[2026] NVIDIA NCP-ADS Practice Verified Answers – Pass Your Exams For Sure! [Q146-Q168]

Posted On November 8, 2026

NCP-ADS Practice NVIDIA Verified Answers – Pass Your Exams For Sure! [2026]

Valid Way To Pass NVIDIA-Certified Professional’s NCP-ADS Exam

NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
MLOps	19%	– Monitoring, logging and maintenance – Model deployment and serving – End-to-end workflow management – Pipeline automation and orchestration
Data Preparation	17%	– Workflow monitoring and bottleneck identification – Data validation and quality assurance – Data cleaning, preprocessing and transformation – Feature engineering and data type optimization
Machine Learning	15%	– Model training and hyperparameter tuning – GPU-accelerated ML frameworks and algorithms – Model evaluation and validation – Distributed training strategies
GPU and Cloud Computing	16%	– CRISP-DM and data science methodology – Resource management and scaling strategies – Cloud GPU environments and deployment – GPU architecture and acceleration principles
Data Analysis	14%	– Time-series analysis and anomaly detection – Exploratory Data Analysis (EDA) – Distributed and parallel data processing – Data visualization and graph analytics
Data Manipulation and Software Literacy	19%	– Performance profiling and optimization tools – GPU-accelerated ETL workflows – Data processing libraries selection and usage – Dependency management and containerization

NO.146 A machine learning engineer wants to evaluate the performance of NVIDIA RAPIDS cuDF and Apache Spark for large-scale data processing on a GPU-enabled cluster.
Which of the following strategies is the most effective for obtaining a fair and comprehensive benchmark?

Run Spark on a CPU cluster while running RAPIDS on a GPU to compare real-world scenarios.

Limit the benchmark to small datasets since GPUs excel at parallel processing.

Focus only on processing speed without considering resource consumption differences between frameworks.

Execute identical ETL workflows on cuDF and Spark-RAPIDS and measure execution time and resource utilization.

NO.147 You are implementing a Dask-based solution for distributed data parallelism across a multi-GPU system.
Which configuration steps would ensure effective use of GPUs for parallel computation? (Select two)

Use Dask’s Cluster class with the distributed scheduler and specify CPU cores only for GPU workloads

Use dask_cuda’s LocalCUDACluster and let Dask automatically allocate GPUs without any configuration

Use dask_cuda’s LocalCUDACluster with proper GPU memory management to handle multiple GPUs

Use dask_cudf to convert DataFrame computations into GPU-accelerated operations using cuDF

Create a LocalCUDACluster and manually specify the GPUs you want to use for each Dask worker

NO.148 You are working with a large dataset in a cloud environment for a deep learning model. The dataset consists of several features including numerical values, categorical data, and timestamps.
Which of the following choices would result in the most efficient use of GPU and cloud resources when determining the optimal data type for each feature? (Select three)

Use float64 for all numerical features to ensure maximum precision.

Use object data types for categorical features to avoid type conversion overhead.

Use int8 for categorical features where there are fewer than 256 categories.

Use int32 for all numerical features to save memory.

Use datetime64[ns] for timestamp features to ensure high precision.

NO.149 You are working on a machine learning pipeline using NVIDIA RAPIDS cuML and need to standardize the dataset to ensure that all features have a mean of 0 and a standard deviation of 1.
Which of the following methods should you use to achieve this in cuML?

cuml.preprocessing.LabelEncoder()

cuml.preprocessing.StandardScaler()

cuml.preprocessing.OneHotEncoder()

cuml.preprocessing.MinMaxScaler()

NO.150 A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?

Using only virtual environments (venv) without managing GPU dependencies separately

Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies

Using a single system-wide installation of CUDA and forcing all projects to use the same version

Manually installing each package and its dependencies using pip

NO.151 You are working with a dataset containing billions of rows and need to perform data transformations, aggregations, and joins efficiently on a single-node GPU-enabled workstation.
Which NVIDIA technology is best suited to optimize performance for these operations?

NVIDIA Triton Inference Server to accelerate data processing workflows on a single GPU.

NVIDIA RAPIDS cuDF to leverage GPU acceleration for large-scale DataFrame operations.

NVIDIA Nsight Compute to profile and optimize the performance of GPU-based aggregations.

NVIDIA TensorRT to optimize DataFrame transformations and aggregations using deep learning.

NO.152 You are tasked with acquiring a dataset for training a machine learning model in healthcare, predicting patient readmission rates. Before using the dataset, you must assess its quality.
Which of the following is the most important factor to evaluate before acquisition?

The programming language used to preprocess the dataset

Whether the dataset is stored on a cloud server or a local machine

The number of missing values and inconsistencies in key columns

The file format (CSV, JSON, or XML) of the dataset

NO.153 Which feature of NVIDIA MLFlow integration with Triton Inference Server allows for the seamless deployment and monitoring of models in production?

MLFlow prevents models from being updated once deployed to production.

MLFlow handles only the training and not the deployment of models.

MLFlow only supports deployment on CPUs.

MLFlow allows for version control and automated model rollout.

NO.154 Which of the following is the primary benefit of using NVIDIA RAPIDS in the context of data science workflows?

It speeds up the preprocessing and analysis of large datasets by utilizing GPUs

It simplifies the creation of visualizations for large datasets

It enables real-time data streaming and analytics

It allows for the implementation of reinforcement learning algorithms

NO.155 You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?

NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.

NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.

NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.

NVIDIA Nsight Systems for managing dataset storage and retrieval performance.

NO.156 You are building a predictive model for retail sales forecasting and need a dataset that includes historical sales transactions, customer demographics, and external economic indicators (e.g., inflation rate, unemployment rate).
Which of the following datasets would be the most appropriate for your model?

A dataset with global temperature trends over the past decade

A dataset of product reviews and customer sentiments from an e-commerce website

A public dataset of annual GDP per country

A dataset containing transaction history and customer profiles from a retail company

NO.157 You are training a machine learning model using NVIDIA RAPIDS cuML and notice that the training process is significantly slower than expected. You suspect that there are bottlenecks in data movement and computation.
Which of the following techniques can best help you diagnose and resolve these bottlenecks?

Move all data from GPU memory to CPU memory before training the model.

Use cudf.DataFrame.to_pandas() to convert the dataset to a pandas DataFrame for analysis.

Reduce the number of features used in training without profiling the actual bottlenecks.

Use cuml.common.device_auto_mem_size() to check GPU memory usage and adjust batch sizes accordingly.

NO.158 Which of the following techniques are commonly used to identify and acquire datasets for machine learning and data science projects? (Select three)

Web scraping

Crowdsourcing

Data augmentation

Manual data entry

Public APIs

NO.159 You are working with a large dataset on an NVIDIA GPU, where optimizing memory usage is a priority. Your dataset contains a column, transaction_id, which stores unique integer values ranging between 0 and 100,000.
Which of the following data types is the most memory-efficient choice for this column in cuDF?

df[‘transaction_id’] = df[‘transaction_id’].astype(‘float32’)

df[‘transaction_id’] = df[‘transaction_id’].astype(‘int32’)

df[‘transaction_id’] = df[‘transaction_id’].astype(‘int8’)

df[‘transaction_id’] = df[‘transaction_id’].astype(‘int64’)

NO.160 A data scientist wants to process a large dataset using multiple GPUs on an NVIDIA-supported system. They decide to use Dask to enable parallelism.
Which of the following steps is most essential for leveraging Dask for multi-GPU scaling?

Convert all data into Pandas DataFrames before distributing computations

Train a deep learning model on a single GPU first, then switch to Dask for scaling

Use ThreadPoolExecutor to manage parallel computations on GPUs

Use dask.array or dask.dataframe with dask_cuda.CUDACluster to distribute computations across multiple GPUs

NO.161 You are working with a social network dataset containing millions of user interactions and need to identify influential users based on their connectivity and interactions.
Which approach using NVIDIA’s cuGraph library is the most appropriate for this task?

Use cuGraph’s PageRank algorithm to rank users based on their importance in the network.

Run a breadth-first search (BFS) on the entire graph to find the most influential users.

Apply cuGraph’s K-Means clustering to group users with similar connectivity patterns.

Use cuGraph’s DBSCAN clustering to detect communities in the social network.

NO.162 Which of the following is the most efficient way to implement data parallelism using Dask for multi- GPU scaling on an Nvidia platform?

Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.

Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.

Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.

Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.

NO.163 You are working with a large dataset in NVIDIA RAPIDS cuDF and notice that the data processing pipeline is taking longer than expected.
Which of the following tools or techniques can help you identify and analyze bottlenecks in the pipeline?

Use cuml.preprocessing.StandardScaler() to normalize the dataset before processing.

Enable pandas compatibility mode in cudf to improve performance.

Use nvtx.annotate() to label sections of code and analyze performance in Nsight Systems.

Use cudf.DataFrame.astype(str) to convert numerical values to strings for easier debugging.

NO.164 You are analyzing a time-series dataset that represents temperature readings from an industrial sensor. Your goal is to detect anomalies that may indicate sensor failures or environmental changes.
Which of the following methods would be the most appropriate statistical technique for detecting anomalies in this time-series dataset?

One-Hot Encoding

Principal Component Analysis (PCA)

Z-score (Standard Score) Analysis

Moving Average Smoothing

NO.165 You are working with a cuDF DataFrame and need to convert a column named sales from float64 to int32 to save memory.
Which of the following is the correct and most efficient way to perform this conversion in cuDF?

df[‘sales’].convert_dtypes(‘int32’)

df[‘sales’] = df[‘sales’].astype(‘int32’)

df[‘sales’].apply(lambda x: int(x))

df[‘sales’] = df[‘sales’].to_numeric(‘int32’)

NO.166 You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?

Use cuDF’s .fillna() method to replace missing values in GPU memory

Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF

Drop all missing values using .dropna() instead of filling them, as GPU memory is limited

Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution

NO.167 You are building a real-time recommendation system that processes high-frequency transactional data from millions of users.
The system must:
– Ingest and preprocess data efficiently
– Perform similarity computations for user-item recommendations
– Scale to handle rapid incoming transactions
Which of the following NVIDIA technologies is the best choice for this use case?

NVIDIA Triton Inference Server

RAPIDS cuGraph

NVIDIA NVTabular

CUDA Kernels with Custom C++ Code

NO.168 Which of the following is the best approach for performing benchmarking and optimizing GPU- accelerated workflows for MLOps using Nvidia technologies?

Use TensorRT to optimize deep learning models by converting them into highly optimized inference engines, allowing faster execution with lower latency.

Use Nvidia’s nvprof tool to profile GPU resource usage and identify bottlenecks, then adjust the batch size to optimize throughput.

Rely exclusively on the nvidia-smi tool for monitoring GPU utilization and memory usage across multiple GPUs without making any other performance adjustments.

Use Nvidia’s nsight tools to benchmark only the model training phase and ignore the inference phase, as training is the primary bottleneck.

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