Title: Quiz Useful dbt-Analytics-Engineering - dbt Analytics Engineering Certification [Print This Page] Author: rickmur586 Time: 3 day before Title: Quiz Useful dbt-Analytics-Engineering - dbt Analytics Engineering Certification Our dbt Analytics Engineering Certification Exam (dbt-Analytics-Engineering) web-based practice exam software also simulates the dbt Analytics Engineering Certification Exam (dbt-Analytics-Engineering) environment. These dbt Labs dbt-Analytics-Engineering mock exams are also customizable to change the settings so that you can practice according to your preparation needs. TopExamCollection web-based dbt Analytics Engineering Certification Exam (dbt-Analytics-Engineering) practice exam software is usable only with a good internet connection.
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Authorized dbt-Analytics-Engineering New Exam Bootcamp & Valuable Valid Braindumps dbt-Analytics-Engineering Sheet & Professional dbt Labs dbt Analytics Engineering Certification ExamIt is a universally accepted fact that the dbt-Analytics-Engineering exam is a tough nut to crack for the majority of candidates, but there are still a lot of people in this field who long to gain the related certification so that a lot of people want to try their best to meet the challenge of the dbt-Analytics-Engineering Exam. A growing number of people know that if they have the chance to pass the exam, they will change their present situation and get a more decent job in the near future. dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q11-Q16):NEW QUESTION # 11
You are building an incremental model.
Identify the circumstances in which is_incremental() would evaluate to True or False. Answer:
Explanation:
Explanation:
1. There is a config block at the top of the model with materialized = 'table'
## Answer: False
2. The corresponding table already exists in the data warehouse
## Answer: True
3. The incremental model was executed with the --full-refresh flag
## Answer: False
4. The corresponding table does not already exist in the data warehouse
## Answer: False
The is_incremental() macro in dbt is used within an incremental model to determine whether the current invocation should perform an incremental update or a full rebuild. It evaluates to True only during an incremental run when the target table already exists in the data warehouse and dbt is not performing a full refresh.
For is_incremental() to return True, two conditions must be met:
* The model must be materialized as incremental, not table or view.Therefore, if the config specifies materialized = 'table', the model is not incremental, and the function evaluates to False.
* The table already exists in the target schema.In this case, dbt will run the model in incremental mode, making is_incremental() return **True`.
If the incremental model is run with --full-refresh, dbt intentionally rebuilds the entire table, meaning is_incremental() evaluates to False, even if the table exists.
Lastly, if the target table does not exist yet, dbt must create it from scratch, so the run is treated as a full rebuild, not an incremental update, causing is_incremental() to evaluate to False.
Thus, the only scenario where is_incremental() returns True is when the table exists and the model is actually incremental.
NEW QUESTION # 12
Examine model stg_customers_sales that exists in the main branch:
select
id as customer_id,
name as customer_name
from {{ source('my_data','my_source') }}
A developer creates a branch feature_a from main and modifies the model as:
select
id as customer_id,
name as customer_name,
country as customer_country
from {{ source('my_data','my_source') }}
A second developer also creates a branch feature_b from main and modifies the model as:
select
id as customer_id,
name as customer_name,
address as customer_address
from {{ source('my_data','my_source') }}
The first developer creates a PR and merges feature_a into main.
Then the second developer creates a PR and attempts to merge feature_b into main.
How will git combine the code from feature_b and the code from main, which now contains the changes from feature_a as well?
Statement:
"As feature_a is already approved and merged to main, the code for the model stg_customers_sales will stay as-is and the changes from feature_b won't be added."
A. No
B. Yes
Answer: A
Explanation:
Git does not automatically reject or ignore changes from the second branch (feature_b). Instead, Git attempts to merge both sets of changes, and if they modify the same lines or nearby blocks of code, Git produces a merge conflict that must be manually resolved. In this scenario, both feature_a and feature_b introduce new columns into the same SELECT statement of the same model file, meaning Git must reconcile two different edits made in parallel on branches that diverged from the same commit.
Once feature_a is merged into main, the code in main contains the new column customer_country. When developer B then tries to merge feature_b, Git compares the modified file in feature_b with the updated file in main. Since both branches changed the same section of SQL, Git cannot automatically determine the correct combined output. It will not discard feature_b's changes; instead, Git requires the developer to manually merge both sets of additions, typically resulting in a combined SELECT clause with both customer_country and customer_address, unless the developer chooses otherwise.
This behavior is documented in Git fundamentals: when multiple developers modify the same file region, manual conflict resolution is required. Therefore, the statement claiming feature_b's changes "won't be added" is incorrect.
NEW QUESTION # 13
Choose a correct command for each statement. Answer:
Explanation:
Explanation:
Will always point to the latest version of the source schema
Correct Match: # defer
Allows to use objects built in the target schema with any downstream tool Correct Match: # dbt clone
3## Allows to safely modify objects built in the target schema
Correct Match: # defer
4## Is a point-in-time operation
Correct Match: # dbt clone
defer and dbt clone serve very different purposes in dbt, and understanding their behavior is essential for choosing the right command.
The --defer flag tells dbt to use already-built objects from a previous environment (often production) instead of rebuilding them. Because it always references the existing target schema's most recent objects, it "always points to the latest version of the source schema." Since no objects are overwritten when using defer, it also
"allows safely modifying objects built in the target schema"-your development environment uses production objects without altering them.
By contrast, dbt clone creates a point-in-time copy of existing relations. This cloned schema is static; it does not auto-update when source data changes. Therefore, clone is classified as a "point-in-time operation." Since clone copies physical tables/views into a new schema, downstream tools (BI dashboards, ML pipelines) can safely query the cloned environment without affecting production, making "allows to use objects built in the target schema with any downstream tool" the correct match.
Thus, defer is used for logic substitution without copies, while clone is used for replicable, point-in-time snapshots.
NEW QUESTION # 14
Consider this DAG:
app_data.detail_categories -> stg_detail_categories -> skills_with_details app_data.details -> stg_details -> lessons_with_details What will support making this DAG more modular? Choose 1 option.
A. Examine the SQL of lessons_with_details and skills_with_details to see if there is a candidate for an intermediate model.
B. Union stg_detail_categories and stg_details in the staging layer to reduce the need for downstream joins.
C. Combine lessons_with_details and skills_with_details into one wide table called curriculum_with_details.
D. Join stg_detail_categories and stg_details in the staging layer to reduce the need for downstream joins.
E. Consolidate the two staging models into one model and then use this downstream for both skills_with_details and curriculum_with_details.
Answer: A
Explanation:
Modularity in dbt means breaking transformations into small, reusable steps that are easy to reason about, test, and reuse. In this DAG, two staging models (stg_detail_categories and stg_details) feed into two mart- level models (skills_with_details and lessons_with_details). If there is repeated logic in those two mart models-such as common joins, filters, or derived fields-that logic is a good candidate to be extracted into an intermediate model.
Option E follows dbt best practices: inspect the SQL of lessons_with_details and skills_with_details and identify shared logic to move into a new intermediate model (for example, int_details_with_categories). Both downstream models can then reference this intermediate model using ref(), centralizing logic and improving maintainability.
Options A and B push joins/unions into the staging layer, which should focus on clean, 1:1 representations of sources-not combining multiple sources. Option C collapses distinct staging models into one, reducing clarity and breaking the source-to-staging contract. Option D creates an overly wide, less reusable final table instead of focusing on shared reusable logic.
Therefore, the action that truly increases modularity is Option E: creating an intermediate model based on common SQL patterns.
NEW QUESTION # 15
Which two are true about version controlling code with Git?
Choose 2 options.
A. All the code changes along the lifecycle of a project are tracked.
B. Git automatically creates versions of files with suffixes.
C. When bugs are raised, email notifications are automatically sent by Git to repository users.
D. Code can be reverted to a previous state.
E. Git prevents any sensitive fields from being saved in code.
Answer: A,D
Explanation:
The correct answers are B: All the code changes along the lifecycle of a project are tracked, and E: Code can be reverted to a previous state.
Git is a distributed version control system designed to maintain a complete, chronological history of all code changes. Every commit records who made the change, when it occurred, and what the modification included.
This ensures transparency, reproducibility, and accountability across the development lifecycle, which makes B correct. Git also allows users to revert code to any previous commit, branch, or tag, making E correct as well. This capability is critical for recovering from mistakes, undoing faulty deployments, and ensuring stable releases.
Option A is incorrect because Git does not create file versions with suffixes; instead, it stores changes as snapshots within a repository. File suffixing is not part of Git's functionality.
Option C is incorrect because Git does not automatically send email notifications. Notification mechanisms come from hosting platforms like GitHub, GitLab, or Bitbucket-not from Git itself.
Option D is incorrect because Git does not prevent committing sensitive information. Developers must manually ensure secrets are excluded via .gitignore, secret managers, or pre-commit hooks. Git will store whatever is committed unless prevented through tooling.
Thus, only B and E correctly describe how Git supports version control in analytics engineering and dbt workflows.
NEW QUESTION # 16
......
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