Latest NVIDIA NCP-ADS Actual Free Exam Questions & Community Discussion, Page 6

Question #56

You are working on a large-scale graph analysis problem that involves computing the shortest paths between nodes in a massive social network dataset. You decide to leverage NVIDIA RAPIDS cuGraph for accelerated computation.
Which of the following cuGraph functions should you use?

A. cugraph.k_truss()

B. cugraph.label_propagation()

C. cugraph.sssp()

D. cugraph.pagerank()

Discussion 0

Correct Answer: C Vote an answer

Question #57

Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)

A. Using Excel to manipulate large datasets

B. Using Dask for parallel processing of large datasets

C. Using Apache Spark for distributed data processing

D. Using SQLAlchemy to interact with databases for large data processing

E. Using SQLite as a local database for large-scale data analysis

Discussion 0

Correct Answer: B,C Vote an answer

Question #58

You are working on a dataset containing missing values, duplicate records, and inconsistent data types.
The dataset size is 15GB and you need to efficiently perform data cleansing operations such as:
- Handling missing values
- Dropping duplicates
- Converting data types
Which of the following approaches would be the most efficient way to perform these operations on an NVIDIA GPU?

A. Use pandas to load the dataset and perform the operations using standard pandas functions

B. Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame

C. Use Vaex to perform data cleansing, as it is optimized for large-scale datasets

D. Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods

Discussion 0

Correct Answer: D Vote an answer

Question #59

After profiling a deep learning model using NVIDIA DLProf, you notice that a specific GEMM (General Matrix Multiplication) operation takes significantly longer than expected. The profiler output reveals that tensor cores are underutilized despite having an Ampere-based GPU with Tensor Cores enabled.
Which of the following actions is the MOST appropriate to improve performance?

A. Switch from stochastic gradient descent (SGD) to Adam optimizer, as Adam improves convergence and computational efficiency.

B. Disable CUDA graphs and enforce PyTorch's eager execution mode to improve kernel execution order.

C. Convert the model's data type to float16 or bfloat16 and re-run the training with automatic mixed precision (AMP).

D. Increase the batch size to maximize GPU memory usage and reduce kernel launch overhead.

Discussion 0

Correct Answer: C Vote an answer

Question #60

A data scientist is working with a large dataset containing missing values and outliers. The dataset will be used for training a machine learning model. The scientist decides to preprocess the data using RAPIDS cuDF, an accelerated dataframe library.
Which of the following is the most efficient approach to handle missing values while maintaining data integrity?

A. Use df.dropna() to remove all rows with missing values.

B. Replace missing values with zero using df.fillna(0).

C. Use df.fillna(df.mean()) to replace missing values with the column mean.

D. Convert missing values to a separate categorical class using df.fillna("missing").

Discussion 0

Correct Answer: C Vote an answer

Question #61

You are working with a dataset in a cloud-based GPU environment that contains a column country representing the country of origin for customers. The column contains only 10 unique country values, but the dataset has millions of rows.
Which of the following is the most memory-efficient approach to handle the country column in a cuDF DataFrame?

A. df['country'] = df['country'].astype('string')

B. df['country'] = df['country'].astype('int32')

C. df['country'] = df['country'].astype('object')

D. df['country'] = df['country'].astype('category')

Discussion 0

Correct Answer: D Vote an answer

Question #62

You are processing a large dataset using NVIDIA Dask-cuDF to distribute GPU-accelerated computation across multiple nodes. Users report inconsistent execution times, with some jobs taking significantly longer than expected.
Which of the following actions would best help diagnose the performance bottleneck?

A. Switch to using Pandas with Dask to compare execution speed differences.

B. Limit GPU memory usage to force more frequent spilling to disk and observe performance differences.

C. Use Dask's dashboard and NVTX markers to analyze task execution times and GPU utilization.

D. Reduce the number of Dask workers to minimize parallel execution overhead.

Discussion 0

Correct Answer: C Vote an answer

Question #63

A company is deploying an MLOps pipeline for training and serving deep learning models. The data scientists want to leverage GPU acceleration at multiple stages of the pipeline to enhance efficiency.
Which of the following steps would benefit the most from GPU acceleration?

A. Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.

B. Storing and retrieving models from a centralized object storage system.

C. Training and inference workloads using deep learning models with TensorFlow or PyTorch.

D. Model monitoring by logging metadata and performance metrics in a database.

Discussion 0

Correct Answer: C Vote an answer

Question #64

You need to set up an isolated, GPU-accelerated environment for a deep learning project that requires specific CUDA, cuDNN, and RAPIDS versions.
Which of the following best ensures a reproducible environment using Docker?

A. Install NVIDIA drivers manually inside a Docker container every time it runs.

B. Use a system-wide CUDA installation and mount the /usr/local/cuda directory into the container to provide GPU support.

C. Build a container from an Ubuntu base image and manually install all dependencies without specifying versions.

D. Use the nvidia/cuda base image and specify the required RAPIDS and deep learning libraries in a Dockerfile.

Discussion 0

Correct Answer: D Vote an answer

Question #65

In the context of the CRISP-DM process, you are tasked with building a machine learning model on a large dataset. You decide to leverage GPU resources to accelerate training.
Which of the following steps in the CRISP-DM process are most likely to benefit from the use of GPU resources in the cloud environment? (Select two)

A. Model Deployment and Monitoring

B. Data Exploration

C. Business Understanding

D. Data Understanding

E. Model Training and Evaluation

Discussion 0

Correct Answer: B,E Vote an answer

Question #66

You are working on a large dataset for a machine learning model and need to preprocess the data efficiently using NVIDIA RAPIDS cuDF on a GPU-accelerated system.
Which of the following statements is correct regarding data preparation using cuDF?

A. cuDF operations are executed lazily and require an explicit compute() call to execute transformations.

B. cuDF can be used as a direct replacement for Pandas, but it is optimized for multi-node distributed computing rather than GPU acceleration.

C. cuDF requires all input data to be stored in Apache Parquet format, as it does not support CSV or JSON ingestion.

D. cuDF supports common Pandas-like operations, such as filtering, merging, and groupby, but performs them efficiently on the GPU.

Discussion 0

Correct Answer: D Vote an answer

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