Anaconda Certified Professional (ACP) — Questions and Answers

Question 1: Which Anaconda-hosted service allows you to upload and share your built conda packages publicly or within a team?

Binstar
Anaconda.org (Correct answer)
conda-forge
PyPI

Correct answer: Anaconda.org

Anaconda.org (formerly Binstar) is the cloud repository where users and organizations can upload, manage, and share conda packages and channels.

Question 2: When building a data pipeline, what does the term 'idempotency' mean in the context of pipeline task execution?

A task can process data from any source format
A task runs in parallel with zero overhead
A task automatically retries on failure
Running a task multiple times produces the same result as running it once (Correct answer)

Correct answer: Running a task multiple times produces the same result as running it once

An idempotent pipeline task can be safely re-executed without side effects — running it once or ten times yields the same final state, which is critical for reliable data engineering.

Question 3: In conda packaging terminology, what is a 'noarch' package?

A package compiled without architecture-specific optimizations
A package that skips the test phase
A package with no dependencies
A platform-independent package that installs on any OS/architecture (Correct answer)

Correct answer: A platform-independent package that installs on any OS/architecture

A `noarch` package (typically `noarch: python`) contains no compiled binaries and can be installed on any platform, eliminating the need to build per-OS variants.

Question 4: Which Python library provides the `Pipeline` class to chain preprocessing steps and a final estimator into a single reusable workflow object?

numpy
scikit-learn (Correct answer)
scipy
pandas

Correct answer: scikit-learn

scikit-learn's `Pipeline` chains transformers and a final estimator so that `fit` and `predict` calls automatically apply all steps in sequence.

Question 5: Which conda-build feature allows you to build multiple variants of a package (e.g., different Python versions) from a single recipe?

build_variants.cfg
conda_build_config.yaml (Correct answer)
conda-matrix
variant_config.yaml

Correct answer: conda_build_config.yaml

The `conda_build_config.yaml` file defines variant matrices (e.g., `python: [3.9, 3.10, 3.11]`) so conda-build automatically produces one package per combination.

Question 6: What file is required at the root of a conda package recipe to define its build metadata and dependencies?

setup.py
build.sh
conda.yaml
meta.yaml (Correct answer)

Correct answer: meta.yaml

The meta.yaml file is the recipe descriptor that defines package name, version, source, build requirements, and runtime dependencies for conda-build.

Question 7: Which tool or methodology is most appropriate for analyzing machine learning & ai integration outcomes?

Prioritizing relationships over professional standards
Maintaining strict formality that inhibits collaboration
Adjusting boundaries based on individual situations without guidelines
Maintaining professional boundaries while building collaborative relationships (Correct answer)

Correct answer: Maintaining professional boundaries while building collaborative relationships

Maintaining professional boundaries while building collaborative relationships is the correct approach because effective machine learning & ai integration in the anaconda certified professional field requires adherence to professional standards, evidence-based practices, and systematic methodology. This approach ensures consistent, high-quality outcomes while maintaining professional accountability.

Question 8: When auditing a production conda environment for compliance, which approach provides the most complete record of installed package provenance?

Running `conda list --export` and storing the output with build strings and channel URLs (Correct answer)
Using `pip freeze` inside the environment
Reviewing the conda cache directory timestamps
Checking the `environment.yml` file

Correct answer: Running `conda list --export` and storing the output with build strings and channel URLs

`conda list --export` outputs exact package names, versions, build strings, and channel URLs, providing full provenance information needed for compliance documentation.

Question 9: Which Python library provides the `Parquet` file format support for high-performance columnar storage of DataFrames?

openpyxl
h5py
pyarrow (Correct answer)
feather

Correct answer: pyarrow

`pyarrow` (and `fastparquet`) enables pandas to read/write Parquet files via `df.to_parquet()` and `pd.read_parquet()`, offering efficient columnar compression for large datasets.

Question 10: In a data engineering context, what is the purpose of using Dask instead of pandas for large dataset processing in Python?

Dask replaces pandas with a SQL-only interface
Dask enables parallel and out-of-core computation on datasets larger than RAM (Correct answer)
Dask is only used for streaming real-time data
Dask provides faster single-threaded operations than pandas

Correct answer: Dask enables parallel and out-of-core computation on datasets larger than RAM

Dask partitions large datasets into chunks and processes them in parallel across cores or a cluster, handling data that exceeds available memory with a pandas-compatible API.

Question 11: In a meta.yaml recipe, where do you specify packages that are needed ONLY during the build process (e.g., compilers)?

requirements: test
requirements: host
requirements: run
requirements: build (Correct answer)

Correct answer: requirements: build

The `requirements: build` section lists cross-compilation tools and compilers that run on the build machine but are not needed in the final environment.

Question 12: A new regulation impacts package management & environment configuration procedures. What should a ACP professional do first?

Interpreting regulations loosely to allow maximum flexibility
Complying only with regulations that have enforcement mechanisms
Ensuring compliance with current regulatory requirements and standards (Correct answer)
Delegating compliance oversight to administrative staff

Correct answer: Ensuring compliance with current regulatory requirements and standards

Ensuring compliance with current regulatory requirements and standards is the correct approach because effective package management & environment configuration in the anaconda certified professional field requires adherence to professional standards, evidence-based practices, and systematic methodology. This approach ensures consistent, high-quality outcomes while maintaining professional accountability.

Question 13: Which Python library is specifically designed for defining, scheduling, and monitoring data pipeline workflows as Directed Acyclic Graphs (DAGs)?

Luigi
Apache Airflow
Prefect
All of the above (Correct answer)

Correct answer: All of the above

Luigi, Apache Airflow, and Prefect are all Python-native workflow orchestration frameworks that model pipelines as DAGs with scheduling and monitoring capabilities.

Question 14: Which tool or methodology is most appropriate for analyzing python programming & data science outcomes?

Maintaining professional boundaries while building collaborative relationships (Correct answer)
Maintaining strict formality that inhibits collaboration
Prioritizing relationships over professional standards
Adjusting boundaries based on individual situations without guidelines

Correct answer: Maintaining professional boundaries while building collaborative relationships

Maintaining professional boundaries while building collaborative relationships is the correct approach because effective python programming & data science in the anaconda certified professional field requires adherence to professional standards, evidence-based practices, and systematic methodology. This approach ensures consistent, high-quality outcomes while maintaining professional accountability.

Question 15: What does CVE stand for in the context of Anaconda security scanning?

Common Vulnerability Exposure
Conda Version Error
Common Vulnerabilities and Exposures (Correct answer)
Certified Vulnerability Entry

Correct answer: Common Vulnerabilities and Exposures

CVE stands for Common Vulnerabilities and Exposures, the industry-standard identifier for publicly known security flaws.

Question 16: Which command checks a built conda package for common packaging issues such as missing files or incorrect metadata?

conda verify
conda inspect (Correct answer)
conda audit
conda-build --check

Correct answer: conda inspect

`conda inspect` provides subcommands like `conda inspect linkages` and `conda inspect objects` to audit installed packages for linking issues and metadata correctness.

Question 17: In pandas, which method is used to apply a custom function to every row or column of a DataFrame?

df.map()
df.transform()
df.apply() (Correct answer)
df.execute()

Correct answer: df.apply()

`df.apply()` applies a function along an axis (rows with `axis=1`, columns with `axis=0`), enabling custom transformations across the entire DataFrame.

Question 18: Which file format does `conda audit` primarily reference to identify vulnerable package versions?

environment.yml
conda-lock.yml
NIST NVD / OSV database feeds (Correct answer)
requirements.txt

Correct answer: NIST NVD / OSV database feeds

`conda audit` queries vulnerability databases such as the NIST National Vulnerability Database (NVD) and OSV to match installed packages against known CVEs.

Question 19: What is an advantage of using AI in data analysis?

Increases manual workload
Automates data analysis and pattern recognition (Correct answer)
Reduces accuracy in predictions
Requires no computational resources

Correct answer: Automates data analysis and pattern recognition

AI enables automation of complex data analysis, allowing for faster insights and pattern recognition across large datasets.

Question 20: Which tool or methodology is most appropriate for analyzing data visualization & analysis outcomes?

Maintaining strict formality that inhibits collaboration
Maintaining professional boundaries while building collaborative relationships (Correct answer)
Adjusting boundaries based on individual situations without guidelines
Prioritizing relationships over professional standards

Correct answer: Maintaining professional boundaries while building collaborative relationships

Maintaining professional boundaries while building collaborative relationships is the correct approach because effective data visualization & analysis in the anaconda certified professional field requires adherence to professional standards, evidence-based practices, and systematic methodology. This approach ensures consistent, high-quality outcomes while maintaining professional accountability.

Question 21: Which Anaconda feature allows administrators to audit which users installed or updated packages in a shared enterprise environment?

PM2 process logs
conda history --users
Anaconda Nucleus activity logs (Correct answer)
environment.yml diff tracking

Correct answer: Anaconda Nucleus activity logs

Anaconda Nucleus and enterprise repository solutions maintain activity logs that record user actions such as package installs and updates for compliance auditing.

Question 22: In an ETL pipeline using pandas, which operation is used to combine two DataFrames based on a shared key column (similar to a SQL JOIN)?

pd.join_tables()
pd.concat()
pd.combine()
pd.merge() (Correct answer)

Correct answer: pd.merge()

`pd.merge()` performs database-style joins between DataFrames on one or more key columns, supporting inner, outer, left, and right join types.

Question 23: Which method in pandas handles missing values by filling them with a specified value or strategy (e.g., forward fill)?

df.impute()
df.replace_nan()
df.fillna() (Correct answer)
df.dropna()

Correct answer: df.fillna()

`df.fillna(value)` replaces NaN values with a constant or uses methods like `ffill` (forward fill) and `bfill` (backward fill) to propagate adjacent valid values.

Question 24: What is an advantage of using Conda over pip?

Conda is only for machine learning projects
Conda does not handle dependencies
Conda supports multiple programming languages (Correct answer)
Pip provides better dependency resolution

Correct answer: Conda supports multiple programming languages

Conda manages packages and dependencies across multiple languages, whereas pip is limited to Python packages only.

Question 25: What command renders the final meta.yaml for a recipe after applying all variant substitutions, without actually building it?

conda inspect recipe
conda-build --render
conda-build --dry-run
conda render (Correct answer)

Correct answer: conda render

`conda render` processes all Jinja2 templating and variant configs in a recipe and outputs the fully resolved meta.yaml without triggering a build.

Question 26: Which conda command exports a complete list of all packages in the current environment to a file for reproducibility?

conda freeze > requirements.txt
conda save environment.yml
conda env export > environment.yml (Correct answer)
conda list --export > packages.txt

Correct answer: conda env export > environment.yml

`conda env export > environment.yml` captures all packages, versions, and channels in the active environment into a YAML file that can recreate the environment elsewhere.

Question 27: Which scenario would require a anaconda certified professional professional to escalate a data visualization & analysis concern?

Using feedback solely for personnel evaluations
Collecting feedback only during formal review periods
Creating feedback mechanisms that encourage continuous improvement (Correct answer)
Discouraging critical feedback to maintain team morale

Correct answer: Creating feedback mechanisms that encourage continuous improvement

Creating feedback mechanisms that encourage continuous improvement is the correct approach because effective data visualization & analysis in the anaconda certified professional field requires adherence to professional standards, evidence-based practices, and systematic methodology. This approach ensures consistent, high-quality outcomes while maintaining professional accountability.

Question 28: In a conda recipe, where would you add a `run_test.py` script to verify the package works after installation?

In the test section of meta.yaml or as a run_test.py file in the recipe directory (Correct answer)
In the source section of meta.yaml
In the build section of meta.yaml
In a separate test-requirements.txt file

Correct answer: In the test section of meta.yaml or as a run_test.py file in the recipe directory

The `test` section in meta.yaml (or a `run_test.py` file alongside the recipe) specifies imports, commands, and scripts that conda-build runs to validate the installed package.

Question 29: Which command is used to create a new virtual environment in Anaconda?

python -m venv
conda create (Correct answer)
pip install
virtualenv new

Correct answer: conda create

The `conda create` command allows users to create isolated virtual environments with specific dependencies.

Question 30: Which Python library is commonly used for data analysis?

Pandas (Correct answer)
Matplotlib
TensorFlow
Seaborn

Correct answer: Pandas

Pandas is a powerful Python library used for data manipulation and analysis, providing data structures like DataFrames and Series.

Question 31: What is the purpose of NumPy in Python?

Building machine learning models
Creating interactive plots
Handling multi-dimensional arrays (Correct answer)
Performing web scraping

Correct answer: Handling multi-dimensional arrays

NumPy provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on them.

Question 32: In pandas, which method converts a column's data type (e.g., from object/string to numeric or datetime)?

df['col'].astype() (Correct answer)
df['col'].cast()
df['col'].to_type()
df['col'].convert()

Correct answer: df['col'].astype()

`df['col'].astype(dtype)` casts a Series to the specified dtype (e.g., `int64`, `float32`, `str`), and `pd.to_datetime()` / `pd.to_numeric()` handle specialized conversions.

Question 33: In the context of anaconda certified professional, which principle most directly governs data visualization & analysis practices?

Applying evidence-based methodologies with peer-reviewed support (Correct answer)
Using trial-and-error without systematic documentation
Following popular trends without evaluating their applicability
Relying exclusively on vendor-provided solutions

Correct answer: Applying evidence-based methodologies with peer-reviewed support

Applying evidence-based methodologies with peer-reviewed support is the correct approach because effective data visualization & analysis in the anaconda certified professional field requires adherence to professional standards, evidence-based practices, and systematic methodology. This approach ensures consistent, high-quality outcomes while maintaining professional accountability.

Question 34: When running `conda audit` on an environment, what output indicates that a package has a critical severity vulnerability?

A CRITICAL severity tag with the associated CVE ID (Correct answer)
An asterisk (*) next to the package name
A yellow WARNING label
A broken-pipe error in the terminal

Correct answer: A CRITICAL severity tag with the associated CVE ID

`conda audit` outputs vulnerability entries with severity labels (LOW, MEDIUM, HIGH, CRITICAL) alongside the CVE identifier for traceability.

Question 35: In Anaconda's role-based access control (RBAC), which role typically has permission to publish packages to a private channel?

Viewer
Read-only collaborator
Anonymous user
Contributor or higher (e.g., Owner) (Correct answer)

Correct answer: Contributor or higher (e.g., Owner)

In Anaconda's RBAC model, Contributor or Owner roles have the necessary permissions to upload and publish packages to private organizational channels.

Question 36: What is the recommended frequency for reviewing and updating machine learning & ai integration protocols?

Relying on periodic external audits as the sole evaluation method
Tracking activity volume without measuring quality
Reviewing results only at year-end
Monitoring outcomes through regular data collection and trend analysis (Correct answer)

Correct answer: Monitoring outcomes through regular data collection and trend analysis

Monitoring outcomes through regular data collection and trend analysis is the correct approach because effective machine learning & ai integration in the anaconda certified professional field requires adherence to professional standards, evidence-based practices, and systematic methodology. This approach ensures consistent, high-quality outcomes while maintaining professional accountability.

Question 37: In meta.yaml, which top-level key defines the scripts (build.sh on Linux/macOS and bld.bat on Windows) used to compile and install the package?

source
about
build (Correct answer)
test

Correct answer: build

The `build` section in meta.yaml controls the build scripts, number (build string), skip conditions, and entry points for the package.

Question 38: In the context of anaconda certified professional, which principle most directly governs package management & environment configuration practices?

Applying evidence-based methodologies with peer-reviewed support (Correct answer)
Following popular trends without evaluating their applicability
Relying exclusively on vendor-provided solutions
Using trial-and-error without systematic documentation

Correct answer: Applying evidence-based methodologies with peer-reviewed support

Applying evidence-based methodologies with peer-reviewed support is the correct approach because effective package management & environment configuration in the anaconda certified professional field requires adherence to professional standards, evidence-based practices, and systematic methodology. This approach ensures consistent, high-quality outcomes while maintaining professional accountability.

Question 39: Which pandas method stacks a DataFrame from wide format (one column per variable) to long format (one row per observation)?

df.pivot()
pd.wide_to_long()
df.melt() (Correct answer)
df.stack()

Correct answer: df.melt()

`df.melt()` unpivots a DataFrame from wide to long format by converting specified columns into rows, creating `variable` and `value` columns.

Question 40: Which technique is commonly used to train AI models?

Supervised learning (Correct answer)
Random sampling
Hard coding patterns
Automated data sorting

Correct answer: Supervised learning

Supervised learning is a machine learning technique where models are trained using labeled datasets to make predictions.

Question 41: What is the primary advantage of using Anaconda for Python data science?

Is not recommended for beginners
Includes pre-installed libraries and package management (Correct answer)
Requires manual installation of dependencies
Does not support machine learning libraries

Correct answer: Includes pre-installed libraries and package management

Anaconda provides an easy-to-use platform with pre-installed libraries, a package manager, and an isolated environment for data science projects.

Question 42: Which pandas method reads a CSV file into a DataFrame and can handle large files by specifying a `chunksize` parameter?

pd.read_csv() (Correct answer)
pd.load_csv()
pd.read_table()
pd.from_csv()

Correct answer: pd.read_csv()

`pd.read_csv()` is the standard function for loading CSV data into a DataFrame, and its `chunksize` parameter returns an iterator of DataFrame chunks for memory-efficient processing.

Question 43: When processing a large dataset in chunks to avoid memory overflow, which pandas parameter in `read_csv()` controls how many rows are loaded per iteration?

max_rows
batch_size
chunksize (Correct answer)
nrows

Correct answer: chunksize

Setting `chunksize=N` in `pd.read_csv()` returns a `TextFileReader` iterator where each iteration yields a DataFrame of N rows.

Question 44: In pandas, what does `groupby()` followed by `agg()` allow you to do?

Apply multiple aggregation functions to groups simultaneously (Correct answer)
Sort a DataFrame by multiple columns
Filter rows based on group membership
Join two DataFrames on a common group key

Correct answer: Apply multiple aggregation functions to groups simultaneously

`df.groupby('col').agg({'col1': 'sum', 'col2': 'mean'})` groups rows and applies different aggregation functions to different columns in one vectorized operation.

Question 45: Which SQLAlchemy function is used alongside pandas `read_sql()` to connect to a database and execute SQL queries into a DataFrame?

open_session()
connect_db()
create_engine() (Correct answer)
make_connection()

Correct answer: create_engine()

`create_engine('dialect+driver://user:pass@host/db')` from SQLAlchemy creates a connection engine that pandas `read_sql()` uses to execute queries and return results as a DataFrame.

Question 46: What is the primary purpose of Seaborn in data visualization?

Statistical data visualization (Correct answer)
Creating simple text reports
Generating machine learning models
Managing databases

Correct answer: Statistical data visualization

Seaborn is built on Matplotlib and specializes in statistical data visualization, offering beautiful and informative visualizations with minimal code.

Question 47: After building a conda package locally, which flag do you use with `conda install` to install it directly from the local build output directory?

--local
--use-local (Correct answer)
--offline
--file

Correct answer: --use-local

The `--use-local` flag instructs conda to search the local package cache (typically `~/anaconda3/conda-bld/`) before checking remote channels.

Question 48: What does the `conda convert` command allow you to do with an existing conda package?

Repackage a built artifact for a different platform/OS (Correct answer)
Convert pip packages to conda format
Merge two packages into one
Convert a .conda file to .whl format

Correct answer: Repackage a built artifact for a different platform/OS

`conda convert` repackages a compiled conda artifact for alternative platforms (e.g., from linux-64 to osx-64) when no compiled C extensions are involved.

Question 49: What is the purpose of the `build_number` field in a meta.yaml recipe?

Defines the number of parallel build jobs
Sets the Python version for the build
Distinguishes multiple builds of the same package version (Correct answer)
Specifies the conda-build version required

Correct answer: Distinguishes multiple builds of the same package version

The `build_number` increments when a recipe is rebuilt without changing the package version, allowing conda to distinguish between different builds of the same release.

Question 50: What is the primary use of Jupyter Notebook in Python?

Compiling Python code into executables
Building desktop applications
Developing video games
Creating interactive coding environments (Correct answer)

Correct answer: Creating interactive coding environments

Jupyter Notebook provides an interactive environment for writing and running Python code, visualizing data, and documenting workflows.

Question 51: How can a user switch to an existing Conda environment?

conda activate (Correct answer)
conda use
conda start
conda enter

Correct answer: conda activate

The `conda activate` command allows users to switch to a specific Conda environment for package management and execution.

Question 52: When configuring a conda channel with `channel_priority: strict`, what is the security benefit?

It disables third-party channel access entirely
It ensures packages are only resolved from the highest-priority channel, avoiding accidental use of untrusted channels (Correct answer)
It forces HTTPS on all channel URLs
It prevents older package versions from being installed

Correct answer: It ensures packages are only resolved from the highest-priority channel, avoiding accidental use of untrusted channels

With `channel_priority: strict`, conda resolves packages exclusively from the first matching channel in the priority list, preventing lower-priority (potentially untrusted) channels from supplying packages.

Question 53: In pandas, which method writes a DataFrame to a SQL database table using a SQLAlchemy engine?

df.export_sql()
df.write_sql()
df.to_database()
df.to_sql() (Correct answer)

Correct answer: df.to_sql()

`df.to_sql('table_name', engine, if_exists='replace')` writes DataFrame contents to a SQL table, with options to append, replace, or fail if the table exists.

Question 54: When a conda package recipe uses `{{ version }}` in meta.yaml, where is the value of `version` typically sourced from?

From a .version file in the source directory
Automatically from PyPI
From a set statement at the top of meta.yaml using Jinja2 templating (Correct answer)
From an environment variable named VERSION

Correct answer: From a set statement at the top of meta.yaml using Jinja2 templating

Jinja2 `{% set version = '1.2.3' %}` at the top of meta.yaml assigns the variable, which is then referenced as `{{ version }}` throughout the recipe for DRY versioning.

Question 55: When automating a data pipeline on a schedule using cron or a task scheduler, which Python standard library module provides programmatic access to run shell commands and subprocesses?

os.system only
shutil
subprocess (Correct answer)
threading

Correct answer: subprocess

The `subprocess` module provides `subprocess.run()` and `Popen` for launching external processes, capturing output, and handling errors within automated Python pipeline scripts.

Question 56: What is the recommended frequency for reviewing and updating data visualization & analysis protocols?

Tracking activity volume without measuring quality
Reviewing results only at year-end
Relying on periodic external audits as the sole evaluation method
Monitoring outcomes through regular data collection and trend analysis (Correct answer)

Correct answer: Monitoring outcomes through regular data collection and trend analysis

Monitoring outcomes through regular data collection and trend analysis is the correct approach because effective data visualization & analysis in the anaconda certified professional field requires adherence to professional standards, evidence-based practices, and systematic methodology. This approach ensures consistent, high-quality outcomes while maintaining professional accountability.

Question 57: When uploading a package to Anaconda.org via the CLI, which command is used?

pip upload
conda push
anaconda upload (Correct answer)
conda upload

Correct answer: anaconda upload

The `anaconda upload` command (from the anaconda-client package) pushes a built .tar.bz2 or .conda file to your Anaconda.org channel.

Question 58: Which pandas method efficiently removes duplicate rows from a DataFrame, keeping only the first occurrence by default?

df.deduplicate()
df.unique_rows()
df.remove_duplicates()
df.drop_duplicates() (Correct answer)

Correct answer: df.drop_duplicates()

`df.drop_duplicates()` returns a DataFrame with duplicate rows removed, with `keep='first'` as default and options for `keep='last'` or `keep=False` to drop all duplicates.

Question 59: What is a `.conda` file format compared to the older `.tar.bz2` conda package format?

A format exclusive to Windows platforms
A format that only stores pure-Python packages
An encrypted package for enterprise distribution
A zip-based format with separate metadata and data archives for faster extraction (Correct answer)

Correct answer: A zip-based format with separate metadata and data archives for faster extraction

The `.conda` format is a zip archive containing separate `pkg-*.tar.zst` (data) and `info-*.tar.zst` (metadata) components, enabling faster installs by extracting only needed parts.

Question 60: What is the most common mistake professionals make when implementing package management & environment configuration strategies?

Transferring all risk to external partners through contracts
Developing contingency plans for high-probability risk scenarios (Correct answer)
Responding to problems only after they occur
Creating contingency plans for every possible scenario regardless of probability

Correct answer: Developing contingency plans for high-probability risk scenarios

Developing contingency plans for high-probability risk scenarios is the correct approach because effective package management & environment configuration in the anaconda certified professional field requires adherence to professional standards, evidence-based practices, and systematic methodology. This approach ensures consistent, high-quality outcomes while maintaining professional accountability.

Anaconda Certified Professional (ACP)

The Anaconda Certified Professional (ACP) exam validates expertise in the Anaconda data science platform, covering conda package management, data engineering and workflow automation, and machine learning and AI integration using Python.

Questions

90 min

Time Limit

70%

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