About the Exam

This Snowflake certification validates advanced knowledge and skills used to apply comprehensive data engineering principles using Snowflake. It is aimed at data engineers with 2 or more years of hands-on experience in a production environment. The exam covers sourcing data from data lakes, APIs, and on-premises systems, transforming and sharing data across cloud platforms, designing near real-time streams, and evaluating performance metrics. Passing demonstrates you can design scalable Snowflake data engineering solutions for production workloads.

Exam Topics

  • Ingest and Process Data20%
  • Implement Data Pipelines25%
  • Transform Data20%
  • Design and Manage Snowflake Resources and Performance20%
  • Implement Data Protection and Recovery15%

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Last updated July 9, 2026 at 9:52 PM

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QuestionQ1

Implement Data Pipelines

A company wants to obtain data from a Snowflake Marketplace provider for data-enrichment purposes. A Data Engineer must build a data pipeline that blends existing data in the company’s account with the provider’s data, including some transformations. The provider’s data is unavailable in the region where the company account is located.

How can the Data Engineer make the data available to the company for processing?

Explanation

A full Snowflake account in the provider’s region can consume the Marketplace data; a reader account cannot serve this purpose because it can consume data only from the account that created it. Replicating the acquired data to the company account makes it available alongside the company’s existing data, where the pipeline can transform and blend it. Snowflake’s cross-region sharing guidance describes replication as the mechanism for making data available across regions and cloud platforms.

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QuestionQ2

Transform Data

How can the following relational data be converted into semi-structured data with the least operational overhead?

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Explanation

OBJECT_CONSTRUCT creates a Snowflake OBJECT directly from relational column values; with a wildcard, it uses column names as object keys. This produces one semi-structured object per row without first serializing or parsing JSON, or converting each column individually.

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QuestionQ3

Ingest and Process Data

Which command successfully loads data into the table named finance_dept_location?

Explanation

Snowflake supports loading selected staged-file columns through a COPY INTO ... FROM (SELECT ...) transformation. A transformation cannot use WHERE or LIMIT, and VALIDATION_MODE is unsupported for transformed COPY loads and performs validation rather than loading data.

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QuestionQ4

Ingest and Process Data

When loading data from a CSV file with SnowSQL, which copy option removes the header record from the data being loaded?

Explanation

SKIP_HEADER specifies the number of lines at the beginning of a CSV file that Snowflake skips during loading. Setting it to 1 excludes one header record from the loaded data.

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QuestionQ5

Implement Data Protection and Recovery

A Data Engineer is designing a Snowflake SQL API for calling a stored procedure with the following code:

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When a request is sent, it must be authenticated.

Which authentication techniques meet this requirement?

Choose two
Explanation

Snowflake SQL API requests can authenticate programmatically with an OAuth bearer token or with key-pair authentication, which uses a JWT generated from an assigned public/private key pair. MFA and browser-based SSO methods are not the SQL API request authentication methods.

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