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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Implementing dbt Tests	10%	- Custom tests - Built-in tests - Test configuration and execution
External Dependencies	10%	- External sources integration - Using packages - Managing snapshots
Debugging and Error Resolution	15%	- Identifying modeling errors - Resolving data quality issues - Debugging techniques
Leveraging dbt State	5%	- State management - State-aware operations
Developing dbt Models	20%	- Model optimization 1. Model contracts 2. Performance tuning - Model design and structure 1. Incremental models 2. Ref and source functions 3. Materialization types
dbt Models Governance	15%	- Project organization and structure - Version control integration - Naming conventions and standards
Creating and Maintaining Documentation	10%	- Documentation standards - Generating documentation - Descriptions and metadata
Managing Data Pipelines	15%	- Deployment strategies - CI/CD integration - Pipeline orchestration

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q189-Q194):

NEW QUESTION # 189
You establish a process using dbt tests and snapshots to track data quality changes over time. To maximize the utility of this historical record for troubleshooting, what additional step would be beneficial?

A. Convert the dbt test results and snapshots into time-series tables for easier trend analysis.
B. Integrate metadata about dbt model versions and schema changes alongside the snapshots.
C. Schedule regular alerts whenever a test result deviates significantly from previous runs.
D. Automatically purge snapshots older than a certain date to save storage resources.

Answer: A,B,C

Explanation:
These enhance the historical context and make the data actionable for resolving issues- A might discard valuable information prematurely

NEW QUESTION # 190
You introduce a new data source for a dbt model. Production policies require review and approval of the source's schema before it can be used for reporting. How could you streamline this within your dbt project?

A. Use dbfs ephemeral models feature to temporarily load the data for the approval process.
B. Write dbt tests that explicitly fail if the new source schema does not match the approved definition.
C. Create a pre-hook that automates schema validation and sends the output to the designated team.
D. Run a separate dbt project in production exclusively for source validation, providing access to relevant stakeholders.

Answer: A,C,D

Explanation:
A provides proactive checks integrated into the workflow. B establishes a self-contained validation environment. D is an easy, controlled way for review. C is reactive and could potentially halt valid deployments.

NEW QUESTION # 191
31. Your entire DAG looks like the image shown.

(Several stg_ models appear upstream, feeding into int_ and fct_ models.) The question asks:
"Was this modeling rule violated?
Staging models dependent on other staging models"

A. Yes
B. No

Answer: A

Explanation:
In dbt's recommended layered modeling architecture, the staging layer is intended to provide a clean, one- to-one representation of raw source tables. Each stg_ model should depend only on sources, not on other staging models. This ensures staging remains a simple, transparent layer where data is renamed, recast, standardized, and lightly transformed before being passed to intermediate and mart layers.
In the DAG shown, at least one staging model (for example, stg_line_items or stg_tpch_line_items) appears downstream of another staging model, meaning a stg_ model is referencing another stg_ model. This violates dbt's recommended modeling practice, because it creates unnecessary complexity in the staging layer and reduces modularity and transparency. Downstream layers such as intermediate (int_) and marts (fct_) should be used to combine, enrich, or join multiple staging outputs.
When staging models depend on each other, it becomes harder to trace lineage, reduces clarity about where transformations occur, and complicates the entire DAG. The proper pattern is:
* Sources # Staging (stg_) # Intermediate (int_) # Marts (fct_)
Since the DAG shows staging models referencing other staging models, the rules have indeed been violated.

NEW QUESTION # 192
You need to run a dbt job that performs a long-running data cleansing operation. The job occasionally times out. Which of the following are potential solutions?

A. Schedule the job to run concurrently with other dbt models to maximize resource usage-
B. Increase the timeout setting in your CI/CD tool or dbt job configuration.
C. Break the cleansing logic into multiple models, chaining them with dependencies-
D. Use a dbt macro to retry the cleansing operation with a backoff strategy within the model itself-

Answer: B,C,D

Explanation:
Each tackles the timeout issue differently:A reduces the work done in a single step. B provides more time directly . C adds retry resilience within the logic. D would likely worsen performance due to resource contention.

NEW QUESTION # 193
After configuring a dbt package, you notice its models depend on a source table absent from your warehouse. How do you typically address this?

A. File an issue or submit a pull request to the package's repository.
B. Override the package model's SQL query to reference an existing table in your warehouse.
C. Update your source definitions to include the required table.
D. Rewrite the package's model from scratch within your project to remove the dependency.

Answer: B

Explanation:
Start by fulfilling the package's dependencies. If the table is truly generic, then customizing the SQL (B) might be the next step.

NEW QUESTION # 194
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