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| Section | Weight | Objectives |
|---|---|---|
| Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Query optimization and troubleshooting - Semi-structured data processing |
| Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis |
| Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Views, materialized views, CTEs - Handling NULLs and structuring datasets |
| Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Prepare and Present Data | 10–15% | - Data visualization and reporting - Align outputs with business requirements - Snowsight dashboards and sharing results |
>> Reliable DAA-C01 Exam Question <<
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NEW QUESTION # 21
You are preparing to load data from an internal stage that contains multiple CSV files into a Snowflake table. You need to ensure that only files matching a specific pattern are loaded (e.g., files starting with 'sales_'). You also need to transform the date column (located in the third position of each CSV file) from 'YYYYMMDD' to 'YYYY-MM-DD' format during the load. The target table 'SALES has columns 'product_id', and 'region'. Which combination of options correctly filters the files and transforms the date format during the load process?
Answer: B
Explanation:
Option D is correct. It uses the 'PATTERN' parameter to filter files starting with 'sales_' and ending with '.csv'. It then uses the TRANSFORM AS SELECT clause to select and transform the data. Inside the select clause, it correctly uses 'substr' to reformat the date and 'TO_DATE to convert the re-formatted string into a DATE datatype. The 'ON_ERROR = 'SKIP_FILE" is a good practice for handling potential errors. Option A has an invalid parameter declaration within the COPY INTO command and does not use PATTERN, making the filter inaccurate. Option B attempts to define date format at file format level, but date transformatinon is required and FILES filter declaration is invalid. Option C uses 'TRANSFORMATION' instead of 'TRANSFORM AS SELECT , which is incorrect and will cause an error. Option E attempts to define DATE FORMAT with the wrong TYPE and is missing concatination of DATE string.
NEW QUESTION # 22
Consider a Snowflake table 'LOG ENTRIES' with a VARIANT column 'LOG DATA' containing log messages with potentially varying structures. Some logs contain error details under the key 'error', while others do not. If 'error' exists, it contains a nested object with 'code' and 'message'. You want to extract the error 'message' when it exists, otherwise return 'No Error'. Which query accomplishes this with optimal performance?
Answer: D
Explanation:
Option D is the most robust and efficient solution. 'IF-NULLS handles cases where 'LOG DATA:error:message' is NULL (either because 'error' doesn't exist or 'message' is NULL). The explicitly casts the value to a string, ensuring consistent data type. Options A and B will return NULL when the 'error' object or the 'message' key doesn't exist without handling the null replacement. Option C will error as it does not cast value into a String from a variant data type. Option E is not valid as TRY_TO_STRING function only accepts one argument
NEW QUESTION # 23
What critical role do user-defined functions (UDFs) play in SQL for advanced data analysis?
Answer: A
Explanation:
UDFs extend SQL functionalities by enabling customized operations on data, going beyond standard SQL capabilities.
NEW QUESTION # 24
A retail company uses Snowflake to store sales data'. They have a dashboard showing daily sales trends, powered by a materialized view The sales data is updated every hour. The dashboard team reports that the dashboard sometimes shows stale data'. They want to ensure the dashboard always reflects the latest sales figures without significantly impacting warehouse costs. Which of the following strategies is MOST effective?
Answer: C
Explanation:
Scheduling a task to refresh the materialized view directly after the data load provides the best balance between data freshness and cost control. Increasing warehouse size (A) is expensive. Changing to a standard view (B) eliminates the performance benefits of the materialized view. AUTO REFRESH (D) might not refresh immediately and is less precise. Dropping and recreating (E) is highly inefficient.
NEW QUESTION # 25
How do materialized views differ from regular views in terms of data access and storage?
Answer: C
Explanation:
Materialized views provide precomputed snapshots, differentiating them from regular views.
NEW QUESTION # 26
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