Unit 6: Artificial Intelligence Ecosystem and Future Trends - Practice Quiz

CSE276 — Artificial Intelligence Foundations 60 Questions
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1 What does an Artificial Intelligence development ecosystem primarily include?

Artificial Intelligence development ecosystem Easy
A. Documents, printers, and scanners
B. Tools, libraries, platforms, and communities
C. Only computer hardware and cables
D. Only websites and search engines

2 What is the main role of an AI framework in the development ecosystem?

Artificial Intelligence development ecosystem Easy
A. Creating internet connection cables
B. Managing office attendance records
C. Providing tools for building AI models
D. Designing physical computer cases

3 Why is Python widely used in Artificial Intelligence development?

Python ecosystem Easy
A. It replaces all computer hardware
B. It has many AI libraries
C. It requires no programming knowledge
D. It works only on mobile phones

4 Which Python library is commonly used for numerical array operations?

Python ecosystem Easy
A. Flask
B. NumPy
C. Pygame
D. Django

5 What are TensorFlow and PyTorch mainly used for?

TensorFlow and PyTorch Easy
A. Managing relational databases
B. Designing network equipment
C. Editing digital photographs
D. Building machine learning models

6 Which organization originally developed TensorFlow?

TensorFlow and PyTorch Easy
A. Microsoft
B. Google
C. Oracle
D. Adobe

7 Which organization originally developed PyTorch?

TensorFlow and PyTorch Easy
A. Intel
B. Meta
C. Apple
D. IBM

8 What is Hugging Face best known for in the AI ecosystem?

Hugging Face Easy
A. Building physical network routers
B. Hosting models and datasets
C. Selling office productivity software
D. Manufacturing graphics processors

9 What does the Hugging Face Transformers library provide?

Hugging Face Easy
A. Spreadsheet calculation templates
B. Pre-trained machine learning models
C. Physical electrical transformers
D. Computer operating system drivers

10 What is Ollama commonly used for?

Ollama Easy
A. Editing relational database tables
B. Managing spreadsheet formulas
C. Running language models locally
D. Creating printed circuit boards

11 What is one benefit of running an AI model locally with Ollama?

Ollama Easy
A. Training always becomes completely free
B. Data can remain on the device
C. Hardware requirements are fully removed
D. Every model gains perfect accuracy

12 What is LangChain mainly designed to help developers build?

LangChain Easy
A. Layouts for printed magazines
B. Applications powered by language models
C. Drivers for wireless networks
D. Processors for desktop computers

13 In LangChain, what is a chain?

LangChain Easy
A. A type of computer network cable
B. A sequence of connected operations
C. A physical device for data storage
D. A collection of database passwords

14 What does a vector database primarily store and search?

Vector databases: conceptual overview Easy
A. Numerical representations called vectors
B. Passwords stored as plain text
C. Programs converted into paper documents
D. Printed reports arranged by date

15 What is similarity search in a vector database used to find?

Vector databases: conceptual overview Easy
A. Users with matching account passwords
B. Files with identical creation dates
C. Items with related vector representations
D. Devices with equal screen sizes

16 Which activity usually occurs before training an AI model?

Artificial Intelligence development workflow Easy
A. Collecting and preparing data
B. Publishing the final user manual
C. Deleting the evaluation results
D. Replacing all network equipment

17 Why is an AI model evaluated after training?

Artificial Intelligence development workflow Easy
A. To rename its source files
B. To measure its performance
C. To replace its programming language
D. To increase the monitor size

18 Which development is a major future trend in Artificial Intelligence?

Future trends in Artificial Intelligence Easy
A. More multimodal AI systems
B. Removal of all automation
C. Less use of digital data
D. Replacement of every database

19 What does Artificial General Intelligence refer to?

Artificial General Intelligence Easy
A. Hardware used only for printing
B. AI created for one narrow task
C. Software used only for storage
D. AI with broad human-like abilities

20 What is a main goal of Sustainable AI?

Sustainable AI Easy
A. Increasing energy use during training
B. Replacing efficient hardware with older devices
C. Using larger models for every task
D. Reducing environmental and resource costs

21 A team wants to replace its image-classification model without changing the web application that uses it. Which ecosystem design best supports this requirement?

Artificial Intelligence development ecosystem Medium
A. Require the application to retrain before each request
B. Store predictions in the source-code repository
C. Expose the model through a stable inference API
D. Embed model weights directly in the web interface

22 An AI service performs well during testing but slows down when many users access it simultaneously. Which ecosystem component should the team examine first?

Artificial Intelligence development ecosystem Medium
A. Data-labeling guidelines
B. Source-code formatting rules
C. Notebook visualization settings
D. Model-serving infrastructure

23 Two Python AI projects require incompatible versions of the same library. What is the most appropriate way to prevent dependency conflicts?

Python ecosystem Medium
A. Use one global installation for both projects
B. Rename the imported library in one project
C. Install both versions in the operating-system folder
D. Create a separate virtual environment for each project

24 A data scientist wants teammates to reproduce the Python environment used to train a model. Which artifact is most directly useful?

Python ecosystem Medium
A. A document listing variable names
B. A folder containing only prediction results
C. A dependency file with pinned package versions
D. A screenshot of the notebook output

25 A researcher frequently changes a neural network during experiments and wants Python-like debugging with immediate execution. Which choice is most suitable?

TensorFlow and PyTorch Medium
A. PyTorch with eager execution
B. A vector database query engine
C. TensorFlow Lite for mobile inference
D. Hugging Face Datasets alone

26 A trained TensorFlow model must run locally on a mobile device with limited memory. Which deployment option is designed for this use case?

TensorFlow and PyTorch Medium
A. TensorFlow Lite
B. Hugging Face Spaces
C. TensorBoard
D. PyTorch DataLoader

27 A developer needs to build a sentiment classifier quickly using an existing transformer model. Which Hugging Face feature offers the most direct starting point?

Hugging Face Medium
A. An Ollama model manifest
B. A task-specific pipeline
C. A vector index partition
D. A TensorBoard callback

28 Before using a model downloaded from the Hugging Face Hub in a commercial product, what should a development team verify first?

Hugging Face Medium
A. The number of files in the repository
B. The model license and usage restrictions
C. The alphabetical order of model tags
D. The color theme of the model page

29 A hospital wants employees to summarize sensitive notes without sending them to an external AI provider. When suitable hardware and governance are available, why might Ollama be useful?

Ollama Medium
A. It automatically anonymizes every medical record
B. It converts unstructured notes into relational tables
C. It can run supported language models locally
D. It guarantees compliance with all health regulations

30 A large model runs too slowly on a developer's laptop through Ollama. Which change is most likely to improve local inference feasibility while preserving the same general task?

Ollama Medium
A. Duplicate the model files on disk
B. Disable all input preprocessing
C. Increase the prompt length substantially
D. Choose a smaller quantized model

31 An assistant must answer a question by retrieving company policies, inserting relevant passages into a prompt, and calling a language model. What role can LangChain play?

LangChain Medium
A. Providing specialized GPU hardware for inference
B. Training the language model from random weights
C. Replacing the policy documents with embeddings
D. Orchestrating retrieval and model-call components

32 A LangChain agent can call tools that send emails and modify customer records. Which design most effectively reduces the risk of harmful actions?

LangChain Medium
A. Require approval for high-impact tool calls
B. Increase the model's temperature for flexibility
C. Allow every tool to accept unrestricted arguments
D. Store longer conversation histories by default

33 A search system stores document embeddings and receives a query embedding. What does the vector database primarily use to find relevant documents?

Vector databases: conceptual overview Medium
A. Frequency of database backup operations
B. Creation time of each document record
C. Similarity between query and document vectors
D. Equality between query and document filenames

34 An enterprise search application must return semantically similar documents, but only from the user's department. Which query strategy is most appropriate?

Vector databases: conceptual overview Medium
A. Exact embedding matching without department filters
B. Vector similarity search with metadata filtering
C. Random sampling followed by manual selection
D. Keyword sorting without access-control metadata

35 A fraud-detection dataset contains only 1% fraudulent transactions. Why can accuracy alone be a misleading evaluation metric?

Artificial Intelligence development workflow Medium
A. A model can score 99% by predicting every case as legitimate
B. Accuracy always assigns greater weight to fraudulent cases
C. Accuracy cannot be calculated for binary classification tasks
D. A balanced dataset is required before accuracy can be computed

36 A recommendation model performs well initially, but its quality declines after customer preferences change. Which workflow practice best addresses this problem?

Artificial Intelligence development workflow Medium
A. Evaluate only the application's response time
B. Monitor production drift and schedule reevaluation
C. Remove validation data from future experiments
D. Keep the original model unchanged indefinitely

37 An AI assistant must interpret a chart, read its caption, and answer a spoken question about both. Which future AI direction is most relevant?

Future trends in Artificial Intelligence Medium
A. Multimodal model integration
B. Text-only database normalization
C. Single-format keyword indexing
D. Manual feature encoding only

38 A manufacturer wants an AI system to inspect products with low latency even when internet connectivity is unreliable. Which trend best supports this goal?

Future trends in Artificial Intelligence Medium
A. Edge AI deployment
B. Manual database replication
C. Longer prompt construction
D. Centralized cloud-only inference

39 Which capability would provide stronger evidence of Artificial General Intelligence than of a narrow AI system?

Artificial General Intelligence Medium
A. Transferring knowledge across many unfamiliar task domains
B. Optimizing routes within one predefined road network
C. Classifying one fixed collection of medical images
D. Answering questions from one curated product manual

40 A company wants to reduce the environmental impact of an AI service without removing the service. Which action is most directly aligned with Sustainable AI?

Sustainable AI Medium
A. Retain duplicate datasets in multiple unused regions
B. Retrain the full model after every user interaction
C. Use a smaller efficient model and carbon-aware scheduling
D. Increase model size for every type of request

41 A team can reproduce a model's Python packages from a lockfile, but inference outputs differ across machines using the same input and checkpoint. One machine uses a different GPU architecture and CUDA/cuDNN stack. Which action most directly addresses this reproducibility gap?

Artificial Intelligence development ecosystem Hard
A. Store only the model weights and regenerate the Python environment for each deployment
B. Increase the inference batch size until numerical differences become statistically insignificant
C. Pin the complete runtime image, GPU libraries, framework build, and deterministic execution settings
D. Replace the package lockfile with a list containing only top-level Python dependencies

42 A CPU-bound Python preprocessing function executes pure Python loops and cannot keep a GPU training job supplied with data. Profiling confirms that storage is not the bottleneck. Which change is most likely to improve throughput?

Python ecosystem Hard
A. Run preprocessing in additional threads that share the same CPython interpreter
B. Run preprocessing in multiple processes and transfer prepared batches through bounded queues
C. Call the preprocessing function from an asynchronous event loop with more concurrent tasks
D. Disable garbage collection permanently so that Python loops execute outside the interpreter lock

43 A PyTorch model contains dropout and batch-normalization layers. An engineer wraps inference in torch.no_grad(), yet repeated predictions remain inconsistent and batch-normalization statistics continue to change. What is missing?

TensorFlow and PyTorch Hard
A. Calling model.eval() before inference
B. Setting every parameter's gradient to zero manually
C. Calling model.zero_grad() before inference
D. Recreating the optimizer without momentum and loading its state after each prediction

44 A custom training loop must compute per-example gradients for a loss and then differentiate a regularizer built from those gradients. Which capability is fundamentally required?

TensorFlow and PyTorch Hard
A. A distributed parameter server that computes ordinary first-order gradients on remote workers
B. A serialized inference graph containing fixed tensor signatures
C. Mixed-precision inference with automatic casting and static loss scaling
D. Higher-order automatic differentiation with a retained or nested gradient graph

45 A production service loads a model and tokenizer from the same Hugging Face repository name. After the repository owner updates the default branch, token IDs change while the service still uses cached model weights. What is the strongest preventive measure?

Hugging Face Hard
A. Pin both model and tokenizer artifacts to the same immutable commit revision
B. Download the tokenizer from the default branch and reconstruct missing tokens from model embeddings
C. Pin only the model configuration because it defines the vocabulary size
D. Clear the artifact cache whenever the service process is restarted

46 A decoder-only Hugging Face model is batch-generating continuations from prompts of unequal length. With right padding, the system selects logits from the final padded position for shorter prompts and produces degraded output. Which correction best preserves batched generation semantics?

Hugging Face Hard
A. Use random padding token IDs and average logits across all padded positions
B. Keep right padding but remove the attention mask so padding participates equally
C. Truncate every prompt to its first token, generate independently, and append the removed context afterward
D. Use left padding with a valid pad token and supply the corresponding attention mask

47 An Ollama deployment uses the same quantized model weights in two custom model definitions, but one definition consistently follows chat instructions better. Hardware and generation parameters are identical. Which difference most plausibly explains the result?

Ollama Hard
A. The Ollama client sends requests through different local TCP source ports
B. The model definitions store their blobs under different content-addressed filenames
C. One definition converts every quantized tensor back to full precision before each token is generated
D. The model definitions apply different prompt templates or system-message formatting

48 A LangChain retrieval pipeline must stream model tokens, preserve request-specific metadata, and avoid blocking the event loop while calling an asynchronous vector store. Which design is most appropriate?

LangChain Hard
A. Start a new event loop inside each retriever call and synchronously wait for all generated tokens before returning
B. Store request metadata in module-level variables that callbacks read during streaming
C. Wrap the entire synchronous chain in one global lock and invoke it from every request
D. Compose async-capable runnables and propagate configuration through the runnable invocation

49 An agent can call a tool that executes database queries. Retrieved documents may contain malicious text telling the agent to ignore its policy and invoke that tool. Which control most directly limits the resulting security risk?

LangChain Hard
A. Place retrieved text after the user's question so it has lower positional importance
B. Enforce tool permissions and validate structured arguments outside the language model
C. Increase the model temperature so malicious instructions are less consistently followed
D. Ask the model to inspect its own hidden reasoning and certify that every generated query is harmless

50 A vector database uses cosine similarity. All stored vectors and the query vector are normalized to unit length. Which ranking statement is correct?

Vector databases: conceptual overview Hard
A. Cosine ranking becomes independent of the angle between vectors after normalization
B. Ranking by cosine similarity is equivalent to ranking by smallest squared Euclidean distance
C. Ranking by cosine similarity is equivalent to ranking by largest Manhattan distance
D. Euclidean ranking is equivalent only when every vector has zero mean and every coordinate has unit variance

51 A company replaces its embedding model with one having the same output dimension, then continues querying an index containing vectors from the old model. Retrieval quality collapses. Why does matching dimension not prevent this?

Vector databases: conceptual overview Hard
A. Approximate nearest-neighbor indexes support only one query vector during their lifetime
B. The new model necessarily uses Euclidean distance while every old embedding model necessarily uses cosine distance
C. Equal-dimensional embeddings must be byte-identical unless the text corpus has changed
D. The two models can encode meaning in incompatible vector spaces despite equal dimensionality

52 A fraud model is evaluated using random train-test splitting over transactions. The same customers appear in both sets, and each customer's behavior is highly stable. Offline performance is excellent but performance on newly acquired customers is poor. Which evaluation redesign best estimates the deployment scenario?

Artificial Intelligence development workflow Hard
A. Split by customer identity and preserve a later time window for final testing
B. Randomly reshuffle individual transactions with a different seed before each evaluation
C. Oversample transactions from customers that already occur in both partitions
D. Train on the complete dataset and estimate generalization from the model's average training loss

53 A model's online accuracy declines, but the marginal distribution of each monitored input feature appears unchanged. Which investigation can still reveal a meaningful form of drift?

Artificial Intelligence development workflow Hard
A. Increase the alert threshold and wait until at least one feature's arithmetic mean changes substantially
B. Compare only the compressed sizes of the old and new feature files
C. Test changes in joint feature relationships and in the conditional relationship between inputs and labels
D. Conclude that drift is impossible because all univariate feature distributions are stable

54 A benchmark reports that a new autonomous agent completes 80% of tasks, but the benchmark allows unlimited retries and excludes API charges, latency, and human recovery effort. Which conclusion is best supported?

Future trends in Artificial Intelligence Hard
A. The result proves that scaling the model will eliminate tool failures without changes to evaluation or orchestration
B. The agent is production-ready because task completion exceeds a simple majority
C. Unlimited retries make the benchmark stricter than real deployment in every relevant respect
D. The reported completion rate is insufficient to establish practical agent reliability or efficiency

55 An edge device must run a multimodal assistant with intermittent connectivity, strict privacy requirements, and limited memory. Which architecture best reflects likely near-term AI deployment trends?

Future trends in Artificial Intelligence Hard
A. Disable all model compression because quantization always increases memory use and guarantees lower throughput
B. Send every raw sensor stream to one remote model and retain no local inference capability
C. Run several full-precision frontier models concurrently on the device for majority voting
D. Use a compact local model for routine tasks and selectively escalate permitted cases to a larger remote model

56 A system exceeds human performance on thousands of benchmark tasks but requires task-specific fine-tuning and cannot transfer knowledge to unfamiliar domains without new labeled data. Which claim is most defensible?

Artificial General Intelligence Hard
A. The system is necessarily AGI because the number of benchmarks exceeds one thousand
B. Broad benchmark superiority alone does not establish domain-general adaptive intelligence
C. The system cannot be intelligent because any use of supervised data invalidates intelligence
D. The system is AGI only if its parameter count exceeds the combined number of neurons in all benchmark authors' brains

57 Which evaluation design most strongly tests whether an alleged AGI can generalize rather than reproduce memorized benchmark patterns?

Artificial General Intelligence Hard
A. Measure only next-token loss on a random sample of the system's training corpus
B. Repeat public benchmark questions that have circulated online for several years
C. Use newly constructed hidden tasks requiring cross-domain transfer and auditable interaction traces
D. Let the system choose previously published questions, discard failed attempts, and report the best answer per category

58 Two data centers run the same training job with identical energy consumption. Center A reports lower operational carbon emissions than Center B. Which factor can validly explain the difference?

Sustainable AI Hard
A. Center A excludes model checkpoints from the parameter count even though parameter-count conventions directly determine grid emissions
B. Center A records energy in kilowatt-hours while Center B records the same energy in joules
C. Center A uses larger batches, which mathematically forces its energy consumption to have zero emissions
D. Center A's electricity supply has lower carbon intensity during the job

59 A team compares two language models. Model X uses less inference energy per request, but its lower accuracy causes users to retry more often. What is the most appropriate sustainability metric?

Sustainable AI Hard
A. Training emissions alone, allocated equally to every possible request whether or not the model is ever deployed
B. Energy consumed by one forward pass regardless of task outcome
C. Parameter count divided by the maximum advertised context length
D. Expected energy or emissions per successfully completed task

60 An AI service pulls an unpinned model artifact containing custom loading code from a public registry during every deployment. Which combination most directly reduces software supply-chain risk?

Artificial Intelligence development ecosystem Hard
A. Use the repository's latest branch so security changes are always adopted without review
B. Grant the loading process administrative access so it can automatically repair any dependency or permission problem
C. Rename the downloaded artifact and store it in a directory excluded from vulnerability scans
D. Pin an immutable revision, verify its digest, review custom code, and restrict its runtime privileges