Snowflake · ADA-C02
SnowPro Advanced: Administrator (ADA-C02) Practice Test
Validates advanced skills in managing Snowflake accounts including data security, governance, RBAC, performance tuning, data sharing, and disaster recovery. Designed for administrators with 2+ years of hands-on Snowflake production experience.
Exam Details
Questions
600
Duration
115 minutes
Passing Score
750/1000
Difficulty
ProfessionalLast Updated
Jun 2026
Topics Covered
Snowflake Security, RBAC, and User AdministrationAuthentication (SSO, MFA, OAuth, Key Pair)Network Policies and Private ConnectivityData Governance and Access ControlDatabase Object and Virtual Warehouse ManagementQuery Performance Monitoring and OptimizationData Sharing and Snowflake MarketplaceDisaster Recovery, Backup, and Data Replication
SnowPro Advanced: Administrator (ADA-C02) Practice Exam Preparation
Use this ADA-C02 practice exam to prepare for SnowPro Advanced: Administrator (ADA-C02) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 600 questions for Snowflake ADA-C02, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to recurring topics such as Snowflake Security, RBAC, and User Administration, Authentication (SSO, MFA, OAuth, Key Pair), Network Policies and Private Connectivity, Data Governance and Access Control, and Database Object and Virtual Warehouse Management. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Snowflake Security, RBAC, and Administration30%
Account Management and Data Governance25%
Exam Overview
The SnowPro® Advanced: Administrator (ADA-C02) is Snowflake's professional-level certification that validates deep expertise in administering, securing, and optimizing Snowflake Data Cloud environments. Launched on January 19, 2026 as the successor to the ADA-C01, this credential covers the full spectrum of advanced administrative responsibilities including role-based access control architecture, federated authentication, data governance policies, virtual warehouse optimization, and cross-cloud replication. It is one of five SnowPro Advanced certifications and sits above the SnowPro Core in Snowflake's certification hierarchy.
The exam tests a candidate's ability to design and enforce security boundaries using network policies, private connectivity, and SCIM-based identity management; implement column-level and row-level security through masking and access policies; tune query performance using Query Profiles and caching strategies; orchestrate data sharing via the Snowflake Marketplace and private exchanges; and build resilient disaster recovery architectures using database replication, Time Travel, and Fail-safe. The ADA-C02 revision notably replaces the former Data Transformation domain with a dedicated Disaster Recovery, Backup, and Replication domain, reflecting Snowflake's growing enterprise focus on operational resilience.
Who Should Take This Exam
The ADA-C02 is designed for experienced practitioners who manage Snowflake environments in production settings. Primary target roles include Snowflake Administrators, Snowflake Data Cloud Administrators, Cloud Infrastructure Administrators, Database Administrators (DBAs), Cloud Data Administrators, and Application Developers who handle platform governance responsibilities. The credential is most relevant for professionals who own account-level configuration, security policy enforcement, cost management, and data sharing arrangements within their organizations.
Candidates should have at least two years of hands-on Snowflake administrative experience in a production environment before attempting the exam. Those transitioning from traditional DBA roles into cloud data platforms, or cloud engineers seeking to formalize their Snowflake expertise, will find this certification a strong signal of advanced capability to employers.
Prerequisites
Snowflake requires candidates to hold an active SnowPro Core Certified credential before sitting for the ADA-C02. The SnowPro Core covers foundational Snowflake concepts including architecture, data loading, query fundamentals, and basic account management, and serves as the essential baseline for the advanced content tested here.
Beyond the formal prerequisite, Snowflake strongly recommends a minimum of two years of hands-on Snowflake administrative experience in a production environment. Candidates should be fluent in both ANSI SQL and Snowflake-extended SQL, comfortable navigating the Snowsight interface and SnowSQL CLI, and familiar with cloud networking concepts (VPC/VNet, private endpoints) across at least one major cloud provider (AWS, Azure, or GCP). Practical experience configuring identity providers for SSO, designing RBAC hierarchies, and working with resource monitors is highly advisable.
Exam Format
The ADA-C02 consists of 65 scored questions for first-time certification candidates, or 40 questions for those recertifying, both within a 115-minute time limit. Questions are primarily scenario-based and situational, requiring candidates to evaluate multiple technically valid options and select the most appropriate or cost-effective solution — not simply recall definitions. The exam is delivered online through Certmetrics and costs $375 USD per attempt.
Scoring follows a scaled system ranging from 0 to 1,000, with a passing score of 750. The certification remains valid for two years from the issue date, after which holders must recertify through Snowflake's Continuing Education program. Snowflake does not publish a breakdown of scored versus unscored (pretest) questions for this exam.
Skills Measured
- 1.Domain 1 – Snowflake Security, RBAC, and Administration (30%): Covers federated authentication and SSO configuration with identity providers, MFA enforcement, key-pair authentication and rotation, OAuth 2.0 token management, SCIM integration for user and group provisioning, network policy design, private connectivity setup (AWS PrivateLink, Azure Private Link, GCP Private Service Connect), RBAC hierarchy design, privilege management, and preventing data exfiltration. This is the highest-weighted domain on the exam.
- 2.Domain 2 – Account Management and Data Governance (25%): Covers Snowflake Organization management, multi-account strategies, Tri-Secret Secure encryption and customer-managed key (CMK) configuration, column-level security via dynamic data masking and external tokenization, row access policies, object tagging and data classification, and auditing through the Account Usage schema and INFORMATION_SCHEMA views.
- 3.Domain 3 – Performance and Cost Optimization (approx. 20%): Covers virtual warehouse sizing, multi-cluster warehouse configuration, auto-suspend and auto-resume tuning, query performance analysis using Query Profile and EXPLAIN plans, result set caching and metadata caching strategies, micro-partition pruning, clustering keys, resource monitor configuration, and credit consumption reporting.
- 4.Domain 4 – Data Sharing and Collaboration (approx. 15%): Covers secure data sharing fundamentals, one-to-one and one-to-many sharing configurations, private data exchange setup and membership management, Snowflake Marketplace listing creation and management, provider profiles, cross-region and cross-cloud data sharing, and Data Clean Rooms concepts.
- 5.Domain 5 – Disaster Recovery, Backup, and Data Replication (approx. 10%): Covers database and account replication across regions and cloud providers, replication group configuration, failover and failback procedures, disaster recovery planning, Time Travel configuration and usage for querying historical data and restoring dropped objects, and Fail-safe retention periods and limitations. This domain is new to the ADA-C02 and replaces the Data Transformation domain from the prior ADA-C01 version.
Study Tips
- Download and structure your study plan around the official ADA-C02 Exam Study Guide available on learn.snowflake.com. It lists every subdomain with linked Snowflake documentation, blog posts, and exercises — treat it as your curriculum rather than a reference to skim at the end.
- Prioritize Domain 1 (Security/RBAC at 30%) and Domain 2 (Governance at 25%) together, as they account for more than half the exam. Build a sandbox Snowflake account and hands-on configure SSO with a free Okta developer tenant, implement dynamic data masking policies on PII columns, and design a multi-role RBAC hierarchy from scratch.
- For Domain 3 performance tuning, practice reading actual Query Profiles in Snowsight. Load a large dataset, run queries with and without clustering keys, observe partition pruning statistics, and deliberately create warehouse contention to understand multi-cluster scaling behavior. The exam favors scenario questions requiring you to diagnose performance issues from Profile output.
- Study the ADA-C02 with a focus on cost-versus-functionality trade-offs, as many scenario questions present multiple technically correct options and test whether you can identify the most cost-effective approach. For example, understand when cross-region replication via replication groups is preferable to ETL-based duplication, and when a single-cluster warehouse suffices over a multi-cluster configuration.
- Use the Snowflake Account Usage schema (SNOWFLAKE.ACCOUNT_USAGE) and INFORMATION_SCHEMA views as hands-on study tools. Query LOGIN_HISTORY, QUERY_HISTORY, ACCESS_HISTORY, and DATA_TRANSFER_HISTORY to understand governance auditing — these views appear frequently in Domain 2 and Domain 3 questions.
- Since Domain 5 (Disaster Recovery) is entirely new in ADA-C02 and replacing a previously tested area, allocate dedicated study time to Snowflake's documentation on replication groups, failover/failback, and the operational differences between Time Travel (configurable, up to 90 days on Enterprise+) and Fail-safe (fixed 7 days, non-configurable, Snowflake-managed recovery only).
- Take at least two timed full-length practice exams under exam conditions before your attempt. The 65-question, 115-minute format averages under 2 minutes per question, and many scenario questions are lengthy — time management is a practical skill to develop, not just content knowledge.
Career Benefits
Earning the ADA-C02 positions administrators for senior and principal-level roles in organizations that run Snowflake at scale, including titles such as Senior Snowflake Administrator, Cloud Data Platform Engineer, Data Infrastructure Architect, and Database Reliability Engineer. Snowflake-certified administrators report salaries in the range of $120,000 to $150,000 USD annually at the mid-level, with senior professionals commanding $150,000 to $185,000 base in 2025–2026. Principal-level architects with SnowPro Advanced credentials alongside complementary cloud certifications (AWS, Azure, or GCP) can reach $210,000 to $265,000 in total compensation at larger enterprises.
Snowflake's rapid enterprise adoption — with over 10,000 enterprise customers and a significant demand-supply gap for experienced administrators — means the ADA-C02 carries strong market signal value. Compared to the entry-level SnowPro Core, the Advanced Administrator credential demonstrates the operational depth that platform engineering and data infrastructure teams require when entrusting someone with account-level security, compliance enforcement, and cross-cloud replication. It is frequently listed alongside cloud provider certifications as a preferred or required qualification in senior data platform engineering job postings.
Sample Questions
5 sample questions with answers and explanations. Start a practice session to test yourself across all 600 questions.
Preview — answers shown1. A data governance architect at Lamna Healthcare has applied a tag-based masking policy to the PII_PHONE tag that masks phone numbers for all non-privileged roles. Every column in the PATIENT_CONTACTS table is tagged with PII_PHONE. A new regulatory requirement mandates a different masking algorithm specifically for the EMERGENCY_PHONE column. The architect applies a direct column-level masking policy to EMERGENCY_PHONE while leaving the PII_PHONE tag in place. When a non-privileged role queries the EMERGENCY_PHONE column, which masking policy is applied? (Select one!)
ABoth policies are applied in sequence and Snowflake combines their masking logic, returning the more restrictive result to the querying role
BThe tag-based masking policy associated with PII_PHONE, because tag-based policies take precedence over column-level policies in Snowflake's evaluation order
CThe column-level masking policy, because column-level policies always take precedence over tag-based policies when both are present on the same column
DNeither policy is applied because Snowflake raises a conflict error when a column simultaneously has a tag-based and a direct column-level masking policy
Explanation
When a column has both a tag-based masking policy and a direct column-level masking policy, the column-level policy takes precedence. This design allows administrators to implement broad governance through tags at scale while still overriding specific columns when regulatory requirements demand a different masking format. In this case, EMERGENCY_PHONE will be masked using the direct column-level policy, while all other PII_PHONE-tagged columns in the PATIENT_CONTACTS table continue to apply the tag-based policy. Tag-based policies cannot override column-level policies — the precedence is always column-level over tag-based, not the reverse. Snowflake does not raise a conflict error when both exist; it silently applies the column-level policy. This precedence behavior is an important governance design consideration when using tag-based policies at scale: applying a direct column-level policy is an intentional, supported mechanism for overriding the tag-based default on a case-by-case basis.
2. Litware Corp's data engineer is building a reporting layer and runs CREATE TABLE AS SELECT (CTAS) to create a new REPORTING.PATIENT_SUMMARY table by querying the source CLINICAL.PATIENT_DATA table. The source table has a Row Access Policy that restricts data visibility based on the user's regional role assignment. The engineer's active role is REPORT_BUILDER, which is authorized to see patient data from Region A only. What will the new REPORTING.PATIENT_SUMMARY table contain after the CTAS completes? (Select one!)
AAll rows from CLINICAL.PATIENT_DATA, but with Dynamic Data Masking applied to any sensitive columns
BNo rows, because CTAS operations are blocked when the source table has a Row Access Policy applied
CAll rows from CLINICAL.PATIENT_DATA, because CTAS bypasses Row Access Policies when creating new objects
DOnly the rows from CLINICAL.PATIENT_DATA that are visible to the REPORT_BUILDER role under the active Row Access Policy at the time of execution
Explanation
Row Access Policies filter rows at query execution time, and CTAS is treated as a standard query against the source table. The new REPORTING.PATIENT_SUMMARY table will contain only the rows visible to the executing role (REPORT_BUILDER, with Region A access) at the moment of creation. This represents a critical and irreversible data governance risk: once the CTAS completes, the Row Access Policy no longer protects the destination table, and all rows that were visible to REPORT_BUILDER are permanently materialized without any policy protection. CTAS does not bypass Row Access Policies; it materializes the filtered result. Similarly, any Dynamic Data Masking policies on source columns also apply during CTAS, so masked values are written as the masked output. CTAS is never blocked solely because a source table has a Row Access Policy applied.
3. The data governance team at Tailspin Analytics has attached a tag-based masking policy to a tag named PII_CLASSIFICATION and applied that tag to the CUSTOMER_EMAIL column in the ORDERS table. A data steward subsequently applies a separate column-level masking policy directly to the same CUSTOMER_EMAIL column for a stricter masking requirement. How does Snowflake determine which masking policy is enforced when a user queries the CUSTOMER_EMAIL column? (Select one!)
ABoth policies are evaluated simultaneously and their conditions are combined to produce the most restrictive masking outcome
BThe column-level masking policy takes precedence over the tag-based masking policy
CSnowflake raises a configuration error because a column cannot simultaneously carry both a tag-based and a directly assigned masking policy
DThe tag-based masking policy takes precedence because tags represent a higher-level governance control than direct column assignments
Explanation
When a column has both a directly assigned column-level masking policy and a tag-based masking policy, the column-level policy takes precedence and the tag-based policy is effectively bypassed for that specific column. This design allows governance teams to apply broad data protection across many columns using tags at scale while still permitting individual column overrides where different masking logic is required. The two policies are never combined or applied simultaneously — exactly one masking policy governs a column during any given query execution. Snowflake does not raise an error in this scenario; the precedence rule resolves the conflict silently and automatically. All other columns carrying the PII_CLASSIFICATION tag that do not have a direct assignment continue to be governed by the tag-based policy, preserving the scalability benefit of tag-based governance.
4. A data governance team at Adatum Financial has configured a tag-based masking policy that automatically applies email masking to all columns tagged with SENSITIVE_EMAIL across the entire account. After deployment, a security reviewer reports that a specific column in the CUSTOMER_360 table displays masked values that differ from the organization-wide tag-based policy behavior. Investigation reveals that a direct column-level masking policy had been previously applied to that column. Which Snowflake behavior explains this discrepancy? (Select one!)
ASnowflake raises a configuration error when both a tag-based and a column-level masking policy are detected on the same column simultaneously
BColumn-level masking policies take precedence over tag-based masking policies, causing the directly applied column-level policy to override the tag-based policy for that specific column
CSnowflake applies both the column-level and tag-based masking policies in sequence, combining their masking logic and producing a composite result for the column
DTag-based masking policies always take precedence over column-level policies to enforce consistent organization-wide data governance standards
Explanation
When both a column-level masking policy and a tag-based masking policy apply to the same column, the column-level masking policy takes precedence and overrides the tag-based policy for that specific column. This precedence hierarchy is intentional and allows administrators to create targeted, column-specific exceptions to organization-wide tag-based governance without removing the tag or modifying the broader policy. Snowflake does not apply both policies sequentially or combine their masking logic. Only the higher-precedence column-level policy executes, and the tag-based policy is effectively ignored for that column. No configuration error or warning is raised when this conflict exists; the resolution is silent and determined entirely by the precedence hierarchy. Tag-based policies provide excellent scalability for governing large numbers of columns automatically, but they intentionally yield to more specific column-level policies to allow fine-grained control where needed.
5. A data analyst at Litware Financial is assigned only the ANALYST role, which sees masked values for the card_number column of the production.transactions table. An active dynamic data masking policy on that column restricts full card numbers to users with the FINANCE_ADMIN role. The analyst executes the following statement: CREATE TABLE reporting.card_archive AS SELECT * FROM production.transactions. What is the outcome for the card_number column in the newly created table? (Select one!)
ASnowflake raises an error and blocks the CREATE TABLE AS SELECT operation when it detects that the source table has an active dynamic data masking policy
BThe card_number column in card_archive contains the original unmasked credit card numbers because CREATE TABLE AS SELECT copies the underlying stored data directly, bypassing query-time masking
CThe masking policy is automatically cloned and applied to the card_number column in card_archive, protecting unmasked underlying values in the new table
DThe card_number column in card_archive permanently stores the masked values that were visible to the ANALYST role at execution time, creating an irreversible loss of the original data in this copy
Explanation
Dynamic data masking in Snowflake is applied at query time based on the active role of the session executing the query. When the ANALYST role executes CREATE TABLE AS SELECT, the SELECT portion evaluates the masking policy using the ANALYST role context and returns the masked representation of card_number. Those masked values are then physically inserted as the actual stored data in the new table. The result is that card_archive permanently contains only masked card numbers with no mechanism to recover the originals from this copy. This CREATE TABLE AS SELECT data loss risk is a critical behavior for administrators managing masking policies. Snowflake does not block CTAS operations on masked tables, nor does it automatically transfer the masking policy to the new table. The masking policy remains on the source table only; the new table receives no policy and simply stores whichever values were returned by the query at execution time.
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