Data Build Tool DBT-Analytics-Engineering Exam Questions

Master Your Preparation for the Data Build Tool DBT-Analytics-Engineering

We give our customers with the finest DBT-Analytics-Engineering preparation material available in the form of pdf .Data Build Tool DBT-Analytics-Engineering exam questions answers are carefully analyzed and crafted with the latest exam patterns by our experts. This steadfast commitment to excellence has built unbreakable trust among countless people who aspire to advance their careers. Our learning resources are designed to help our students attain an impressive score of over 97% in the Data Build Tool DBT-Analytics-Engineering exam, thanks to our effective study materials. We appreciate your time and investments, ensuring you receive the best resources. Rest assured, we leave no room for error, committed to excellence.

Friendly Support Available 24/7:

If you face issues with our Data Build Tool DBT-Analytics-Engineering Exam dumps, our customer support specialists are ready to assist you promptly. Your success is our priority, we believe in quality and our customers are our 1st priority. Our team is available 24/7 to offer guidance and support for your Data Build Tool DBT-Analytics-Engineering exam preparation. Feel free to reach out with any questions if you find any difficulty or confusion. We are committed to ensuring you have the necessary study materials to excel.

Verified and approved Dumps for Data Build Tool DBT-Analytics-Engineering:

Our team of IT experts delivers the most accurate and reliable DBT-Analytics-Engineering dumps for your Data Build Tool DBT-Analytics-Engineering exam. All the study material is approved and verified by our team regarding Data Build Tool DBT-Analytics-Engineering dumps. Our meticulously verified material, endorsed by our IT experts, ensures that you excel with distinction in the DBT-Analytics-Engineering exam. This top-tier resource, consisting of DBT-Analytics-Engineering exam questions answers, mirrors the actual exam format, facilitating effective preparation. Our committed team works tirelessly to make sure that our customers can confidently pass their exams on their first attempt, backed by the assurance that our DBT-Analytics-Engineering dumps are the best and have been thoroughly approved by our experts.

Data Build Tool DBT-Analytics-Engineering Questions:

Embark on your certification journey with confidence as we are providing most reliable DBT-Analytics-Engineering dumps from Microsoft. Our commitment to your success comes with a 100% passing guarantee, ensuring that you successfully navigate your Data Build Tool DBT-Analytics-Engineering exam on your initial attempt. Our dedicated team of seasoned experts has intricately designed our Data Build Tool DBT-Analytics-Engineering dumps PDF to align seamlessly with the actual exam question answers. Trust our comprehensive DBT-Analytics-Engineering exam questions answers to be your reliable companion for acing the DBT-Analytics-Engineering certification.

Data Build Tool DBT-Analytics-Engineering Sample Questions

Question # 1

You have just executed dbt run on this model:select * from {{ source("{{ env_var('input') }}", 'table_name') }}and received this error:Compilation Error in model my_modelexpected token ':', got '}'line 14{{ source({{ env_var('input') }}, 'table_name') }}How can you debug this?

A. Incorporate a log function into your macro.  
B. Check your SQL to see if you quoted something incorrectly.  
C. Check your Jinja and see if you nested your curly brackets.  
D. Take a look at the compiled code.  

Show Answer

Question # 2

Consider these SQL and YAML files for the model model_a:models/staging/model_a.sql{{ config(materialized = "view") }}with customers as (...)dbt_project.ymlmodels:my_new_project:+materialized: tablestaging:+materialized: ephemeralWhich is true about model_a? Choose 1 option.Options:

A.Select statements made from the database on top of model_a and transformationprocessing within model_a will be quicker, but the data will only be as up to date as the lastdbt run.
B.Select statements made from the database on top of model_a will result in an error
C.Select statements made from the database on top of model_a will be slower, but the datawill always be up to date.
D.Select statements made from the database on top of model_a will be quicker, but the datawill only be as up to date as the last dbt run.(Note: A and D are duplicates — typical exam formatting.) 

Show Answer

Question # 3

You want to run and test the models, tests, and snapshots you have added ormodified in development.Which will you invoke? Choose 1 option.Options:

A.dbt build --select state:modified --defer <path/to/artifacts> 
B.dbt run --select state:modified --defer --state <path/to/artifacts>dbt test --select state:modified --defer --state <path/to/artifacts>
C.dbt build --select state:modified --defer --state <path/to/artifacts> 
D. dbt run --select state:modified --state <path/to/artifacts>dbt test --select state:modified --state <path/to/artifacts>
E.dbt build --select state:modified --state <path/to/artifacts>

Show Answer

Question # 4

Your model has a contract on it.When renaming a field, you get this error:This model has an enforced contract that failed.Please ensure the name, data_type, and number of columns in your contract matchthe columns in your model's definition.| column_name | definition_type | contract_type | mismatch_reason ||-------------|------------------|----------------|-----------------------|| ORDER_ID | TEXT | TEXT | missing in definition || ORDER_KEY | TEXT | | missing in contract |Which two will fix the error? Choose 2 options. 

A. Remove order_id from the contract.  
B. Remove order_key from the contract.  
C. Remove order_id from the model SQL.  
D. Add order_key to the contract.  
E. Add order_key to the model SQL.  

Show Answer

Question # 5

Which two dbt commands work with dbt retry?Choose 2 options.

A. run-operation  
B. parse  
C. debug  
D. deps  
E. snapshot  

Show Answer

Question # 6

You work at an e-commerce company and a vendor provides their inventory data viaCSV file uploads to an S3 bucket.How do you prep the data for dbt transformations?Choose 1 option.

A. Create a dbt model with a view querying the external table directly.  
B. Run a pre-hook to create a temporary table and query from it in a staging model.  
C. Use dbt seed to stage the data in your data platform.  
D. Declare the external table as a source using the external configuration.  

Show Answer

Question # 7

You have written this new agg_completed_tasks dbt model:with tasks as (select * from {{ ref('stg_tasks') }})selectuser_id,{% for task in tasks %}sum(casewhen task_name = '{{ task }}' and state = 'completed'then 1else 0end) as {{ task }}_completed{% endfor %}from tasksgroup by 1The dbt model compiles to:with tasks as (select * from analytics.dbt_user.stg_tasks)selectuser_id,from tasksgroup by 1The case when statement did not populate in the compiled SQL. Why?

A. Because there is not a {% if not loop.last %}{% endif %} to compile a valid case when statement.  
B. Because the Jinja for-loop should be written with {{ }} instead of {% %}.  
C. Because there is no {% set tasks %} statement in the model defining the tasks variable.  
D. Because there is not a task_name column in stg_tasks.  

Show Answer

Question # 8

An analyst on your team has informed you that the business logic creating theis_active column of your stg_users model is incorrect.You update the column logic to:casewhen state = 'Active'then trueelse falseend as is_activeWhich test can you add on the state column to support your expectations of thesource data? Choose 1 option.

A.- name: statetests:- accepted_values:values: ['active', 'churned', 'trial']- not_null 
B.- name: is_activetests:- accepted_values:values: ['active', 'churned', 'trial']- not_null
C.- name: statetests:- not_null- unique 
D.- name: is_activetests:- not_null- unique 

Show Answer

Question # 9

You are working on a complex dbt model with many Common Table Expressions (CTEs)and decide to move some of those CTEs into their own model to make your code moremodular.Is this a benefit of this approach?The new model can be documented to explain its purpose and the logic it contains

A. Yes  
B. No  

Show Answer

Question # 10

You are creating a fct_tasks model with this CTE:with tasks as (select * from {{ ref('stg_tasks') }})You receive this compilation error in dbt:Compilation Error in model fct_tasks (models/marts/fct_tasks.sql)Model 'model.dbt_project.fct_tasks' (models/marts/fct_tasks.sql) depends on a nodenamed 'stg_tasks' which was not foundWhich is correct? Choose 1 option.Options:

A. stg_tasks is configured as ephemeral.  
B. There is no dbt model called stg_tasks.  
C. There is no stg_tasks in the data warehouse.  
D. A stg_tasks has not been defined in schema.yml.  

Show Answer

Question # 11

Consider this DAG:model_a model_c model_emodel_b model_d model_f(With model_c and model_d both feeding into the final layer.)You execute:dbt run --fail-fastin production with 2 threads. During the run, model_b and model_c are running in parallelwhen model_b returns an error.Assume there are no other errors in the model files, and model_c was still running whenmodel_b failed.Which model or models will successfully build as part of this dbt run? Choose 1option.

A. model_a, model_c, model_d, model_e, model_f  
B. model_a, model_c  
C. model_a  
D. model_a, model_c, model_e  

Show Answer

Question # 12

Given this dbt_project.yml:name: "jaffle_shop"version: "1.0.0"config-version: 2profile: "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: tableWhen 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, 0seed files, 0 sources, 0 exposures, 0 metrics19:36:16 Concurrency: 1 threads (target='default')19:36:17 Finished running 1 view model in 3.35s.19:36:17 Completed successfully19:36:17 Done. PASS=1 WARN=0 ERROR=0 SKIP=0 TOTAL=1Which could be a root cause of why the model was not materialized as a table?The target-path is incorrectly configured.

A. Yes  
B. No  

Show Answer

Question # 13

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. Underlying data must be accessible on your data platform.  
D. Raw data must be cleaned.  

Show Answer