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質問 1：
You are tasked with ingesting a large volume of CSV files from an external stage into a Snowflake table. Some of these CSV files contain corrupted records with inconsistent delimiters or missing values. You need to ensure that only valid records are loaded into the table, and the corrupted records are captured for further analysis. Which of the following COPY INTO options would BEST address this requirement?
A. Option E
B. Option C
C. Option A
D. Option D
E. Option B
正解：A
解説: (Pass4Test メンバーにのみ表示されます)
質問 2：
You are developing a Snowpark Python application that reads data from a large Snowflake table, performs several transformations, and then writes the results back to a new table. You notice that the write operation is taking significantly longer than the read and transformation steps. The target table is not clustered. Which of the following actions, either individually or in combination, would likely improve the write performance most significantly ?
A. Increase the size of the Snowflake warehouse used for the Snowpark session.
B. Disable auto-tuning for the warehouse to ensure consistent performance
C. Use the 'DataFrame.repartition(numPartitions)' method before writing to the table. Choose a 'numPartitionS value that is significantly higher than the number of virtual warehouses in your warehouse size.
D. Use the FILE SIZE', value)' method to reduce the size of the output files, potentially leading to more parallelism during the write operation.
E. Cluster the target table on the primary key before writing to it. Then, ensure the data being written is pre-sorted according to the clustering key.
正解：E
解説: (Pass4Test メンバーにのみ表示されます)
質問 3：
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 'COPY_HISTORY view and the view in 'ACCOUNT_USAG Also, check the S3 bucket for throttling errors.
B. Query the ' LOAD_HISTORY function and monitor the network latency between S3 and Snowflake using an external monitoring tool.
C. Use Snowflake's partner connect integrations to monitor the virtual warehouse resource consumption and query the 'VALIDATE function to ensure data quality before loading.
D. Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and monitor CPU utilization of the virtual warehouse using the Snowflake web I-Jl.
E. Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and enable Snowflake's query profiling for the COPY INTO statement.
正解：A,E
解説: (Pass4Test メンバーにのみ表示されます)
質問 4：
A Snowflake table 'SALES DATA' is frequently updated via Snowpipe. Historical data is occasionally queried using time travel. You notice that time travel queries are becoming increasingly slow. Which of the following Snowflake features or techniques would BEST address this performance degradation?
A. Decrease the parameter for the 'SALES_DATA' table.
B. Increase the parameter for the 'SALES_DATX table.
C. Periodically clone the ' SALES DATA' table to a separate historical table.
D. Create a materialized view that pre-computes the results of common time travel queries.
E. Implement data clustering on a column that is frequently used in time travel query filters.
正解：E
解説: (Pass4Test メンバーにのみ表示されます)
質問 5：
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 '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.
B. 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.
C. Snowflake event tables provide complete audit trail capabilities. These tables capture all the events including policies.
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'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.
正解：B
解説: (Pass4Test メンバーにのみ表示されます)
質問 6：
You are responsible for monitoring data quality in a Snowflake data warehouse. Your team has identified a critical table, 'CUSTOMER DATA, where the 'EMAIL' column is frequently missing or contains invalid entries. You need to implement a solution that automatically detects and flags these anomalies. Which of the following approaches, or combination of approaches, would be MOST effective in proactively monitoring the data quality of the 'EMAIL' column?
A. Utilize an external data quality tool (e.g., Great Expectations, Deequ) to define and run data quality checks on the 'CUSTOMER DATA' table, integrating the results back into Snowflake for reporting and alerting.
B. Implement a Streamlit application connected to Snowflake that visualizes the percentage of NULL and invalid 'EMAIL' values over time, allowing the team to manually monitor trends.
C. Use Snowflake's Data Quality features (if available) to define data quality rules for the 'EMAILS column, specifying acceptable formats and thresholds for missing values. Configure alerts to be triggered when these rules are violated.
D. Create a Snowflake Task that executes a SQL query to count NULL 'EMAIL' values and invalid 'EMAIL' formats (using regular expressions). The task logs the results to a separate monitoring table and alerts the team if the count exceeds a predefined threshold.
E. Schedule a daily full refresh of the 'CUSTOMER DATA' table from the source system, overwriting any potentially corrupted data.
正解：A,C,D
解説: (Pass4Test メンバーにのみ表示されます)
質問 7：
You are designing a data ingestion process that involves loading data from an external stage. The data is partitioned into multiple files based on date. The stage is configured to point to the root directory of the partitioned dat a. You want to efficiently load only the data for a specific date (e.g., '2023-01-15') using the 'COPY' command. Assume your stage name is 'my _ stage' , your table is 'my_table', your date column is named 'event_date', and the files in the stage are named in the format 'data YYYY-MM-DD.csv'. Which of the following options allows you to selectively load the data for the specific date? (Select ALL that apply)
A. Option E
B. Option C
C. Option A
D. Option D
E. Option B
正解：A,C,E
解説: (Pass4Test メンバーにのみ表示されます)
質問 8：
You have data residing in AWS S3 in Parquet format, which is updated daily with new columns being added occasionally. The data is rarely accessed, but when it is, it needs to be queried using SQL within Snowflake. You want to minimize storage costs within Snowflake while ensuring the data can be queried without requiring manual table schema updates every time a new column is added to the S3 data'. Which approach is MOST suitable?
A. Option E
B. Option C
C. Option A
D. Option D
E. Option B
正解：E
解説: (Pass4Test メンバーにのみ表示されます)
You are tasked with ingesting a large volume of CSV files from an external stage into a Snowflake table. Some of these CSV files contain corrupted records with inconsistent delimiters or missing values. You need to ensure that only valid records are loaded into the table, and the corrupted records are captured for further analysis. Which of the following COPY INTO options would BEST address this requirement?
A. Option E
B. Option C
C. Option A
D. Option D
E. Option B
正解：A
解説: (Pass4Test メンバーにのみ表示されます)
質問 2：
You are developing a Snowpark Python application that reads data from a large Snowflake table, performs several transformations, and then writes the results back to a new table. You notice that the write operation is taking significantly longer than the read and transformation steps. The target table is not clustered. Which of the following actions, either individually or in combination, would likely improve the write performance most significantly ?
A. Increase the size of the Snowflake warehouse used for the Snowpark session.
B. Disable auto-tuning for the warehouse to ensure consistent performance
C. Use the 'DataFrame.repartition(numPartitions)' method before writing to the table. Choose a 'numPartitionS value that is significantly higher than the number of virtual warehouses in your warehouse size.
D. Use the FILE SIZE', value)' method to reduce the size of the output files, potentially leading to more parallelism during the write operation.
E. Cluster the target table on the primary key before writing to it. Then, ensure the data being written is pre-sorted according to the clustering key.
正解：E
解説: (Pass4Test メンバーにのみ表示されます)
質問 3：
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 'COPY_HISTORY view and the view in 'ACCOUNT_USAG Also, check the S3 bucket for throttling errors.
B. Query the ' LOAD_HISTORY function and monitor the network latency between S3 and Snowflake using an external monitoring tool.
C. Use Snowflake's partner connect integrations to monitor the virtual warehouse resource consumption and query the 'VALIDATE function to ensure data quality before loading.
D. Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and monitor CPU utilization of the virtual warehouse using the Snowflake web I-Jl.
E. Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and enable Snowflake's query profiling for the COPY INTO statement.
正解：A,E
解説: (Pass4Test メンバーにのみ表示されます)
質問 4：
A Snowflake table 'SALES DATA' is frequently updated via Snowpipe. Historical data is occasionally queried using time travel. You notice that time travel queries are becoming increasingly slow. Which of the following Snowflake features or techniques would BEST address this performance degradation?
A. Decrease the parameter for the 'SALES_DATA' table.
B. Increase the parameter for the 'SALES_DATX table.
C. Periodically clone the ' SALES DATA' table to a separate historical table.
D. Create a materialized view that pre-computes the results of common time travel queries.
E. Implement data clustering on a column that is frequently used in time travel query filters.
正解：E
解説: (Pass4Test メンバーにのみ表示されます)
質問 5：
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 '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.
B. 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.
C. Snowflake event tables provide complete audit trail capabilities. These tables capture all the events including policies.
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'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.
正解：B
解説: (Pass4Test メンバーにのみ表示されます)
質問 6：
You are responsible for monitoring data quality in a Snowflake data warehouse. Your team has identified a critical table, 'CUSTOMER DATA, where the 'EMAIL' column is frequently missing or contains invalid entries. You need to implement a solution that automatically detects and flags these anomalies. Which of the following approaches, or combination of approaches, would be MOST effective in proactively monitoring the data quality of the 'EMAIL' column?
A. Utilize an external data quality tool (e.g., Great Expectations, Deequ) to define and run data quality checks on the 'CUSTOMER DATA' table, integrating the results back into Snowflake for reporting and alerting.
B. Implement a Streamlit application connected to Snowflake that visualizes the percentage of NULL and invalid 'EMAIL' values over time, allowing the team to manually monitor trends.
C. Use Snowflake's Data Quality features (if available) to define data quality rules for the 'EMAILS column, specifying acceptable formats and thresholds for missing values. Configure alerts to be triggered when these rules are violated.
D. Create a Snowflake Task that executes a SQL query to count NULL 'EMAIL' values and invalid 'EMAIL' formats (using regular expressions). The task logs the results to a separate monitoring table and alerts the team if the count exceeds a predefined threshold.
E. Schedule a daily full refresh of the 'CUSTOMER DATA' table from the source system, overwriting any potentially corrupted data.
正解：A,C,D
解説: (Pass4Test メンバーにのみ表示されます)
質問 7：
You are designing a data ingestion process that involves loading data from an external stage. The data is partitioned into multiple files based on date. The stage is configured to point to the root directory of the partitioned dat a. You want to efficiently load only the data for a specific date (e.g., '2023-01-15') using the 'COPY' command. Assume your stage name is 'my _ stage' , your table is 'my_table', your date column is named 'event_date', and the files in the stage are named in the format 'data YYYY-MM-DD.csv'. Which of the following options allows you to selectively load the data for the specific date? (Select ALL that apply)
A. Option E
B. Option C
C. Option A
D. Option D
E. Option B
正解：A,C,E
解説: (Pass4Test メンバーにのみ表示されます)
質問 8：
You have data residing in AWS S3 in Parquet format, which is updated daily with new columns being added occasionally. The data is rarely accessed, but when it is, it needs to be queried using SQL within Snowflake. You want to minimize storage costs within Snowflake while ensuring the data can be queried without requiring manual table schema updates every time a new column is added to the S3 data'. Which approach is MOST suitable?
A. Option E
B. Option C
C. Option A
D. Option D
E. Option B
正解：E
解説: (Pass4Test メンバーにのみ表示されます)
宫泽** -
DEA-C02初心者でも分かり易いと感じました。
図や表も多いのでわかりやすいです。