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NEW QUESTION # 142
A colleague has made updates to some model descriptions and wants you to quickly regenerate the docs to only include those changes. Which command variation would be the most efficient without a full recompilation?
- A. It's not possible to partially regenerate dbt docs; you need to run the full dbt docs generate command.
- B. dbt docs generate -select [model_name]
- C. dbt docs generate --models +[model_namel
- D. Utilize dbt's incremental model feature along with dbt docs generate -no-compile.
Answer: B
Explanation:
The -select flag limits the documentation generation to the specified model(s). However, for this to work smoothly, dependencies need to be considered. If the updated model depends on others, they might need to be included or recompiled for the docs to be correct.
NEW QUESTION # 143
What must happen before you can build models in dbt?
Choose 1 option.
- A. Sources must have been defined in your dbt project.
- B. You must have created a service account in your data platform.
- C. Raw data must be cleaned.
- D. Underlying data must be accessible on your data platform.
Answer: D
Explanation:
The correct answer is C: Underlying data must be accessible on your data platform.
dbt does not perform data ingestion or data loading. Instead, dbt operates after raw data is already available in your warehouse. This means that before dbt can build any models-whether staging, intermediate, or mart- layer models-the underlying source data must already exist and be accessible in the connected data platform (Snowflake, BigQuery, Redshift, Databricks, etc.). dbt uses SQL to transform existing relations; therefore, if the data platform cannot access the underlying tables or external sources, model execution will fail.
Option A is incorrect because sources do not need to be defined before building models. Models can be built without using sources at all. Source definitions are optional metadata and lineage declarations, not prerequisites.
Option B is incorrect because service accounts are not required; dbt can connect through any credential mechanism supported by the warehouse (OAuth, user accounts, tokens, etc.).
Option D is incorrect because dbt itself performs transformations on raw data-cleaning raw data beforehand is not required; in fact, that is one of dbt's main responsibilities.
Thus, the only true prerequisite is that the warehouse must contain accessible underlying data.
NEW QUESTION # 144
You're implementing singular tests (accepted values). After providing a list of valid values, you notice some strange behavior: the test is passing even with records that contain unexpected dat a. What's the most likely issue?
- A. Your test case is accidentally permitting null values, which bypass the check.
- B. The accepted_values test configuration has the wrong column specified.
- C. The model's data type isn't compatible with the accepted_values test.
- D. A downstream process is overwriting the results after the model is built.
Answer: A,B,C
Explanation:
Each of these directly impacts the accepted_values test's effectiveness, while D is unlikely for a singular test.
NEW QUESTION # 145
You're setting up CI/CD for your dbt project to automate deployments. Which aspects of your testing strategy might need adaptation for a production-focused CI/CD workflow?
- A. Prioritizing tests with strict thresholds to avoid false positives from minor data variations in production.
- B. Emphasizing tests with longer run times for thorough checks before deployment.
- C. Disabling all tests that rely on creating new tables, to avoid impacting the production database.
- D. Including smoke tests that run in production to verify core functionality after a deployment.
Answer: A,D
Explanation:
B ensures the deployed changes actually work in the real environment. C helps maintain CI/CD flow without unnecessary disruptions. A is counterproductive in a time-sensitive CI/CD context. D would overly limit your testing coverage.
NEW QUESTION # 146
A team member suggests building a dbt package for a set of reusable transformations your team frequently uses. When is this MOST likely a good idea?
- A. The transformations are complex and involve domain-specific logic that might be beneficial to others.
- B. The transformations are project-specific but used across multiple models.
- C. You need to limit access to these transformations to a specific team within your organization.
- D. The transformations are relatively simple and could be written as macros.
Answer: A
Explanation:
Sharing common, complex logic is a key reason for creating packages. Simple logic is better as macros. Project-specificity favors macros or CTEs. Packages arent ideal for access control.
NEW QUESTION # 147
You encounter the following error when running dbt compile:Runtime Error in model customer _ churn (models/transformations/customer_churn.sql)
- A. The fct_orders source is not configured correctly in your dbt_project.yml file.
- B. There's a typo in how you're referencing the fct_orders object in your SQL.
- C. The fct_orders model depends on other models that haven't compiled successfully.
- D. You need to add a schema test to validate the existence of the fct orders table.
Answer: B
Explanation:
Explanation: This is a runtime error indicating incorrect usage of fct_orders. Options A and C could cause errors but of a different nature. Schema tests won't fix a referencing error.
NEW QUESTION # 148
dbt Model Snippet
- A. The presence of filters on the source fct_orders or dim_customers.
- B. The ref() functions arent correctly resolving dependencies.
- C. The underlying data types of o.customer_id and c.customer_id mismatch.
- D. The logic contained within a custom macro.
Answer: A
Explanation:
Filters on sources frequently cause missing rows in joins. Check your source definitions and any model- level filtering. While the others could be issues, start with the most common cause.
NEW QUESTION # 149
28. Consider this DAG:
* model_a # model_c # model_e
* model_b # model_d # model_f
(With model_c and model_d both feeding into the final layer.)
You execute:
dbt run --fail-fast
in production with 2 threads. During the run, model_b and model_c are running in parallel when model_b returns an error.
Assume there are no other errors in the model files, and model_c was still running when model_b failed.
Which model or models will successfully build as part of this dbt run? Choose 1 option.
- A. model_a, model_c
- B. model_a, model_c, model_e
- C. model_a, model_c, model_d, model_e, model_f
- D. model_a
Answer: A
Explanation:
The --fail-fast flag tells dbt to stop scheduling any new nodes as soon as one node fails. Importantly, dbt does not kill models that are already running; in-flight nodes are allowed to finish.
Here's what happens step by step with 2 threads:
* Roots model_a and model_b start first.
* model_a finishes successfully. That makes model_c eligible to run.
* dbt now runs model_b and model_c in parallel.
* While they are running, model_b fails.
* Because --fail-fast is set, dbt immediately stops scheduling any additional models (like model_d, model_e, or model_f).
* model_c was already running when model_b failed, so it is allowed to complete successfully.
Downstream models of either branch (model_d, model_e, and model_f) never start, because fail-fast prevents any further nodes from being queued after the first failure.
So, the only models that successfully build during this run are:
* model_a (completed before model_b failed)
* model_c (already running at the time of failure and allowed to finish) Hence the correct choice is B: model_a, model_c.
NEW QUESTION # 150
You've added new tests but notice they aren't failing even though you've intentionally introduced errors. What's a LIKELY cause?
- A. You haven't compiled your project since writing the new tests.
- B. Your dbt project has multiple targets configured.
- C. The tests are defined in seeds.csv files.
- D. Your tests have incorrect severity thresholds.
Answer: A
Explanation:
Compilation is necessary for new tests to be recognized. Others are less likely, though severity thresholds could be a factor if misconfigured.
NEW QUESTION # 151
Consider these SQL and YAML files for the model model_a:
models/staging/model_a.sql
{{ config(
materialized = "view"
) }}
with customers as (
...
)
dbt_project.yml
models:
my_new_project:
+materialized: table
staging:
+materialized: ephemeral
Which is true about model_a? Choose 1 option.
Options:
- A. Select statements made from the database on top of model_a will result in an error.
- B. Select statements made from the database on top of model_a will be slower, but the data will always be up to date.
- C. Select statements made from the database on top of model_a and transformation processing within model_a will be quicker, but the data will only be as up to date as the last dbt run.
- D. Select statements made from the database on top of model_a will be quicker, but the data will only be as up to date as the last dbt run.
(Note: A and D are duplicates - typical exam formatting.)
Answer: B
Explanation:
model_a contains an in-model config explicitly setting:
{{ config(materialized = "view") }}
In dbt, in-model config overrides project-level config, including folder-level defaults. Even though the staging/ folder is configured as:
staging:
+materialized: ephemeral
...this does not apply because the in-file config has higher precedence.
So model_a is materialized as a view.
A view in dbt is not persisted as data; instead, it stores the SQL definition. When a downstream query selects from that view, the database executes the underlying SQL at query time. As a result:
* Queries run slower than selecting from a table, because the underlying computation happens each time.
* But the data is always up to date, because views read directly from the current underlying tables.
This matches Option C.
Why the other options are wrong
* A & D describe table behavior (faster queries, data only as fresh as last run). model_a is not materialized as a table.
* B is incorrect because views are fully queryable; no error occurs.
NEW QUESTION # 152
You've configured your project to include both schema and data tests in the generated documentation. A user reports the tests are visible but don't show any execution results (pass/fail status). Why might this be happening?
- A. The type of test you're using is not supported for displaying results in the documentation.
- B. The documentation generator has a known limitation on how test results are displayed.
- C. Your data pipeline hasn't been executed recently, so there are no recent test results.
- D. The project settings are explicitly configured to hide test results in the documentation.
Answer: C,D
Explanation:
A Test results depend on recent executions; if your data pipeline is not updated, there won't be fresh results. B: Its possible to have configurations that display the tests themselves but not their execution results
NEW QUESTION # 153
While explaining dbt concepts to a colleague, they ask, "Don't macros just add an extra layer of complexity?" How would you address their concern?
- A. "Macros simplify debugging because they break down your dbt model logic."
- B. "Yes, they do, but their maintainability benefits outweigh the initial complexity."
- C. "Macros don't introduce additional complexity but are mainly used for performance tuning."
- D. "Macros are primarily needed for legacy projects; new projects should avoid them."
Answer: B
Explanation:
Acknowledge the potential increase in upfront complexity but emphasize the long-term benefits in reducing code duplication, improving readability, and enhancing maintainability.
NEW QUESTION # 154
Which of the following is NOT a direct benefit of thorough source, table, and column descriptions in dbt?
- A. Improved lineage and data traceability within the documentation.
- B. Better data quality due to the automated enforcement of column descriptions
- C. Faster dbt model compilation and execution times.
- D. Increased discoverability for other team members working on the project
Answer: C
Explanation:
Explanation: While good descriptions have numerous benefits, they don't inherently speed up model compilation and execution within dbt-
NEW QUESTION # 155
You're setting up baseline comparisons with historical dat
a. Which factors are crucial to consider when selecting or generating the baseline dataset?
- A. If using data, the baseline might need sanitization to remove PII-
- B. The data volume in the baseline should be large enough to identify statistical patterns.
- C. The baseline should cover a representative time range relevant to your analysis.
- D. The baseline needs to align in structure (schemas) with the dbt models being tested.
Answer: A,C,D
Explanation:
Each of these directly impacts the validity and usefulness of the baseline for comparison. B, while good, is less universally critical.
NEW QUESTION # 156
You suspect discrepancies between a raw data source and its corresponding dbt representation. Which of the following would NOT directly help in pinpointing the cause of this mismatch?
- A. Comparing the dbt-generated SQL against the raw table's SELECT statement.
- B. Examining any source freshness warnings or errors in recent dbt runs.
- C. Checking if any custom macros or tests are modifying the source data.
- D. Reviewing the source definition within your dbt_project.yml .
Answer: B
Explanation:
While freshness warnings might hint at problems, they don't directly reveal data mismatches- Checking the actual SQL against the raw source and project configurations is key.
NEW QUESTION # 157
You try updating a column description using its corresponding YAML file. After regenerating the docs, the update doesn't appear. Which of the following might be the cause?
- A. There could be a syntax error within the updated description that dbt is silently ignoring.
- B. Changes in column descriptions only update the database schema, not the project documentation.
- C. Your browser is aggressively caching the documentation site.
- D. The column description might need additional configuration with a test for it to render in the docs.
Answer: A,C
Explanation:
A: Aggressive browser caching can sometimes prevent updates from being reflected. C: Even small syntax errors in YAML can lead to dbt ignoring certain elements during the documentation generation process.
NEW QUESTION # 158
Examine the configuration for the source:
sources:
- name: jaffle_shop
schema: jaffle_shop_raw_current
tables:
- name: orders
identifier: customer_orders
Which reference to the source is correct?
- A. {{ source('jaffle_shop', 'customer_orders') }}
- B. {{ source('jaffle_shop', 'orders') }}
- C. {{ source('jaffle_shop_raw_current', 'customer_orders') }}
- D. {{ source('jaffle_shop_raw_current', 'orders') }}
Answer: B
Explanation:
In dbt, the source() function resolves a source by its declared source name and table name, not by the physical schema or identifier in the warehouse. The YAML block defines a source named jaffle_shop, and under that source, a table named orders. The identifier: customer_orders field tells dbt that although the logical table name is orders, the actual physical object in the warehouse is named customer_orders.
dbt always expects the syntax:
{{ source(source_name, table_name) }}
Here, the correct reference uses jaffle_shop as the source name and orders as the table name because these are the logical names assigned in the YAML. dbt internally resolves the physical table name via the identifier field, so the model should not reference customer_orders directly.
Option A and B are incorrect because the first argument is not the schema; dbt does not use schemas in the source() call. Option D is incorrect because customer_orders is the warehouse identifier, not the logical table name recognized by dbt.
Therefore, the correct reference is:
{{ source('jaffle_shop', 'orders') }}
This ensures consistent modeling, dependency tracking, and accurate documentation.
NEW QUESTION # 159 
Answer:
Explanation:
Explanation:
(new_project)
(models:)
(grants)
(require-dbt-version: '>= 1.0.0' )
The dbt_project.yml file acts as the configuration backbone for every dbt project. dbt requires that project names follow a strict naming convention, allowing only lowercase letters, numbers, and underscores.
Therefore, new_project is the only valid name among the choices. Names with spaces or hyphens, such as new project or new-project, violate dbt's project naming rules.
To configure how models behave-materializations, schemas, tags, or per-directory overrides-dbt uses the models: block inside dbt_project.yml. This block defines configuration inheritance rules and is different from the config() function, which is used inside SQL models, not in YAML files.
When granting permissions on database objects created by dbt, you use the grants configuration. This allows you to define access roles at the model level or project level for downstream BI or analytics tools, ensuring proper data governance and controlled data exposure.
Finally, compatibility with specific dbt versions is set using require-dbt-version. The correct YAML formatting uses quotes, such as '>= 1.0.0', ensuring dbt interprets the value as a string. This prevents accidental mis-parsing and enforces version requirements during dbt run or dbt build.
NEW QUESTION # 160
You need to override a source description at the model level. How would you achieve this most effectively?
- A. Use the meta configuration in your model file to provide an alternative description.
- B. Create a separate column in the source with a model-specific description.
- C. You cannot override a source description directly in the model.
- D. Utilize a Jinja macro to dynamically construct the description based on specific conditions.
Answer: A
Explanation:
The meta configuration can be used to override source descriptions and other metadata at the model level. However, make sure to check the documentation for your specific dbt version, as the exact way to do this might vary_
NEW QUESTION # 161
Your data warehouse is undergoing a migration to a different vendor (e.g., Snowflake to BigQuery). Which category of tools might play a critical role in ensuring a smooth transition of your dbt project?
- A. Data virtualization tools
- B. dbt adapters
- C. Cloud infrastructure management tools
- D. SQL dialect translation tools
Answer: B,D
Explanation:
C is key because SQL dialects differ subtly between databases, and a translator can help with compatibility. D is important because dbt adapters provide the 'bridge' between dbt and the specific warehouse technology.
NEW QUESTION # 162
(Multiple Select)
- A. Automated code linters
- B. Data discovery platforms to catalog available data assets
- C. Knowledge sharing platforms or internal wikis for documenting best practices.
- D. Centralized dbt packages for reusable code
Answer: A,B,C,D
Explanation:
Each of these promotes collaboration, discoverability, and standardization - essential when scaling dbt across teams.
NEW QUESTION # 163
You discover that dbt models run significantly slower in your production environment than in development. What potential causes should you investigate?
- A. Production data volume being significantly larger than development data.
- B. Inefficient use of snapshots or incremental models in your production dbt project_
- C. Different database configurations or resource allocations (memory, CPU, etc.) between environments-
- D. Network latency impactingressing access to remote data sources.
Answer: A,C,D
Explanation:
Each of these directly affects performance and could differ significantly between environments. C is relevant, but less likely the sole cause if development is performant.
NEW QUESTION # 164
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