You want to avoid data loss and ensure that you capture both string and object representations of the field. What is the most efficient approach to achieve this, minimizing data transformation outside of Snowflake?

• A. Define the field as a VARCHAR in an internal stage and use a COPY INTO statement with the VALIDATE function to identify records with object representations. Load the valid VARCHAR values. Create a separate table for the invalid object representations identified during validation.
• B. Use a single external table with the field defined as VARIANT. During data loading, use the TRY CAST function within a SELECT statement to convert the field to VARCHAR when possible,V otherwise retain the VARIANT representation. Handle further processing in subsequent views or queries.
• C. Pre-process the JSON files using a scripting language (e.g., Python) to transform object representations to string representations before loading them into Snowflake. This ensures consistent data type for the field.
• D. Create two separate external tables, one with the field defined as VARCHAR and another with the field defined as VARIANT. Load data into both, then UNION the results in a view.
• E. Define the field in the external table as VARCHAR. During data loading, use a UDF written in Python or Java to handle the different data types, transforming objects to strings. This approach requires deploying the UDF to Snowflake.

You are responsible for monitoring the performance of a Snowflake data pipeline that loads data from S3 into a Snowflake table named 'SALES DATA. You notice that the COPY INTO command consistently takes longer than expected. You want to implement telemetry to proactively identify the root cause of the performance degradation. Which of the following methods, used together, provide the MOST comprehensive telemetry data for troubleshooting the COPY INTO performance?

• A. Query the ' LOAD_HISTORY function and monitor the network latency between S3 and Snowflake using an external monitoring tool.
• B. Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and monitor CPU utilization of the virtual warehouse using the Snowflake web I-Jl.
• C. Query the 'COPY_HISTORY view and the view in 'ACCOUNT_USAG Also, check the S3 bucket for throttling errors.
• D. Use Snowflake's partner connect integrations to monitor the virtual warehouse resource consumption and query the 'VALIDATE function to ensure data quality before loading.
• E. Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and enable Snowflake's query profiling for the COPY INTO statement.

You are implementing a data share between two Snowflake accounts. The provider account wants to grant the consumer account access to a function that returns anonymized customer data based on a complex algorithm. The provider wants to ensure that the consumer cannot see the underlying implementation details of the anonymization algorithm. Which of the following approaches can achieve this goal? (Select TWO)

• A. Create a standard UDF in the provider account and grant usage on the UDF to the share. Share the share with the consumer account.
• B. Create a view that calls the secure UDF and share that view with the consumer account.
• C. Create a secure UDF in the provider account and grant usage on the secure UDF to the share. Share the share with the consumer account.
• D. Create an external function in the provider account and grant usage to the share. Share the share with the consumer account.
• E. Share the underlying table and provide the consumer account with the anonymization algorithm separately.

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. 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.
• B. Load the data directly into a 'VARIANT column. Use a SQL transformation with 'FLATTEN' and masking policies on the extracted columns.
• C. 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
• D. 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.
• E. Use a 'COPY command with 'ON ERROR = SKIP FILE'. Use a Task to monitor load failures and trigger alerts.

You have a table named 'EVENT LOGS with columns including 'EVENT ID, 'EVENT TIMESTAMP', 'USER ID, 'EVENT_TYPE, and 'EVENT DATA (which stores JSON data). Users frequently query the table filtering by specific key-value pairs within the 'EVENT DATA column. Which of the following approaches will BEST improve query performance when filtering on values inside the JSON column, considering the use of search optimization?

• A. Extract the frequently queried key-value pairs from the 'EVENT_DATR JSON into separate virtual columns and enable search optimization on these virtual columns.
• B. Convert the ' EVENT_DATX column to a VARCHAR column and enable search optimization on it.
• C. Enable search optimization directly on the 'EVENT DATA' column.
• D. Increase the warehouse size.
• E. Create a materialized view that extracts the key-value pairs from the ' EVENT_DATX column and enable search optimization on the materialized view's columns.