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SnowPro Advanced: Data Analyst (DAA-C01) — Practice Questions and Mock Exam

Name: Pass-IT
Author: Pass-IT

Prepare for SF-DA with original practice questions and clear answer explanations. Ask Alex, your AI tutor, when you need more detail, use your results to identify topics to review, and practice your pacing with timed mock exams.

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65Mock exam questions

115minTime limit

750/ 1000Passing score

Checked against Snowflake · August 2026 · Current exam version

Overview

About the exam

The SnowPro Advanced: Data Analyst Certification (DAA-C01) validates the ability to perform end-to-end data analysis on Snowflake, including data ingestion and preparation, transformation and modeling, advanced analytical techniques, and data presentation and visualization. It tests proficiency in turning raw data into actionable business insights using Snowflake's analytical capabilities and connected visualization tools.

This certification is for data analysts, business intelligence developers, and reporting specialists with one or more years of Snowflake analytics experience. It demonstrates the ability to design analytical workflows, build data models for reporting, and present findings from Snowflake data.

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Try five SF-DA questions

Try five practice questions from the app’s current SnowPro Advanced: Data Analyst (DAA-C01) question bank, with answers and explanations.

Data Transformation and Data Modeling1 / 5

An analyst uses CONNECT BY to query an employee hierarchy table. Which pseudo-column indicates the depth of each node relative to the root in the traversal?

AlexFull explanation from Alex

LEVEL is the correct pseudo-column for depth in CONNECT BY hierarchical queries. It returns 1 for root nodes and increments by 1 for each deeper level. Example: SELECT employee_id, LEVEL FROM employees START WITH manager_id IS NULL CONNECT BY PRIOR employee_id = manager_id. Distractors: DEPTH(), SYS_TREE_DEPTH, and CONNECT_BY_DEPTH are fabricated—none exist in Snowflake. Other valid CONNECT BY pseudo-columns include CONNECT_BY_ROOT (root row value) and SYS_CONNECT_BY_PATH (full path string), but only LEVEL indicates depth. Ref: docs.snowflake.com/en/sql-reference/constructs/connect-by

Sourcedocs.snowflake.com

Data Ingestion and Data Preparation2 / 5

Which function should an analyst use to automatically generate a CREATE TABLE statement that matches the schema of Parquet files in a stage?

AlexFull explanation from Alex

GENERATE_COLUMN_DESCRIPTION takes the ARRAY_AGG output of INFER_SCHEMA and formats it into column definitions for CREATE TABLE, CREATE EXTERNAL TABLE, or CREATE ICEBERG TABLE. INFER_SCHEMA detects column names and types from staged Parquet, Avro, ORC, or CSV (with PARSE_HEADER) files. Workflow: SELECT GENERATE_COLUMN_DESCRIPTION(ARRAY_AGG(OBJECT_CONSTRUCT(*)), 'table') FROM TABLE(INFER_SCHEMA(LOCATION=>'@stage', FILE_FORMAT=>'fmt')). Distractors: DESCRIBE STAGE shows stage properties, not file schemas. SHOW COLUMNS lists existing table columns. GET_DDL returns DDL for existing objects, not staged files. Ref: docs.snowflake.com/en/sql-reference/functions/generate_column_description

Sourcedocs.snowflake.com

Data Analysis4 / 5

What is the purpose of the MEASURES clause in MATCH_RECOGNIZE?

AlexFull explanation from Alex

The MEASURES clause in MATCH_RECOGNIZE defines the output columns returned for each pattern match. It supports expressions over pattern variables using functions like FIRST(), LAST(), CLASSIFIER(), MATCH_NUMBER(), and aggregates. Example: MEASURES FIRST(UP.price) AS start_price, LAST(UP.price) AS end_price. Distractors: distance metrics are not a MATCH_RECOGNIZE concept. Pattern length is not what MEASURES does—it defines output values. Min/max match counts are controlled by quantifiers in the PATTERN clause (+, *, {n,m}), not MEASURES. Ref: docs.snowflake.com/en/sql-reference/constructs/match_recognize

Sourcedocs.snowflake.com

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Question pool

403 practice questions

The Pass-IT question pool gives you material to practice for SF-DA. A Pass-IT mock exam uses 65 questions and a 115-minute time limit; these are practice settings.

Pool details: SF-DA

Exam details checked against SnowflakeAugust 14, 2026

date of the last check against the official Snowflake source

Passing score750 / 1,000

as published by Snowflake

Objectives in the guide19 objectives listed in the official guide

across 4 domains in the official exam guide

Pool size403 questions

= The pool size is equivalent to 6 sets of 65 questions; this does not mean that each mock exam uses a separate set.

Blueprint domains4 domains in the exam blueprint

Data Ingestion and Data Preparation 72 · Data Transformation and Data Modeling 101 · Data Analysis 127 · Data Presentation and Data Visualization 103

Recorded as checked against sources403 of 403

questions recorded as having their answer, options, and explanation checked against official Snowflake documentation

How questions are made →

Exam domains

What's on the exam

Data Analysis carries the most weight at 32%, testing SQL extensibility (UDFs, stored procedures, views), descriptive and diagnostic analysis, and forecasting. Data Presentation and Visualization follows close behind at 28%, covering Snowsight dashboard design, chart selection, and BI tool connectivity. Together these two domains make up 60% of the exam, more than data preparation and modeling combined.

Data Transformation and Modeling (23%) and Data Ingestion and Preparation (17%) round out the blueprint, covering data cleaning, star and Data Vault modeling, and loading structured, semi-structured, and unstructured data. The distribution favors analysts who can turn a query into a decision over analysts who only know how to load and clean data, so study time should lean toward SQL analytics and dashboard design.

Exam blueprint: SF-DA

Data Ingestion and Data Preparation17%

Ingest, stage, and prepare data using COPY, Snowpipe, external tables, and data transformation techniques.

≈ 14 h

Data Transformation and Data Modeling23%

Transform data using SQL, CTEs, window functions, and design star/snowflake schemas and data vault models.

≈ 18 h

Data Analysis32%

Perform advanced analytics using SQL, aggregate functions, time-series analysis, and Snowflake-specific analytical features.

≈ 26 h

Data Presentation and Data Visualization28%

Create dashboards, integrate with BI tools, and present analytical results using Snowsight and partner tools.

≈ 22 h

Format

Exam format and question types

The exam consists of 65 questions in 115 minutes, drawn from multiple-choice, multiple-select, and interactive formats. Most items give you a data set or business use case and ask you to choose the query, transformation, or visualization approach that meets it. At roughly 1.8 minutes per question, the pace is comfortable if your SQL is fluent going in.

Question types: SF-DA

Multiple Choice70%

Select the single answer that best meets the question’s requirements.

Multiple Response30%

Select multiple answers. Follow the question’s instructions on how many to choose.

See Snowflake for official question-format information. The shares shown describe the Pass-IT practice pool; they do not establish the proportions on the official exam.

Planning

Preparing for SF-DA

Delivered by online proctoring or at an onsite testing center, in English. The certification expires two years after your issue date; you recertify through the Snowflake Continuing Education program with an eligible instructor-led training course or an equivalent or higher-level SnowPro certification.

Preparation and logistics: SF-DA

Preparation

Illustrative study time50–120 h

illustrative planning range: 50 h with relevant experience to 120 h when starting out; your needs may fall outside this range

LevelAdvanced

Recommended backgroundSnowPro Core Certified. 1 or more years of Snowflake data cloud analytics experience, including hands-on use of the Snowflake Data Cloud.

Taking and maintaining the certification

DeliveryOnline proctored or onsite testing centers.

Retake policyLimit of 4 attempts in a 12-month period. After three attempts Snowflake recommends attending an onsite Snowflake training course. Each registration requires the full registration fee.

Certification validity2 years

Snowflake certifications expire two years after the certification issue date. Recertify through the Snowflake Continuing Education (CE) program: complete an eligible Snowflake Instructor-Led (ILT) training course, or earn an equivalent or higher-level SnowPro certification. A valid certification is required to take part in the CE program.

Official guideView on provider site

Study notes

Common pitfalls

Topics to review: SF-DA

01Window Functions
Not mastering ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, and NTILE window functions — including PARTITION BY and ORDER BY clauses — leads to analysis question failures.
02QUALIFY Clause
Not knowing that QUALIFY filters window function results (like HAVING for GROUP BY) and is unique to Snowflake SQL causes mistakes on advanced query questions.
03Materialized Views
Confusing regular views with materialized views and not understanding when Snowflake auto-refreshes materialized views or their Enterprise Edition requirement leads to modeling errors.
04BI Tool Integration
Not understanding how Snowflake connects to Tableau, Power BI, and Looker — including partner connect, OAuth, and ODBC/JDBC drivers — causes visualization ecosystem question mistakes.
05PIVOT and UNPIVOT
Struggling with PIVOT (rows to columns) and UNPIVOT (columns to rows) syntax and not understanding dynamic pivot patterns leads to transformation question errors.

Practice with questions and explanations in Pass-IT →

Questions

Frequently asked questions

How long is the SnowPro Advanced: Data Analyst (DAA-C01) exam?

The SnowPro Advanced: Data Analyst (DAA-C01) exam has 65 questions and a 115-minute time limit.

What is the passing score for SnowPro Advanced: Data Analyst (DAA-C01)?

The passing score for the SnowPro Advanced: Data Analyst (DAA-C01) exam is 750 / 1000.

Which pitfalls should I review when preparing for SnowPro Advanced: Data Analyst (DAA-C01)?

Topics to review include Window Functions, QUALIFY Clause, Materialized Views, BI Tool Integration, PIVOT and UNPIVOT. Work through examples to check that you understand the distinctions and can explain your answer.

Which Advanced Data Analyst topics carry the most marks?

Data analysis is the largest section at 32%, with presentation and visualization at 28%, transformation and modeling at 23% and ingestion and preparation at 17%. Analysis and presentation together are six tenths of the exam. The reporting half carries more weight here than on any other SnowPro exam.

What do you need before the Advanced Data Analyst exam?

SnowPro Core certification is required, and Snowflake expects a year or more of Snowflake analytics work including hands-on use of the Data Cloud. The catalog budget is around 80 hours. The one-year expectation is lower than the two years the other advanced exams assume.

Advanced Data Analyst or Advanced Data Engineer?

The analyst exam is weighted towards analysis and presentation, which together are 60% of it. The engineer exam puts 28% on data movement and 25% on transformation, which is the pipeline side. If your output is an answer, take the analyst exam; if it is a table other people use, take the engineer one.

How long is the Advanced Data Analyst certification valid?

Two years from the issue date. Recertification runs through the Snowflake continuing-education programme rather than by resitting, either via an eligible instructor-led course or by earning an equivalent or higher SnowPro certification.

One certification, 12 months

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