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100+ Free Dataiku Developer Certification Practice Questions

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2026 Statistics

Key Facts: Dataiku Developer Certification Exam

Free

Exam Cost

Dataiku Academy

80%

Passing Grade

Dataiku Academy FAQ

MCQ + hands-on

Assessment Format

Dataiku Academy (Developer track)

Coding tier

Certification Level

Dataiku Academy

Free edition / Cloud

Instance Required

Dataiku Academy FAQ

Not published

Question Count

Dataiku Academy

The Dataiku Developer Certification is a free, advanced coding credential from Dataiku Academy that validates your ability to code within Dataiku. It pairs an online multiple-choice knowledge assessment with a hands-on coding assessment on a Dataiku instance, with a documented passing grade of 80%; the exact question count and time limit are not published. Content spans coding in Dataiku (Python/R recipes, code environments), the dataiku and dataikuapi APIs, plugins, variables and scenario automation, custom models and webapps, and API-service deployment to the API node. Dataiku Cloud or the free edition is sufficient to complete it.

Sample Dataiku Developer Certification Practice Questions

Try these sample questions to test your Dataiku Developer Certification exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1In a Dataiku Python recipe, which sequence of calls reads an input dataset named "customers" into a Pandas DataFrame using the dataiku package?
A.df = dataiku.Dataset("customers").get_dataframe()
B.df = dataiku.read_csv("customers")
C.df = dataiku.Flow().load("customers").to_pandas()
D.df = dataikuapi.Dataset("customers").read()
Explanation: Inside a Python recipe you obtain a handle with dataiku.Dataset("name") and call get_dataframe() to read the whole dataset into a Pandas DataFrame, regardless of the storage backend. This is the canonical reading pattern documented for Python recipes.
2A Python recipe builds an output DataFrame whose columns differ from the existing output dataset schema. Which method writes the DataFrame AND updates the output dataset's schema to match the DataFrame in one call?
A.output.write_dataframe(df)
B.output.write_schema_from_dataframe(df)
C.output.write_with_schema(df)
D.output.append_dataframe(df)
Explanation: write_with_schema() writes the DataFrame and replaces the output dataset's schema with the DataFrame's schema on every run. It is the standard way to handle outputs whose columns change, though it should be used with care since downstream Flow steps depend on schema.
3When writing rows individually with dataiku.Dataset(...).get_writer(), why is it strongly recommended to use the Python "with" statement?
A.It enables multithreaded writing to the dataset
B.It compresses the output to Parquet by default
C.It automatically infers the schema from the first row written
D.It guarantees the writer is closed so all buffered rows are flushed; otherwise data may not be fully written
Explanation: The writer buffers rows and only commits them when closed. Using "with output.get_writer() as writer:" ensures the writer is closed and flushed even on error; for some backends (like SQL outputs) forgetting to close means no data is written at all.
4A dataset is far too large to fit in memory. Which dataiku.Dataset reading approach lets a Python recipe process it in fixed-size blocks?
A.get_dataframe(sample=True)
B.iter_dataframes(chunksize=N)
C.get_dataframe().chunk(N)
D.read_partitions(N)
Explanation: iter_dataframes() returns a generator yielding Pandas DataFrames of a fixed chunk size, letting you process datasets that do not fit in RAM without loading everything at once. There is also a row-by-row streaming API via iter_rows().
5By default, dataiku.Dataset("x").get_dataframe() infers column dtypes from the data rather than the dataset's declared schema. Which argument forces it to use the dataset schema's storage types instead?
A.get_dataframe(use_schema=True)
B.get_dataframe(coerce=True)
C.get_dataframe(strict_types=True)
D.get_dataframe(infer_with_pandas=False)
Explanation: get_dataframe() uses a Pandas read under the hood and by default infers dtypes from data. Passing infer_with_pandas=False makes it use the dataset schema's declared types, which is useful when inference produces unexpected types (e.g., a numeric ID read as int but needed as string).
6What is the primary purpose of a Dataiku code environment?
A.To provide an isolated set of Python or R packages (and a language version) that recipes, notebooks, and models can use
B.To store project variables shared across recipes
C.To define the schema of input and output datasets
D.To control which users can run a given scenario
Explanation: A code environment is an isolated, reproducible set of packages and a language version (a Python virtualenv/conda env or an R env) that you attach to recipes, notebooks, webapps, and models. This keeps dependencies separate from the DSS builtin environment and from other projects.
7You need a Python recipe to run with a specific set of packages different from the instance default. How do you make the recipe use a particular managed code environment?
A.Add a #env directive at the top of the recipe code
B.Select the code environment in the recipe's Advanced (code env) settings
C.Install the packages into the recipe with pip at runtime
D.Set a project variable named CODE_ENV
Explanation: Each code recipe (and notebook, webapp, or model) has a code environment selection in its advanced settings where you can choose the instance default or a specific named code environment. This binds execution to that environment's packages and language version.
8Which statement about using a managed code environment for containerized (Kubernetes) execution is correct?
A.Containerized execution ignores code environments entirely
B.Any code environment, managed or not, can be used in containers without configuration
C.Non-managed code environments (named external Conda env or non-managed path) cannot be used for containerized execution
D.You must reinstall packages with pip inside the container at each run
Explanation: Container-based execution requires a managed code environment because DSS must know exactly which packages to bake into the Docker image. Non-managed environments (named external conda or non-managed path), custom interpreters from PATH, and extra PYTHONPATH entries are not compatible with containerized execution.
9What is the difference between a Dataiku code notebook and a Python recipe?
A.Notebooks can read datasets but recipes cannot
B.A notebook is an interactive scratchpad for exploration that is not part of the Flow, whereas a recipe is a Flow object that transforms inputs into outputs
C.Recipes run only on the API node, notebooks only on the design node
D.Notebooks must be written in R, recipes only in Python
Explanation: Code notebooks (Jupyter-based for Python/R) are interactive environments for exploration and prototyping and are not part of the Flow. A code recipe is a persistent Flow component with declared input and output datasets that is run as part of building the Flow. Code is often prototyped in a notebook then deployed as a recipe.
10A Python recipe declares an output dataset whose schema you want to set explicitly before writing rows with a writer. Which method sets the output schema as a list of column definitions?
A.output.set_columns([...])
B.output.create_schema([...])
C.output.define_schema([...])
D.output.write_schema([{"name": "origin", "type": "string"}, ...])
Explanation: write_schema() takes a list of column definition dicts (each with name and type) and sets the output dataset's schema. It is typically called before opening a writer with get_writer() to write rows whose structure matches the declared schema.

About the Dataiku Developer Certification Exam

The Dataiku Developer Certification validates your ability to code within Dataiku, the coding tier above the visual Advanced Designer and ML Practitioner certifications. It assesses writing and debugging Python and R in code notebooks and recipes, reading and writing data with the dataiku.Dataset API, and managing code environments and shared project libraries. It also covers the in-recipe dataiku package and the public REST API (dataikuapi), customizing metrics, checks, and scenarios with code, developing plugins, building code webapps (Standard, Bokeh, Dash, Shiny), creating custom ML models, and deploying real-time API services to the API node. The certification requires access to a Dataiku instance, and Dataiku Cloud or the free edition is compatible.

Assessment

Question count not published by the exam provider

Time Limit

Approximately 120 minutes (learning path duration)

Passing Score

80%

Exam Fee

Free (Dataiku Academy)

Dataiku Developer Certification Exam Content Outline

20%

Coding in Dataiku

Write, run, and debug Python and R in code notebooks and recipes; read with dataiku.Dataset(...).get_dataframe() and write with write_with_schema or a row-by-row writer; handle chunked reads, schemas, and encoding; and manage code environments (virtualenv or conda) and the project library for shared code.

17%

Dataiku APIs

Use the in-recipe dataiku package (Dataset, Folder, Model, get_custom_variables) and the public REST API client dataikuapi.DSSClient; obtain a local client with dataiku.api_client(); read and set project variables; and create recipes, run scenarios, and query the API node with APINodeClient.

13%

Plugins development

Build reusable plugin components such as custom recipes; configure plugin.json (id, label, version, recipesCategory) and recipe.json (meta, inputRoles, outputRoles, params); use presets for shared settings and COLUMN/COLUMNS parameter types; and read config with get_recipe_config and roles with get_input_names_for_role.

25%

Variables, scenarios and automation

Define project global, project local, and instance global variables (JSON, referenced with ${name} or variables["name"]); build step-based and custom scenarios with time-based, dataset-change, SQL-query-change, and Python triggers; add steps and reporters; use conditional execution and scenario variables; and code custom metrics and checks.

12%

Custom models and webapps

Create custom (code) ML models that follow the fit/predict estimator contract inside the visual ML interface, and build Standard (HTML/CSS/JS with a Flask or FastAPI backend), Bokeh, Dash, and Shiny webapps that read authorized datasets and query deployed endpoints, then publish them on dashboards.

13%

Project deployment and API services

Design API services and endpoints in the API Designer (Python prediction, Python function, SQL query, dataset lookup); package versions and push them through the API Deployer to API nodes; perform real-time scoring with enrichment, A/B testing, and multi-version evaluation.

How to Pass the Dataiku Developer Certification Exam

What You Need to Know

  • Passing score: 80%
  • Assessment: Question count not published by the exam provider
  • Time limit: Approximately 120 minutes (learning path duration)
  • Exam fee: Free

Keys to Passing

  • Work through all 100 available questions
  • Review every answer and explanation
  • Track weak areas and revisit them
  • Use our AI tutor for tough concepts

Dataiku Developer Certification Study Tips from Top Performers

1Master the dataiku.Dataset read/write API: get_dataframe(), write_with_schema(), iter_dataframes() for chunking, get_writer() with the with statement, and infer_with_pandas=False for schema-typed reads.
2Know the difference between the in-recipe dataiku package and the public dataikuapi client, and remember dataiku.api_client() returns a DSSClient for the local instance without a host or key.
3Drill variables thoroughly: project global vs local (local is not bundled), ${name} vs variables["name"] behavior on missing keys, and that get_custom_variables() returns strings you must cast.
4Understand scenario triggers cold, especially why a dataset-change trigger misses SQL data changes and when to use a SQL query change trigger returning COUNT or MAX.
5Practice the deployment chain end to end: design in the API Designer, package a version, push through the API Deployer, deploy to an API node, then score with APINodeClient.predict_record().
6Build at least one plugin custom recipe so plugin.json vs recipe.json, input/output roles, params, presets, and get_recipe_config() are second nature.

Frequently Asked Questions

What are the key facts for the Dataiku Developer Certification?

It is a free certification from Dataiku Academy with a documented passing grade of 80%. It combines an online multiple-choice knowledge assessment with a hands-on coding assessment completed on a Dataiku instance (Dataiku Cloud or the free edition works). The exact question count and time limit are not published.

How is the Developer Certification different from the ML Practitioner certification?

The Developer Certification is the coding tier; it validates your ability to write Python and R inside Dataiku, use its APIs, build plugins and webapps, automate with scenarios, and deploy API services. The ML Practitioner certification focuses on visual machine learning and interactive statistics rather than coding.

Do I need to write code to pass this certification?

Yes. Beyond the multiple-choice knowledge assessment, the Developer track includes a hands-on coding assessment, so you should be comfortable with Python (and ideally R), the dataiku.Dataset API, code environments, scenarios, and the dataikuapi public API.

What is the passing score?

Dataiku Academy certificate assessments use an 80% passing grade. Dataiku does not publish a separate pass-rate percentage for the Developer Certification.

What software do I need to take the assessment?

You need access to a Dataiku instance. For this certification, Dataiku Cloud or the free edition is compatible, so you can prepare and complete the hands-on portion without a paid license.

Which topics are most important to study?

Prioritize coding in Dataiku (Python recipes, the dataiku package, code environments) and automation (variables, scenarios, triggers, reporters), then the public API (dataikuapi), plugin components, code webapps, custom models, and deploying real-time API services to the API node.