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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis |
| Topic 2: Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion |
| Topic 3: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Topic 4: Prepare and Present Data | 10–15% | - Data visualization and reporting - Snowsight dashboards and sharing results - Align outputs with business requirements |
| Topic 5: Prepare and Load Data | 15–20% | - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro - External tables and data validation |
| Topic 6: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Complex joins, subqueries, window functions - Semi-structured data processing |
| Topic 7: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
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NEW QUESTION # 21
You are responsible for a Snowflake data pipeline that loads data from various external sources into a data warehouse. You have implemented Snowpipe for continuous data ingestion from AWS S3. One of your data sources is known to occasionally produce corrupted data files. You need to design a resilient pipeline that can handle these corrupted files without halting the entire data loading process and allows for the identification and quarantine of these files for further investigation. Assume you have a table called 'RAW DATA' and you are using a file format 'MY CSV FORMAT. Select TWO actions which best address this situation:
Answer: A,C
Explanation:
Option B directly addresses the requirement for a resilient pipeline by utilizing 'ERROR INTEGRATION' to log errors and quarantine corrupt files. Setting ON_ERROR to CONTINUE avoids pipeline interruption, and creating a task to query the error stage and move files to a quarantine bucket achieves the desired outcome. Option C would be the 2nd best option because it provides for pre-processing and validation, which avoids loading the corrupted records into the RAW_DATA table in the first place. Option A only skips the file and doesn't log the errors or quarantine the file. Option D creates a stream on the RAW DATA table, but we would like to avoid writing erroneous records to the RAW DATA table in the first place. 'VALIDATE only checks the existing data, so it would not solve any problems in Data Load.
NEW QUESTION # 22
How do constraints, such as primary keys, impact table joins between parent/child tables in Snowflake?
Answer: D
Explanation:
Primary keys enforce data uniqueness, ensuring integrity and maintaining relationships between parent and child tables in Snowflake.
NEW QUESTION # 23
Consider a table 'customer_orderS with columns 'order_id' (INT), 'customer_id' (INT), 'order_date' (DATE), and 'order_total' (NUMBER). The table is partitioned by 'order_date'. You need to create a materialized view that summarizes the total order value per customer, per month. Which of the following materialized view definitions will both achieve the desired summarization and effectively leverage partition pruning for efficient refreshes?
Answer: E
Explanation:
Option A is the best choice. It groups by and 'DATE TRUNC('month', order_datey , which creates a monthly aggregation. Since the base table is partitioned by 'order_date' , the materialized view refresh can efficiently use partition pruning based on the monthly date truncations. Option B groups by the full 'order_date' , leading to daily aggregations instead of monthly. Option C uses , which is less efficient and doesn't preserve the year information for partition pruning. Option D uses "CONVERT_TIMEZONE' , which is incorrect for the purpose and will not efficiently leverage partition pruning.Option E using CAST(order_date AS VARCHAR(7)) as it change the date data type.
NEW QUESTION # 24
You are tasked with loading a large CSV file containing website traffic data into Snowflake. The CSV file has the following characteristics: Header row is present. Fields are enclosed in double quotes. The delimiter is a pipe (l) character. One column, 'timestamp' , is stored as milliseconds since the epoch and needs to be converted to a Snowflake TIMESTAMP NTZ. Which of the following COPY INTO statement options would correctly load the data, handle the delimiter and quotes, and convert the 'timestamp' column?
Answer: D
Explanation:
Option E is correct because it correctly handles the milliseconds since epoch conversion. It casts the 'timestamp' column to a to ensure accurate division and then divides by 1000 to convert milliseconds to seconds before applying TO_TIMESTAMP_NTZ. Option A is incorrect because timestamp could be a String. Option B is incorrect as date format is not relevant here and it doesn't divide by 1000. Option C is incorrect because BIGINT might not be sufficient for large timestamps. Option D is incorrect because = 'EPOCH MILLIS" is used in file format options, not transform column.
NEW QUESTION # 25
When managing Snowsight dashboards, what significance do subscriptions and updates hold in meeting business requirements?
Answer: D
Explanation:
Subscriptions and updates ensure timely information delivery, meeting business requirements.
NEW QUESTION # 26
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