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Snowflake SnowPro Advanced: Data Analyst Certification Exam 認定 DAA-C01 試験問題 (Q91-Q96):

質問 # 91
You have a Snowflake table named 'CUSTOMER DATA' with a 'JSON DATA' column containing nested JSON objects. You need to extract specific fields from the nested JSON, transform the data, and load it into a new table named 'CLEANED CUSTOMERS'. You want to automate this process using a task and stream. Which of the following SQL statements, when combined correctly, provide the most efficient and reliable solution for automating this data transformation?

A. Option A
B. Option B
C. Option D
D. Option C
E. Option E

正解：C

解説：
The correct answer (D) uses a stream to capture changes in the 'CUSTOMER_DATX table. The task is chained to the stream using 'AFTER ensuring it runs only when there are changes in the stream. It then inserts the transformed data (name, age) from the stream into 'CLEANED_CUSTOMERS' , filtering based on Options A and C don't leverage streams and will re-process the entire 'CUSTOMER DATA table each time the task runs. Option B uses a MERGE statement without considering any audit columns from the stream and option E sets SHOW INITIAL ROWS = TRUE on stream that are deprecated and doesn't ensure task execution correctly because task doesn't use the stream state properly. A stream is crucial for incremental loading and efficient processing of only the changed data.

質問 # 92
Which of the following statements regarding Secure Views and Materialized Views in Snowflake are CORRECT? (Choose two)

A. Secure Views incur a performance overhead compared to standard views, while Materialized Views generally improve query performance for suitable workloads.
B. Secure Views can be used in conjunction with Row Access Policies to control data visibility at a granular level. Materialized views cannot be used in conjunction with row access policies.
C. Secure Views obfuscate the underlying query logic from users, while Materialized Views store pre-computed data for faster query performance.
D. Both Secure Views and Materialized Views can be automatically refreshed based on data changes in the underlying tables.
E. Materialized Views can only be created on tables, not on other views or functions.

正解：A、C

解説：
Secure Views hide query logic and data lineage, improving security. Materialized Views pre-compute and store data, improving performance. Secure Views can have a performance overhead as additional checks are performed. Materialized Views are automatically refreshed in Snowflake. Materialized views cannot be created with User-Defined Functions in the Select List or Where Clause. Row access policies are not supported on materialized views.

質問 # 93
You are working on a data ingestion pipeline that loads data from a CSV file into a Snowflake table called The CSV file occasionally contains invalid characters in the 'Email' column (e.g., spaces, non-ASCII characters). You want to ensure data integrity and prevent the entire load from failing due to these errors. Which of the following strategies, used in conjunction, would BEST handle this situation during the COPY INTO command and maintain data quality?

A. Use the ERROR = 'SKIP FILE" option in the 'COPY INTO' command along with a file format that specifies 'TRIM SPACE = TRUE and 'ENCODING ='UTF8".
B. Use the 'ON ERROR = 'SKIP FILE" option in the 'COPY INTO' command and then run a subsequent SQL query to identify and correct any invalid email addresses in the 'EmployeeData' table.
C. Use a file format with 'VALIDATE UTF8 = TRUE, and 'ON ERROR='SKIP FILE". Create a separate stage containing invalid data to be handled at a later stage with another transformation job
D. Use the 'ON ERROR = 'CONTINUE" option in the 'COPY INTO' command. Create a separate error queue table and configure the 'COPY INTO' command to automatically insert error records into the queue.
E. Employ the 'VALIDATE function during the 'COPY INTO command to identify erroneous Email columns and use the 'ON ERROR = 'CONTINUE" along with using file format that specifies 'TRIM_SPACE = TRUE and ENCODING = 'UTF8".

正解：D、E

解説：
Options B and E provide the most robust solution. ERROR = 'CONTINUE'' allows the load to proceed despite errors. Creating an error queue (implicitly handled by Snowflake if using allows you to examine and address the problematic records later. By including in the file format definition = TRUE' and 'ENCODING = 'UTF8" and 'VALIDATE' function during the 'COPY INTO command to identify erroneous Email columns, you can standardize character encoding. 'SKIP_FILE (options A, C, and D) might lose valuable data. While correcting data with SQL after the load (option C) is possible, capturing the error data directly during the load is more efficient.

質問 # 94
A data analyst observes a sudden and significant drop in sales for a particular product category within a Snowflake database. Initial investigations point to a possible data quality issue. Which of the following steps provides the MOST effective and efficient diagnostic approach using Snowflake features to pinpoint the root cause of the anomaly, focusing on data integrity?

A. Rebuild the entire product sales table from the raw data source to ensure data consistency, as the drop in sales suggests widespread data corruption. Compare the rebuilt table with the current table.
B. Create a clone of the sales table. Run a full table scan on the clone and perform descriptive statistics on the cloned data to detect statistical outliers without impacting the production environment. Compare with known good statistics.
C. Analyze query history in Snowflake to identify any recent DML (Data Manipulation Language) operations (e.g., UPDATE, DELETE) performed on the sales table, focusing on operations targeting the affected product category. Correlate these operations with the timeframe of the sales drop.
D. Disable all ETL processes and re-ingest the sales data overnight. This will overwrite the potential problematic sales data and resolve the drop in sales.
E. Utilize Snowflake's Time Travel feature to compare the data before and after the suspected anomaly. Query both datasets and perform set operations like EXCEPT or MINUS to identify changed records within the product category. Analyze those changed records for patterns.

正解：C、E

解説：
Options A and C are the most effective. A utilizes Time Travel for direct data comparison before and after the incident, allowing for focused analysis on changed records. C investigates DML operations, which could directly explain data changes. Option B is inefficient and disruptive. Option D, while helpful, might not pinpoint the cause of a data corruption issue as fast as A and C. Option E is a poor solution, as it doesn't identify the root cause of the issue and it leads to potential data lost from the transactions between the last successful load and when the ETL processes were disabled.

質問 # 95
You are using Snowpipe to continuously load data from an external stage (AWS S3) into a Snowflake table named 'RAW DATA. You notice that the pipe is frequently encountering errors due to invalid data formats in the incoming files. You need to implement a robust error handling mechanism that captures the problematic records for further analysis without halting the pipe's operation. Which of the following approaches is the MOST effective and Snowflake-recommended method to achieve this?

A. Utilize Snowpipe's 'VALIDATION_MODE' parameter set to to identify and handle invalid records. This requires modification of the COPY INTO statement to redirect errors to an error table.
B. Implement a custom error logging table and modify the Snowpipe's COPY INTO statement to insert error records into this table using a stored procedure called upon failure.
C. Configure Snowpipe's 'ON_ERROR parameter to 'CONTINUE' and rely on the 'SYSTEM$PIPE_STATUS' function to identify files with errors. Then, manually query those files for problematic records.
D. Disable the Snowpipe and manually load data using a COPY INTO statement with the 'ON_ERROR = 'SKIP_FILE" option, then manually inspect the skipped files.
E. Implement Snowpipe's 'ERROR _ INTEGRATION' object, configuring it to automatically log error records to a designated stage location in JSON format for later analysis. This requires updating the pipe definition.

正解：E

解説：
Snowflake's 'ERROR INTEGRATION' feature, when configured with a pipe, automatically logs details of records that fail during ingestion to a specified stage. This provides a structured and readily accessible log of errors without interrupting the data loading process. Option A is not a native feature. Option B, while potentially usable, doesn't directly integrate with pipes as the PRIMARY mechanism. Option C involves more manual intervention and doesn't offer structured error logging. Option E defeats the purpose of automated loading via Snowpipe.

質問 # 96
......

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DAA-C01学習ツールについて学習した後、実際の試験を刺激することの重要性が徐々に認識されます、ここには、私たちは君の需要に応じます、かなり便利です、多くの受験生が利用してからとても良い結果を反映しました。

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更新するDAA-C01日本語試験-試験の準備方法-素晴らしいDAA-C01模擬対策

DAA-C01模擬対策 & DAA-C01模擬対策問題

Snowflake SnowPro Advanced: Data Analyst Certification Exam 認定 DAA-C01 試験問題 (Q91-Q96):

DAA-C01試験の準備方法｜最高のDAA-C01日本語試験｜高品質なSnowPro Advanced: Data Analyst Certification Exam模擬対策

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