DAA-C01 Sample Questions & Answers
Descriptive and diagnostic analysis using SQL's extended features carries the most weight, next to putting together and keeping up dashboards and reports, cleaning and querying data, and pulling data in through the Marketplace for enrichment.
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- Question 1Intermediate
Data Ingestion and Data Preparation · Use Secure Data Sharing to enrich existing data sets
A healthcare provider wants to share an anonymized patient outcome dataset exclusively with three specific research universities. They require strict control over who can discover the listing and consume the data to comply with regulations. Which Snowflake sharing mechanism should they use?
Show answer & explanation
Correct answer: D
Private Listings allow a provider to share data directly with specific Snowflake accounts. The listing is not visible on the public Snowflake Marketplace, providing the strict control required for sensitive healthcare data.
- Question 2Advanced
Data Ingestion and Data Preparation · Automate pipelines and respond to failures
A data engineering team at a retail company manages a complex data ingestion pipeline. Data lands in an external S3 stage, is loaded into raw tables via Snowpipe, and is transformed downstream using a series of Tasks and Streams.
Recently, PII data was discovered in a downstream aggregate table where it should not exist. The security team needs to audit exactly which users queried the raw and aggregate tables over the last 30 days. Simultaneously, the data analysts need to trace how the PII column propagated from the raw table through the intermediate tables to the aggregate table.
Which combination of Snowflake features provides the natively built-in solution for these requirements?
flowchart LR A[S3 Stage] -->|Snowpipe| B[Raw Table] B -->|Task/Stream| C[Intermediate Table] C -->|Task/Stream| D[Aggregate Table]Show answer & explanation
Correct answer: D
ACCESS_HISTORY is the native Snowflake view used to audit which users accessed what data (including reads and writes). OBJECT_DEPENDENCIES is the native view used to track data lineage, showing how objects like tables and views depend on one another, enabling analysts to trace the PII propagation.
- Question 3Beginner
Data Ingestion and Data Preparation · Keys, joins, and constraints
A data architect is designing a schema for a BI tool that heavily relies on join culling to improve performance. How should they configure PRIMARY KEY and FOREIGN KEY constraints in Snowflake to ensure the query optimizer utilizes them?
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Correct answer: B
In Snowflake, primary and foreign key constraints (except NOT NULL) are informational and not enforced. However, if the data is known to be clean, setting the RELY property on the constraint tells the Snowflake optimizer to assume the constraint is valid, enabling advanced optimizations like join culling.
- Question 4Intermediate
Data Transformation and Data Modeling · Performance tuning
An analyst runs a query filtering on a
transaction_datecolumn against a 500GB table. The Query Profile indicates that 100% of the micro-partitions were scanned, despite the query returning only 1% of the data.What is the most likely cause of this poor partition pruning?
flowchart TD A[Query Execution] --> B[TableScan] B --> C[Partitions Scanned: 15,000 / 15,000] B --> D[Rows Returned: 10,000]Show answer & explanation
Correct answer: D
Applying a function to a column in a WHERE clause (e.g.,
WHERE YEAR(transaction_date) = 2023) often prevents Snowflake from utilizing the partition metadata, resulting in a full table scan. The column should be evaluated against constants (e.g.,WHERE transaction_date >= '2023-01-01') to enable pruning. - Question 5Advanced
Data Transformation and Data Modeling · Query, aggregate, and enrich
A data analyst needs to deduplicate a table of customer events, keeping only the most recent event per customer based on the
event_timestamp. Which query efficiently accomplishes this using Snowflake's native capabilities?Show answer & explanation
Correct answer: A
The QUALIFY clause is a Snowflake extension that filters the results of window functions directly, without needing a subquery or CTE. Using ROW_NUMBER() partitioned by customer and ordered descending by timestamp perfectly isolates the single most recent record.
- Question 6IntermediateSelect 2
Data Transformation and Data Modeling · Cleaning techniques
Which of the following scenarios are appropriate use cases for Data Metric Functions (DMFs) in Snowflake? (Select TWO)
Show answer & explanation
Correct answers: B, C
Data Metric Functions (DMFs) are designed to measure data quality. Checking for NULL counts, duplicates, or value ranges natively on a schedule are standard DMF use cases.
Data Metric Functions (DMFs) are designed to measure data quality. Checking for NULL counts, duplicates, or value ranges natively on a schedule are standard DMF use cases.
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