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Data clustering is an example of which type of data analysis technique?
A. Predictive analysis
B. Prescriptive analysis
C. Descriptive analysis
D. Exploratory analysis
Answer: A
NEW QUESTION # 77
When working with Snowsight dashboards to summarize large data sets, what key advantage do they offer in exploratory analyses?
A. They only support basic data summarization.
B. They are limited to presenting static data sets.
C. Snowsight dashboards facilitate quick, visual comprehension of complex data.
D. Snowsight dashboards can't handle large data sets efficiently.
Answer: C
Explanation:
Snowsight dashboards aid in exploratory analysis by providing visually accessible insights into complex data, aiding quick comprehension.
NEW QUESTION # 78
A data analyst is working with a large table partitioned by (DATE type). The table contains millions of rows spanning several years. They need to optimize a query that retrieves sales data for a specific quarter of 2023. The initial query is: 'SELECT FROM sales_data WHERE EXTRACT(YEAR FROM sale_date) = 2023 AND EXTRACT(QUARTER FROM sale_date) = To improve performance using partition pruning, which of the following queries is the MOST efficient alternative?
A.
B.
C.
D.
E.
Answer: B
Explanation:
Option A is the most efficient because it directly uses the 'sale_date' column with a 'BETWEEN' clause using specific date values. This allows Snowflake to directly leverage the partition pruning based on the date range. Options B and C use functions C YEAR, 'QUARTER on the 'sale_date' column, preventing efficient partition pruning. Option D uses 'LIKE', which is not suitable for date comparisons and would likely result in a full table scan, furthermore 'LIKE operator will not work with Date Data type. Option E does not prune to a specific quarter.
NEW QUESTION # 79
You are using Snowpipe to continuously load JSON data from an external stage. Occasionally, some JSON records are malformed and cause the pipe to fail. You want to configure the pipe to skip these invalid records and continue loading valid data, while also capturing the error details for later analysis. Which approach provides the most efficient and appropriate solution for this scenario?
A. Configure the Snowpipe definition to use the 'VALIDATE(O)' function within the 'COPY INTO' statement.
B. Implement custom error handling in your application code to pre-validate JSON records before uploading them to the stage.
C. Use the 'ON ERROR = 'CONTINUE'' option in the 'COPY INTO' statement used by the Snowpipe definition in conjunction with the 'VALIDATE function to capture error details.
D. Use the = 'SKIP_FILE" option in the 'COPY INTO' statement used by the Snowpipe definition.
E. Use the 'VALIDATION MODE = RETURN ALL ERRORS parameter in the 'COPY INTO' statement and then filter the data based on the errors returned.
Answer: C
Explanation:
Option E is the most efficient and complete solution. SON ERROR = 'CONTINUE" allows Snowpipe to skip bad records and continue processing. Using it in conjunction with the 'VALIDATE function within the 'COPY INTO' statement enables capturing error information for analysis. This combines error skipping with error logging. Options A, B, C, and D are either less efficient (requiring pre-processing or post- processing of data), or do not provide a comprehensive solution for both skipping and capturing error information. Using (option B) is too coarse-grained as it skips entire files, even with only a few errors. Using 'VALIDATION MODE without ERROR=CONTINUE will still stop the pipe on errors.
NEW QUESTION # 80
You are working with a Snowflake environment where data analysts need to query Parquet files located in AWS S3. The analysts require the ability to perform complex aggregations and joins with other Snowflake tables. Some of the Parquet files have evolved their schema over time, adding new columns. Given these requirements, which set of actions would provide the MOST efficient and maintainable solution? Choose the THREE most appropriate actions.
A. Create materialized views on top of the external table, extracting frequently used columns and performing common aggregations to pre-compute results.
B. Implement a process to periodically load the Parquet files into Snowflake internal tables using 'COPY INTO , inferring the schema from the Parquet files and creatin a new table version whenever the schema changes. Create views to handle the multi le table versions.
C. Create an external table in Snowflake pointing to the S3 bucket and use the 'VALIDATE(100);' command frequently to validate if the stage data integrity
D. Develop custom SQL scripts to manually extract data from the external table into temporary tables with specific schemas, then perform aggregations and joins using those temporary tables.
E. Implement a data pipeline that continuously ingests the Parquet data into a Snowflake internal table with a defined schema, ensuring all possible columns are included and using a VARIANT type for potentially evolving fields.
F. Create an external table with 'AUTO REFRESH = TRUE' and use schema evolution features to automatically detect and accommodate new columns in the Parquet files. use parameter to tune query performance.
Answer: A,C,F
Explanation:
'AUTO REFRESH = TRUE' handles schema evolution in external tables. Materialized views on the top of external tables provides precomputed data, which is good for aggregations. The VALIDATE command allows analysts to check the stage data integrity and avoid errors in the data pipeline. Ingesting all data into a single VARIANT column is not efficient for aggregations and joins. Extracting to temporary tables is not maintainable. Periodically creating new tables is complex to manage. Adjusting can help improve performance.
NEW QUESTION # 81
......
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