About the Exam

Advanced Snowflake certification for data engineers who build and operate pipelines on the platform. It covers data ingestion with Snowpipe and streams and tasks, transformation and modelling, performance and cost optimisation, Snowpark and programmatic access, data governance and security, and the monitoring and troubleshooting of production pipelines. It assumes the SnowPro Core certification and hands-on engineering experience.

Exam Topics

  • Official exam objectives/topics0–100%

How to Use This Practice Exam

  1. Browse — Read each question, select your answer, and reveal the explanation.
  2. Exam Mode — Simulate real exam conditions with a timed session and score report.
  3. Learn Mode — Spaced repetition schedules questions you struggle with for long-term retention.

Download the Full Exam PDF

Get every question and answer in a clean, printable PDF built for offline study. Purchase once, keep permanent access, and re-download the latest version anytime.

Last updated August 10, 2026 at 10:50 PM

Topic filter
Question status
Question sort
Questions per page

QuestionQ1

VerifiedOfficial exam objectives/topics

A Data Engineer is optimizing end-user query performance for automobile-shipment data in a portal web application that produces dashboards for different clients.

An internal team queries the data with order_date as a WHERE condition, and ingestion and natural clustering occur on that column. However, the application retrieves data by using client_id, warehouse, product_id, and destination_city. The Engineer must create materialized views with different cluster keys and needs to choose two columns, together with client_id, to optimize the first cluster key.

Which statements provide all the information required to select the MOST effective combination of columns for clustering?

Choose two
Explanation

Snowflake's SYSTEM$CLUSTERING_DEPTH can calculate average clustering depth for any specified parenthesized set of table columns, and a lower depth indicates better clustering for that set. Evaluating (client_id, warehouse, product_id) and (client_id, destination_city, product_id) provides the needed comparison of valid candidate keys that each include client_id plus two application access columns. SYSTEM$CLUSTERING_INFORMATION('shipments', 3) returns clustering errors, not clustering information for a proposed column combination.

Learn more

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ2

VerifiedOfficial exam objectives/topics

Which file-format option escapes the \ character when unloading table data to a stage if FIELD_DELIMITER = ','?

Explanation

ESCAPE_UNENCLOSED_FIELD specifies the escape character for unenclosed field values during data unloading. Snowflake recognizes \\ as the common escape sequence for a literal backslash, so the option value must be '\\'.

Learn more

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ3

VerifiedOfficial exam objectives/topics

A Data Engineer is designing a solution to email a customer report files that are generated and stored in a Snowflake stage. The files must be available for download for only 60 minutes because new files will be produced and the older files will become obsolete.

What is the recommended way to satisfy these requirements?

Explanation

A pre-signed URL provides direct temporary access to a staged file without Snowflake authentication, and its expiration_time can be set to 3,600 seconds (60 minutes). SYSTEM$SEND_EMAIL can send the email containing that link. Scoped URLs instead follow the persisted query-results period, currently 24 hours, while stage file URLs require stage privileges and are not temporary.

Learn more

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ4

VerifiedOfficial exam objectives/topics

A user executed a one-time AI_SUMMARIZE_AGG query on a large log table using a Cortex AI model. Although it ran on a size Small virtual warehouse, the query consumed more than 1,000 credits.

A Data Engineer must implement a Snowflake-native automated alert that fires whenever a single Cortex AI SQL query uses 300 credits, allowing the query text and selected model to be reviewed.

How should these requirements be fulfilled?

Explanation

Cortex AI SQL usage history supplies per-query usage together with model metadata and a query ID. That query ID can be joined to SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY, which contains the SQL text, while a scheduled task can send a notification when the per-query threshold is exceeded. Warehouse resource monitors and daily metering are aggregate controls, so they cannot identify the individual Cortex query and model. In the current CORTEX_AISQL_USAGE_HISTORY view, the billed-credit column is named TOKEN_CREDITS; implementations should use that current column name. CORTEX_AISQL_USAGE_HISTORY view, QUERY_HISTORY view

Learn more

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ5

VerifiedOfficial exam objectives/topics

How can a clustering key definition be used to lower the number of distinct values in a high-cardinality column?

Explanation

For a high-cardinality column, define the clustering key as an order-preserving expression on that column, such as converting a timestamp to a date. The expression reduces the number of distinct key values while retaining useful pruning behavior.

Learn more

Community Discussion

No comments yet. Be the first to start the discussion!
Know a question that should be here? Contribute to this exam
Back home