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100+ Free Dataiku Developer Certification Practice Questions
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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 Practice Questions
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