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| Section | Weight | Objectives |
|---|---|---|
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Semi-structured data processing - Complex joins, subqueries, window functions |
| Prepare and Load Data | 15–20% | - External tables and data validation - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe |
| Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Align outputs with business requirements - Data visualization and reporting |
| Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets |
| Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis - Statistical summarization and trend analysis |
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NEW QUESTION # 49
You are designing a data ingestion pipeline for IoT sensor data'. Sensors transmit data in JSON format every 5 seconds. The volume of data is expected to grow exponentially. The business requires both real-time dashboards and historical trend analysis. Which of the following strategies should you employ to address these requirements, particularly focusing on optimizing for both ingestion frequency and cost?
Answer: B,E
Explanation:
This question has multiple correct answers. Option B and E addresses the need for optimizing both ingestion frequency and cost. Option B involves using Snowflake tasks for transformations, which allows for separating data for real-time and historical analysis. This optimizes query performance for both use cases. Using only Snowpipe directly into a single table is simple but doesn't provide optimization for the different query patterns, while the other options are less efficient or don't fully address the dual requirements. Option E complements using Snowpipe by using Snowflake clustering features and employing stream processing engine to perform pre-aggregation. Snowflake clustering optimize the speed of historical analysis, reducing cost of scanning large datasets.
NEW QUESTION # 50
Which query will provide this data without incurring additional storage costs?
Answer: A
Explanation:
Snowflake utilizes a unique architecture known as Zero-Copy Cloning, which allows users to create a replica of a table, schema, or entire database without physically duplicating the underlying data files (micro- partitions). When you execute the CLONE command, Snowflake simply creates new metadata that points to the existing micro-partitions of the source object.
Because the data is not physically copied, the clone operation is nearly instantaneous and, crucially, incurs no additional storage costs at the moment of creation. Storage costs only begin to accumulate for the clone when the data in the source or the clone diverges-for example, if rows are updated or deleted in the clone, Snowflake creates new micro-partitions to store the changed data while preserving the original state in the source.
Evaluating the Options:
* Option A (LIKE) only copies the column definitions and structure of the table. It does not copy the data itself, so while it doesn't incur storage, it also doesn't provide the "data" requested by the prompt.
* Option B (CTAS - Create Table As Select) performs a full deep copy of the data. This creates entirely new micro-partitions, which immediately increases the storage footprint and associated costs.
* Option D is a filtered CTAS operation. While it may result in less data than the full table, it still involves creating new physical storage for the 2019 records, thus incurring additional costs.
* Option C is the 100% correct answer. It uses the CLONE keyword, which is the specific Snowflake feature designed to provide a full dataset for dev/test environments with zero initial storage impact. This is a core competency in the Data Transformation and Data Modeling domain.
NEW QUESTION # 51
When handling erroneous data in Snowflake, what approaches can be employed to effectively address data anomalies and ensure data integrity? (Select all that apply)
Answer: A,B
Explanation:
Employing error tables to capture erroneous data for analysis and utilizing clones for isolated error resolution ensures effective handling of data anomalies and maintains data integrity in Snowflake.
NEW QUESTION # 52
A data analyst is experiencing slow query performance when joining two large tables, 'SALES' (1 billion rows) and 'CUSTOMERS' (10 million rows), on 'CUSTOMER ID. The 'SALES' table is frequently updated. The following query is used: SELECT s. , c. FROM SALES s JOIN CUSTOMERS c ON s.CUSTOMER_lD = c.CUSTOMER_lD WHERE s.SALE_DATE DATEADD(day, -30, CURRENT DATE()); Which of the following strategies would MOST effectively improve the query performance, assuming you have appropriate privileges to alter objects and cost is a concern?
Answer: A
Explanation:
Creating 'SALES LAST 30 DAYS' table and joining is the most effective strategy because it reduces the size of the SALES table significantly before the join operation. Materialized views (A) are generally effective, but the high update frequency of the SALES table could lead to significant overhead in materialized view maintenance. Standard indexes (B) are not supported in Snowflake. Search optimization (C) may not be suitable as the could have high cardinality. Clustering (D) could improve performance, but the benefits might not outweigh the cost of reclustering after frequent data loading, and it does not limit the initial size of the join.
NEW QUESTION # 53
You have a table 'product_catalog' containing a 'description' column of type TEXT, and a 'tags' column which is a VARIANT containing an array of strings representing tags associated with the product. You need to build an efficient search mechanism that allows users to find products matching specific tags. Considering scalability and performance for large catalogs, which of the following methods using table functions and Snowflake's search capabilities would be most suitable? Choose all that apply.
Answer: D,E
Explanation:
Search optimization service in Snowflake is designed to accelerate search queries and is best practice here. Using 'ARRAY on the 'tags' column lets you directly check if the array contains specific tags. Using on the 'description' column can search for specific search terms in your description. Using a 'SEARCH' clause can improve search performnce significantly. Option C and E, are both correct, since they use contains as well as the array_contains but option E includes the use of Search which is more effiecient. Option A is incorrect, as indexes are not allowed on flattened data. UDF will have performance issues. Creating a view and indexing the view is not optimal as querying directly with CONTAINS on the tags column gives faster results.
NEW QUESTION # 54
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