A binary classification model is evaluated on a test set of 10 images, classifying each as either
Horse ('H') or Zebra ('Z'). The true labels and the predicted labels are provided below:
What is the precision of the model for the "Horse" class?
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An agent's ReAct loop performs 1 initial reasoning step, then 3 more reasoning steps (one after
each of 3 tool calls) — 4 reasoning steps total for one query. Each reasoning step consumes 250
input tokens + 150 output tokens. What is the **total number of tokens** consumed by reasoning
alone for one query?
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An ML practitioner is evaluating a classification model by plotting its training and validation loss
over 100 epochs. Which of the following statements regarding model evaluation and overfitting is
true?
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Overfitting is occurring if the validation loss strictly decreases alongside the
training loss.
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An overfitted model typically exhibits high bias and low variance.
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Early stopping can mitigate overfitting by halting the training process before
the model begins to memorize noise.
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Duplicating the training data points is an effective way to reduce model
variance and improve generalization.
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token prepended to the input sequence in
the original Vision Transformer architecture?
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Its final hidden state at the output of the transformer encoder serves as the
global aggregate representation of the image for the classification task.
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It provides spatial coordinate data to allow the model to capture the relative
distances and positions of the different image patches.
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It acts as a boundary separator to explicitly distinguish where one flattened 2D
image patch ends and the next begins.
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It serves as a padding token to maintain a uniform sequence length when the
model processes images of varying resolutions in a single batch.
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A legal-tech startup is building a tool that must always draft contracts using the firm's exact house
style - specific clause phrasing, tone, and formatting conventions that must appear identically in
every document, regardless of the underlying facts. The firm's knowledge (case law, templates)
rarely changes. Which approach is the better primary fit, and why?
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RAG, because contracts count as "documents" and RAG works with any
document type.
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Fine-tuning, because the requirement is about consistently reproducing a
specific style/behavior in every output, not about injecting frequently-changing factual knowledge.
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RAG, because retrieving the firm's past contracts as context at generation time
will reliably force the LLM to match their exact phrasing and formatting every time.
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Since the knowledge is mainly static, the team should skip both RAG and fine-
tuning and just use a long, detailed system prompt with the house style rules.
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The training dataset contains 960 samples, and there is one GPU.
How many optimizer update steps are performed during training (ignore gradient accumulation
and assume
)?
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A student justifies their chunking choice like this:
"I will set
and
so I get fewer, bigger chunks - that way
retrieval is faster since there's less to search through, and I never lose any context since overlap
does not matter if chunks are big enough."
What is wrong with this reasoning? (Select all that apply)
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Nothing is wrong - this is a sound strategy
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Bigger chunks reduce the number of vectors to compare, but at that size a
chunk likely mixes multiple unrelated topics, hurting the precision of what gets retrieved.
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risks splitting a relevant idea exactly at a chunk boundary
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is invalid and will throw an error
A published solution is not available for this question yet.