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NEW QUESTION # 93
A pull request targeting your main branch has been approved in code review but requires changes after automated tests fail. To address this, you should:
- A. Disable the automated tests temporarily, merge, and re-enable tests later.
- B. Directly modify the branch by pushing commits to fix the tests.
- C. Ask the branch author to add new commits fixing the issues. Then merge when tests pass.
- D. Merge the pull request despite the failing tests, resolving the issues in a subsequent fix.
Answer: C
Explanation:
The branch author should update their pull request with fixes. Merging should typically only happen once tests pass, indicating code stability.
NEW QUESTION # 94
You're working in a dbt Cloud development environment. You make a change to a model definition that accidentally introduces an invalid column reference. What's the likely sequence of events when you trigger a dbt run?
- A. The job execution state will depend on the specific SQL dialect of your data warehouse.
- B. The job will succeed, as dbt only validates schema changes at deployment time.
- C. The job will succeed but produce incorrect output due to the invalid reference.
- D. The job will fail during compilation, and a clear error message will point to the invalid reference.
Answer: D
Explanation:
dbt performs pre-execution validation, catching many schema errors early. Behavior in C is less likely and D shouldnt be so inconsistent.
NEW QUESTION # 95
You discover a model sorting large amounts of data using an ORDER BY clause with several columns. Performance needs to be increased. What's the FIRST step in your optimization process?
- A. Change the materialization from a 'table' to a 'view'.
- B. Examine query execution plans to identify bottlenecks and potential index usage.
- C. Remove unnecessary columns from the ORDER BY clause.
- D. Add a LIMIT clause to restrict the number of records sorted.
Answer: B
Explanation:
Before implementing changes, it's crucial to understand current performance bottlenecks. Query execution plans provide this insight.
NEW QUESTION # 96
You've created a complex lineage macro, and you want to include unit tests for the macro logic itself. How might you approach this?
- A. Currently, there is no standard way to directly unit test macros within a dbt project
- B. Include test cases for the macro within your model-level schema.yml files.
- C. Utilize dbt's built-in macro testing framework.
- D. Write a Jinja script that programmatically tests various input/output scenarios of your macro.
Answer: D
Explanation:
While dbt doesn't have a dedicated macro testing framework, Jinja provides ways to construct tests. However, there's no built-in mechanism to run these as part of your typical dbt workflow.
NEW QUESTION # 97
You're ready to integrate your feature branch into the main branch. Which scenarios would necessitate a pull request and code review process?
- A. In all dbt projects, pull requests should be mandatory.
- B. You are working on a personal dbt project.
- C. You are working in a collaborative team environment with established quality control practices.
- D. You are the only contributor to the dbt project.
Answer: A,C
Explanation:
C: Code reviews are crucial in collaborative settings for improving quality and sharing knowledge. D: While not strictly mandatory always, pull requests are a best practice even when working alone as they encourage structured change tracking-
NEW QUESTION # 98
(Multiple Select)
- A. Columns intended to contain timestamps actually store valid timestamp values.
- B. Tables intended to be empty as part of a process truly have zero records.
- C. Foreign key relationships are consistently enforced.
Answer: A,B,C
Explanation:
Each of these represents a common assumption about source data quality, and any violation could break your dbt models or produce incorrect results.
NEW QUESTION # 99
Your baseline comparison tests start failing intermittently. Data distributions change slightly from day to day, sometimes triggering the thresholds. Which strategy could make your tests more robust?
- A. Introducing configurable tolerances into your test definitions.
- B. Removing the baseline comparison tests entirely, as they provide unreliable signals.
- C. Rewriting your SQL to compare averages or medians instead of exact value counts-
- D. Scheduling the tests to run less frequently (e.g., weekly rather than daily).
Answer: A,C
Explanation:
A makes tests less sensitive to minor noise- B allows for controlled flexibility. C is a workaround, sacrificing freshness. D gives up on a valuable check_
NEW QUESTION # 100
You're using dbt Cloud and want to tighten control over when production deployments can occur. What dbt Cloud feature, in conjunction with your CI/CD configuration, would allow you to enforce this?
- A. Deployment jobs and schedules.
- B. Webhooks for triggering jobs by external events.
- C. Environment variables for sensitive credentials.
- D. dbt Cloud's built-in source freshness checks.
Answer: A
Explanation:
B enables you to define specific times or conditions for production deployments. Others are less directly related to controlling the timing.
NEW QUESTION # 101
(Multiple Select)
- A. Using the 'defer' option to partially run a DAG and expose errors early
- B. Defining source freshness thresholds to catch issues with missing tables or views-
- C. Implementing a robust suite of schema tests to validate object references-
- D. Setting up a linter for your SQL files, either as part of your IDE or code editor
Answer: C,D
Explanation:
Linters improve code hygiene, and schema tests proactively validate assumptions about model dependencies. 'Defer' helps with logic errors, and freshness checks are for source health.
NEW QUESTION # 102
Which of the following is NOT a direct benefit of thorough source, table, and column descriptions in dbt?
- A. Faster dbt model compilation and execution times.
- B. Increased discoverability for other team members working on the project
- C. Improved lineage and data traceability within the documentation.
- D. Better data quality due to the automated enforcement of column descriptions
Answer: A
Explanation:
Explanation: While good descriptions have numerous benefits, they don't inherently speed up model compilation and execution within dbt-
NEW QUESTION # 103
You suspect discrepancies in a critical model. To troubleshoot, you need to understand the order of model execution, but the DAG is complex. What would be the BEST strategy to get this information?
- A. Use dbt Is -select
- B. Run dbt docs generate and inspect the generated documentation.
- C. Manually trace the dependencies in your model files.
- D. Visually inspect the manifest.json file.
Answer: B
Explanation:
While other options provide some information, dbt docs generate creates comprehensive documentation including a visual DAG to show execution flow clearly.
NEW QUESTION # 104
You run a new dbt job, but several models don't execute. Logs indicate: "Database Error in model. The error message seems unrelated to your recent model code changes. What's a possible non- SQL cause?
- A. dbt's connection to the database has incorrect credentials or is encountering network issues.
- B. A macro used in multiple models has an error that prevents the models from compiling correctly.
- C. You've accidentally deleted a source table that these models depend on.
- D. There's a configuration error in your project file causing dbt to skip certain models.
Answer: A
Explanation:
Sometimes, what appears as a model error has roots in database connectivity. The others could be the cause, but check your project-wide connection setup first.
NEW QUESTION # 105
A critical model depends on a third-party data source with periodic update delays. How could you structure your DAG to mitigate the impact of these delays on downstream reporting?
- A. Employ a snapshot of the third-party source to create a historical record.
- B. Utilize the 'defer' option to allow upstream models to run while awaiting the source update.
- C. Configure a 'view' that gracefully handles missing data from the third-party source.
- D. Set up an alert to notify users when the upstream data source is not refreshed on time.
Answer: B
Explanation:
The 'defer' option lets your DAG continue partially while awaiting the delayed data. Others offer solutions but don't address the core DAG flow issue.
NEW QUESTION # 106
Given this dbt_project.yml:
name: "jaffle_shop"
version: "1.0.0"
config-version: 2
profile: "snowflake"
model-paths: ["models"]
macro-paths: ["macros"]
snapshot-paths: ["snapshots"]
target-path: "target"
clean-targets:
- "logs"
- "target"
- "dbt_modules"
- "dbt_packages"
models:
jaffle_shop:
orders:
materialized: table
When executing a dbt run your models build as views instead of tables:
19:36:14 Found 1 model, 0 tests, 0 snapshots, 0 analyses, 179 macros, 0 operations, 0 seed files, 0 sources, 0 exposures, 0 metrics
19:36:16 Concurrency: 1 threads (target='default')
19:36:17 Finished running 1 view model in 3.35s.
19:36:17 Completed successfully
19:36:17 Done. PASS=1 WARN=0 ERROR=0 SKIP=0 TOTAL=1
Which could be a root cause of why the model was not materialized as a table?
The target-path is incorrectly configured.
- A. No
- B. Yes
Answer: B
Explanation:
The behavior described-dbt running the orders model as a view despite being explicitly configured as a table
-indicates that dbt is not correctly detecting or applying the model-level configuration during compilation.
dbt relies heavily on the target-path directory to write compiled SQL, manifest files, and run artifacts. If the target-path is misconfigured, pointing to a location that dbt does not handle correctly or that overlaps with another folder used internally, dbt may fail to load the correct configuration from the merged project settings.
When dbt cannot locate the compiled configuration for a model, it defaults to its standard materialization type, which is view. This explains why the logs show:
"Finished running 1 view model"
even though the dbt_project.yml clearly declares:
materialized: table.
Additionally, the logs indicate no warnings or parsing errors, meaning dbt ran successfully but with incorrect settings-another indicator of configuration metadata being overridden or misplaced due to an incorrect target- path.
By resolving the target-path issue, dbt will successfully load the model configuration and materialize the orders model as a table as intended.
NEW QUESTION # 107
Your development environment uses a smaller-scale version of the production warehouse. You often debug issues that depend on data volume, requiring a way to temporarily "scale up" your development environment. Which strategies are feasible?
- A. Write dbt macros that use temporary tables to simulate larger datasets during development
- B. Selectively snapshot a subset of production data into your development environment.
- C. Dynamically increase resource allocation (CPU, memory) within your data warehouse, if supported.
- D. Run a separate dbt project against production data for testing, but target a staging schema.
Answer: B,C,D
Explanation:
A directly addresses the resource limitation, if possible. C offers a controlled way to bring in real data. D creates a sandboxed environment within production. B is clever, but unlikely to be truly representative.
NEW QUESTION # 108
What must happen before you can build models in dbt?
Choose 1 option.
- A. Sources must have been defined in your dbt project.
- B. Raw data must be cleaned.
- C. Underlying data must be accessible on your data platform.
- D. You must have created a service account in your data platform.
Answer: C
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 # 109
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