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CCNA Fundamentals of AI and ML Questions

22 of 97 questions · Page 2/2 · Fundamentals of AI and ML · Answers revealed

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76
MCQhard

A financial services company uses a machine learning model to approve loan applications. The model is a gradient boosting classifier trained on historical loan data. Recently, the company noticed that the model's approval rate for applicants from a certain demographic group is significantly lower than for other groups, even though the model's overall accuracy remains high. The data science team has been asked to address this potential bias while minimizing the impact on overall model performance. The team has access to the training data and the trained model. They have limited time and budget. Which course of action should the team take first?

A.Remove the sensitive attribute from the training data and retrain the model.
B.Collect more data from the under-represented demographic group and retrain the model.
C.Analyze the training data for bias and retrain the model using bias mitigation techniques such as reweighting.
D.Adjust the model's decision threshold for the affected group after deployment.
AnswerC

This directly addresses the root cause and is resource-efficient.

Why this answer

The most efficient first step is to analyze the training data for bias and then retrain the model with bias mitigation techniques like reweighting. Option A is wrong because collecting more data is resource-intensive and may not address bias. Option C is wrong because feature engineering may not help if the bias is in the labels.

Option D is wrong because post-hoc adjustments can introduce other issues and may not be as effective as addressing bias during training.

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77
Multi-Selecthard

A company is training a deep learning model for image classification. Which THREE practices help reduce overfitting? (Choose three.)

Select 3 answers
A.L2 regularization
B.Increasing model depth
C.Increasing learning rate
D.Dropout
E.Data augmentation
AnswersA, D, E

L2 regularization penalizes large weights, reducing overfitting.

Why this answer

L2 regularization (also known as weight decay) adds a penalty proportional to the square of the weight magnitudes to the loss function. This discourages the model from learning overly complex patterns by forcing weights to stay small, which reduces overfitting by limiting the model's capacity to fit noise in the training data.

Exam trap

Cisco often tests the misconception that increasing model complexity (depth) or tuning the learning rate can mitigate overfitting, when in fact these changes either exacerbate the problem or address unrelated training dynamics.

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78
MCQmedium

A team has built a regression model to predict house prices. The RMSE is 50,000 on the test set. Which action is most appropriate to improve model performance?

A.Remove outliers from training data
B.Apply feature scaling
C.Add more relevant features
D.Use a different evaluation metric
AnswerC

Adding informative features can reduce bias and improve model accuracy.

Why this answer

Option C is correct because adding relevant features can capture more patterns and improve predictive accuracy. Using a different metric (A) does not improve the model. Removing outliers (B) may help if outliers exist, but adding features is generally a more systematic improvement.

Feature scaling (D) helps some algorithms but may not be the primary issue.

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79
MCQeasy

A developer needs to preprocess a dataset consisting of customer reviews for sentiment analysis. Which text preprocessing technique is most likely to improve model accuracy?

A.Stemming
B.All of the above
C.Removing stop words
D.Lowercasing
AnswerB

Combining lowercasing, stop word removal, and stemming is a common and effective preprocessing pipeline.

Why this answer

Option B is correct because all three listed techniques—stemming, removing stop words, and lowercasing—are standard text preprocessing steps that collectively improve model accuracy for sentiment analysis. Stemming reduces words to root forms to consolidate similar meanings, removing stop words eliminates noise from high-frequency but low-information tokens, and lowercasing normalizes case variations. Together, they reduce the feature space and help the model focus on sentiment-bearing terms, leading to better generalization and accuracy.

Exam trap

Cisco often tests the misconception that a single preprocessing step is sufficient, when in fact the combination of all three—stemming, stop word removal, and lowercasing—is standard practice for maximizing model accuracy in NLP tasks like sentiment analysis.

How to eliminate wrong answers

Option A is wrong because stemming alone is insufficient; while it helps consolidate word variants, it does not address noise from stop words or case sensitivity, so it is not the single most likely technique to improve accuracy. Option C is wrong because removing stop words alone reduces noise but ignores the benefits of stemming and lowercasing, which are also critical for handling morphological variations and case mismatches. Option D is wrong because lowercasing alone normalizes case but does not handle word root consolidation or removal of irrelevant high-frequency words, leaving significant noise in the feature set.

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80
MCQeasy

A company wants to predict customer churn. They have historical data with features like usage minutes, support tickets, contract length. The target is binary: churn/not churn. Which ML algorithm is best suited?

A.Logistic regression
B.Principal Component Analysis (PCA)
C.Linear regression
D.K-means clustering
AnswerA

Logistic regression models the probability of a binary outcome using a logistic function.

Why this answer

Logistic regression is the best choice because it is specifically designed for binary classification tasks like predicting churn (churn/not churn). It models the probability of the target class using a logistic (sigmoid) function, making it interpretable and efficient for this type of supervised learning problem with a categorical outcome.

Exam trap

Cisco often tests the distinction between supervised and unsupervised learning, and the trap here is that candidates may confuse dimensionality reduction (PCA) or clustering (K-means) with classification, or mistakenly apply linear regression to a binary outcome without recognizing the need for a logistic function.

How to eliminate wrong answers

Option B is wrong because Principal Component Analysis (PCA) is an unsupervised dimensionality reduction technique, not a classification algorithm; it reduces feature space but does not predict a binary target. Option C is wrong because linear regression predicts a continuous numeric output, not a binary class; using it for classification would violate the assumption of normally distributed errors and produce unbounded predictions. Option D is wrong because K-means clustering is an unsupervised learning algorithm used for grouping unlabeled data into clusters, not for predicting a known binary target variable.

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81
MCQeasy

A company is building a chatbot to answer customer queries using Amazon Lex. The development team has created a large dataset of customer interactions and intends to use Amazon SageMaker to train a custom machine learning model for natural language understanding (NLU). The team wants to integrate the trained model with Amazon Lex to handle intents and slots. The team has limited experience with SageMaker and wants to minimize operational overhead. Which solution should the team use?

A.Encapsulate the custom model in a Docker container, push it to Amazon ECR, and create a custom machine learning resource in Amazon Lex to invoke the container directly.
B.Train a custom model in SageMaker using a built-in algorithm like BlazingText, then deploy it to a SageMaker endpoint and integrate with Lex via a AWS Lambda function that calls the endpoint.
C.Use Amazon Comprehend to perform sentiment analysis and entity recognition, then map the results to Lex intents using Lambda.
D.Use SageMaker Autopilot to automatically build and train the best model, then deploy to a SageMaker endpoint and use Lambda to invoke it for Lex integration.
AnswerD

SageMaker Autopilot automates the machine learning process, minimizing manual effort, and the trained model can be deployed to an endpoint and integrated with Lex via Lambda.

Why this answer

Option D is correct because SageMaker Autopilot automates model building, tuning, and deployment, reducing the need for manual intervention and expertise. Option A requires manual algorithm selection and tuning. Option B uses Amazon Comprehend, which provides general-purpose NLP but does not allow for custom NLU model training.

Option C is not supported because Amazon Lex does not directly invoke custom Docker containers; integration is typically done via Lambda.

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82
MCQeasy

A startup needs to predict customer churn based on historical data containing labels (churned or not). Which type of machine learning should they use?

A.Reinforcement learning
B.Unsupervised learning
C.Supervised learning
D.Semi-supervised learning
AnswerC

Since the data has labels, supervised learning is appropriate for classification.

Why this answer

The startup has labeled historical data (churned or not), which is the defining characteristic of supervised learning. The goal is to learn a mapping from input features to the known output labels to predict churn for new customers. This is a classic classification problem, making supervised learning the correct choice.

Exam trap

Cisco often tests the distinction between supervised and unsupervised learning by presenting a scenario with labeled data, where candidates might mistakenly choose unsupervised learning if they overlook the presence of labels.

How to eliminate wrong answers

Option A is wrong because reinforcement learning involves an agent learning through trial-and-error interactions with an environment to maximize cumulative reward, not from labeled historical data. Option B is wrong because unsupervised learning finds hidden patterns or structures in unlabeled data, but here the labels (churned/not) are explicitly provided. Option D is wrong because semi-supervised learning uses a small amount of labeled data with a large amount of unlabeled data, but the problem states the historical data contains labels, implying fully labeled data is available.

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83
MCQhard

A deployed model on an Amazon SageMaker endpoint is experiencing high inference latency (average 500ms) during peak hours. The model is a deep neural network with 10 million parameters. The endpoint uses a single ml.c5.xlarge instance. The company wants to reduce latency to under 200ms without retraining or changing the model architecture. Which action should they take?

A.Enable automatic scaling to add more instances
B.Switch to a GPU-based instance type like ml.p2.xlarge
C.Deploy the model on a multi-model endpoint
D.Use SageMaker Neo to compile and optimize the model
AnswerD

SageMaker Neo optimizes models for target hardware, significantly reducing inference latency without changing the model.

Why this answer

SageMaker Neo compiles trained models into an optimized format for the target hardware, reducing inference latency without altering the model architecture. For a deep neural network with 10 million parameters on a CPU instance, Neo applies hardware-specific optimizations like operator fusion and memory layout tuning, which can significantly lower latency. This directly addresses the requirement to reduce latency from 500ms to under 200ms without retraining or changing the model.

Exam trap

AWS often tests the misconception that scaling or switching to GPU is the default solution for latency issues, but the trap here is that the question explicitly prohibits retraining or architecture changes, making model compilation via SageMaker Neo the only viable option that directly optimizes inference speed on the existing hardware.

How to eliminate wrong answers

Option A is wrong because automatic scaling adds more instances to handle increased request volume, but it does not reduce per-request latency; it distributes load but each request still processes on a single instance with the same inference time. Option B is wrong because switching to a GPU instance like ml.p2.xlarge may accelerate certain model types but does not guarantee latency reduction for a deep neural network with 10 million parameters, and it introduces higher cost and potential overhead from GPU initialization; the requirement is to reduce latency without retraining or architecture changes, and GPU acceleration often requires model adaptation. Option C is wrong because deploying on a multi-model endpoint is designed to host multiple models on a single endpoint to improve resource utilization, not to reduce inference latency for a single model; it adds container management overhead that could increase latency.

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84
MCQeasy

A social media company needs to automatically detect and flag toxic comments in multiple languages. They have a large stream of user comments and require real-time moderation. Which AWS service is best suited for this task?

A.Amazon Lex
B.Amazon Comprehend
C.Amazon Rekognition
D.Amazon Translate
AnswerB

Amazon Comprehend provides built-in sentiment analysis and toxic content detection in multiple languages, suitable for real-time text analysis.

Why this answer

Amazon Comprehend is the correct choice because it is a natural language processing (NLP) service that can perform real-time toxicity detection across multiple languages using its built-in content moderation and custom classification capabilities. It analyzes text streams to identify toxic comments (e.g., hate speech, threats) and integrates with AWS streaming services like Amazon Kinesis for real-time processing.

Exam trap

The trap here is that candidates may confuse Amazon Comprehend's NLP capabilities with Amazon Lex's conversational AI or Amazon Translate's language translation, assuming any language-related service can detect toxicity, but only Comprehend provides the specific text analysis APIs for content moderation.

How to eliminate wrong answers

Option A is wrong because Amazon Lex is a service for building conversational interfaces (chatbots) using automatic speech recognition (ASR) and natural language understanding (NLU), not for analyzing text for toxicity. Option C is wrong because Amazon Rekognition is designed for image and video analysis (e.g., object detection, facial recognition), not for processing text comments. Option D is wrong because Amazon Translate is a machine translation service that converts text between languages but does not perform toxicity detection or content moderation.

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85
MCQhard

An organization wants to use Amazon Rekognition to analyze images of people for a security application. They must comply with GDPR. What is the best practice?

A.Store images indefinitely for audit
B.Use celebrity recognition
C.Ensure all images are anonymized before analysis
D.Use face detection only
AnswerC

Anonymizing images (e.g., blurring faces) helps comply with privacy regulations like GDPR.

Why this answer

Option C is correct because GDPR requires that personal data, including facial images, be processed lawfully and with appropriate safeguards. Anonymizing images before analysis with Amazon Rekognition ensures that the data cannot be linked back to an identifiable person, thereby reducing GDPR compliance risk. This aligns with the principle of data minimization and privacy by design.

Exam trap

AWS often tests the misconception that using a specific feature like celebrity recognition or face detection alone automatically satisfies compliance requirements, when in fact GDPR mandates data anonymization or pseudonymization as a best practice for processing biometric data.

How to eliminate wrong answers

Option A is wrong because storing images indefinitely violates GDPR's data retention limitation principle, which mandates that personal data be kept no longer than necessary for the processing purpose. Option B is wrong because celebrity recognition is designed to identify known public figures and does not address GDPR compliance for general image analysis; it may still process personal data without anonymization. Option D is wrong because face detection alone still processes biometric data that can be used to identify individuals, and without anonymization, it does not meet GDPR requirements for lawful processing.

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86
MCQhard

A company is deploying a machine learning model for real-time fraud detection. The model must have latency under 100ms. Which infrastructure choice is most appropriate?

A.Amazon SageMaker real-time endpoints
B.Amazon EC2 with Deep Learning AMI
C.Amazon SageMaker batch transform
D.Amazon SageMaker notebook instance
AnswerA

Real-time endpoints provide low-latency inference with automatic scaling.

Why this answer

Amazon SageMaker real-time endpoints are designed for low-latency inference, typically in the tens of milliseconds, making them suitable for real-time fraud detection where latency must be under 100ms. They deploy a model behind a persistent HTTPS endpoint that auto-scales to handle incoming requests with minimal delay.

Exam trap

The trap here is that candidates often confuse batch transform with real-time inference, assuming that any SageMaker inference capability can serve low-latency requests, but batch transform is explicitly asynchronous and designed for high-throughput, not low-latency.

How to eliminate wrong answers

Option B is wrong because Amazon EC2 with Deep Learning AMI requires manual setup of the inference server, scaling, and load balancing, which introduces operational overhead and cannot guarantee sub-100ms latency without significant custom engineering. Option C is wrong because Amazon SageMaker batch transform is designed for asynchronous, offline inference on large datasets, not for real-time, low-latency predictions. Option D is wrong because Amazon SageMaker notebook instance is an interactive development environment for building and testing models, not a production inference endpoint.

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87
MCQhard

Refer to the exhibit. A SageMaker real-time endpoint is experiencing increasing latency and memory errors after running for a few hours. What is the most likely cause and recommended fix?

A.Scale the endpoint to a larger instance type, such as ml.r5.large
B.Enable auto-scaling to add instances during high load
C.Use SageMaker Debugger to identify and fix a memory leak in the inference code
D.Use SageMaker Model Monitor to detect data drift
AnswerC

The increasing memory usage over time indicates a leak; Debugger can help identify the issue.

Why this answer

Option C is correct because the symptoms—increasing latency and memory errors after running for a few hours—point to a memory leak in the inference code. SageMaker Debugger can monitor system metrics like memory utilization and detect anomalies, helping to identify the root cause of the leak. Fixing the memory leak directly resolves the progressive degradation, whereas scaling or auto-scaling only masks the symptom.

Exam trap

Cisco often tests the distinction between scaling solutions (which address capacity) and debugging tools (which address code defects), trapping candidates who confuse symptom relief with root cause resolution.

How to eliminate wrong answers

Option A is wrong because scaling to a larger instance type (e.g., ml.r5.large) provides more memory but does not address the underlying memory leak; the leak will eventually exhaust the larger memory pool as well. Option B is wrong because enabling auto-scaling adds more instances to handle load, but it does not fix the memory leak in the inference code; each instance will still experience the same progressive memory exhaustion. Option D is wrong because SageMaker Model Monitor detects data drift (changes in input data distribution), not memory leaks or latency issues caused by code defects.

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88
MCQhard

A financial institution is deploying a fraud detection model using Amazon SageMaker. The model must be able to handle sudden spikes in inference requests during promotional events while keeping costs low. The team wants to use a serverless architecture to avoid provisioning idle capacity and to scale automatically from zero. However, the inference latency requirement is under 5 seconds for each request. Which SageMaker inference option should they choose?

A.Use Amazon SageMaker Serverless Inference
B.Use Amazon SageMaker Multi-Model Endpoints
C.Use Amazon SageMaker real-time endpoints with auto-scaling
D.Use Amazon SageMaker Asynchronous Inference
AnswerA

Serverless Inference scales automatically from zero and reduces costs during idle periods.

Why this answer

Amazon SageMaker Serverless Inference is the correct choice because it automatically scales from zero to handle sudden spikes in inference requests, aligning with the requirement to avoid provisioning idle capacity. It also meets the sub-5-second latency requirement for fraud detection, as it is designed for low-latency, on-demand inference without managing underlying infrastructure.

Exam trap

AWS often tests the misconception that serverless inference cannot meet low-latency requirements, but SageMaker Serverless Inference is specifically designed for sub-second to few-second latency, making it suitable for real-time fraud detection scenarios.

How to eliminate wrong answers

Option B is wrong because Multi-Model Endpoints require provisioned instances and do not scale from zero; they are designed to host multiple models on a single endpoint but still incur costs for idle capacity. Option C is wrong because real-time endpoints with auto-scaling still require a baseline of provisioned instances, which can lead to idle capacity costs during low-traffic periods, and they do not scale from zero. Option D is wrong because Asynchronous Inference is intended for large payloads and longer processing times (typically minutes), not for sub-5-second latency requirements, and it queues requests rather than providing real-time responses.

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89
MCQeasy

A team is evaluating a classification model. The confusion matrix shows: TP=80, FN=20, FP=10, TN=90. What is the precision?

A.0.89
B.0.75
C.0.80
D.0.90
AnswerA

Precision = 80/(80+10) = 0.8889 ≈ 0.89.

Why this answer

Precision is calculated as TP / (TP + FP). Here, TP=80 and FP=10, so precision = 80 / (80 + 10) = 80 / 90 = 0.888..., which rounds to 0.89. This metric measures the proportion of positive identifications that were actually correct.

Exam trap

Cisco often tests the distinction between precision and recall by providing confusion matrix values that make one metric easy to miscalculate if you confuse the denominator (TP+FP vs TP+FN).

How to eliminate wrong answers

Option B (0.75) is wrong because it incorrectly uses FN in the denominator, likely confusing precision with recall (TP / (TP + FN)). Option C (0.80) is wrong because it uses only TP divided by the total number of actual positives (TP + FN), which is recall, not precision. Option D (0.90) is wrong because it uses TN in the denominator or calculates accuracy (TP + TN) / total, which is not precision.

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90
MCQhard

A team is training a deep learning model using Horovod distributed training on SageMaker. They observe that the loss stops decreasing after a few epochs. Which technique should they implement to reduce overfitting?

A.Increase learning rate
B.Add more layers to the model
C.Reduce the number of epochs
D.Use dropout regularization
AnswerD

Dropout is a regularization technique that reduces overfitting.

Why this answer

Dropout regularization randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on specific features and forces it to learn more robust representations. This directly addresses overfitting, which is the likely cause of the loss plateauing after a few epochs in a Horovod distributed training setup on SageMaker.

Exam trap

Cisco often tests the misconception that early stopping (reducing epochs) is a regularization technique to reduce overfitting, but the trap here is that early stopping only halts training and does not actively prevent the model from memorizing noise during the epochs it does train.

How to eliminate wrong answers

Option A is wrong because increasing the learning rate can cause the optimizer to overshoot minima, leading to divergence or unstable training, not a reduction in overfitting. Option B is wrong because adding more layers increases model capacity, which typically worsens overfitting by making it easier for the model to memorize noise. Option C is wrong because reducing the number of epochs only stops training earlier; it does not address the underlying overfitting issue, and the loss may still plateau due to memorization rather than convergence.

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91
MCQeasy

A startup with limited ML expertise wants to quickly prototype a binary classification model using a small customer dataset. They need a managed environment to run Jupyter notebooks and access pre-built algorithms. Which AWS service should they choose?

A.AWS Lambda
B.Amazon SageMaker
C.Amazon EMR
D.AWS Glue
AnswerB

SageMaker provides managed notebooks and built-in algorithms for quick experimentation.

Why this answer

Amazon SageMaker is the correct choice because it provides a fully managed environment for Jupyter notebooks and includes built-in, pre-built algorithms for binary classification. This allows the startup to quickly prototype without deep ML expertise, as SageMaker handles infrastructure, scaling, and model training.

Exam trap

Cisco often tests the distinction between managed ML platforms (SageMaker) and general-purpose compute or data processing services (Lambda, EMR, Glue), leading candidates to pick a service that can run code but lacks the specific notebook and pre-built algorithm capabilities required.

How to eliminate wrong answers

Option A is wrong because AWS Lambda is a serverless compute service for running code in response to events, not a managed environment for Jupyter notebooks or pre-built ML algorithms. Option C is wrong because Amazon EMR is a big data processing service using frameworks like Apache Spark and Hadoop, not designed for interactive Jupyter notebook-based ML prototyping with pre-built algorithms. Option D is wrong because AWS Glue is a serverless data integration and ETL service, not a platform for running Jupyter notebooks or accessing pre-built ML models.

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92
MCQmedium

A data scientist is building a binary classification model for fraud detection. The dataset is highly imbalanced (99% legitimate, 1% fraud). Which metric is most appropriate to evaluate model performance?

A.Accuracy
B.F1-score
C.Recall
D.Precision
AnswerB

F1-score is the harmonic mean of precision and recall, providing a balanced evaluation for imbalanced datasets.

Why this answer

In highly imbalanced datasets (99% legitimate, 1% fraud), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy but fail to detect any fraud. The F1-score is the harmonic mean of precision and recall, providing a balanced measure that accounts for both false positives and false negatives, making it the most appropriate metric for evaluating fraud detection models.

Exam trap

Cisco often tests the misconception that accuracy is always the best metric, especially when candidates overlook the impact of class imbalance on model evaluation.

How to eliminate wrong answers

Option A is wrong because accuracy is not suitable for imbalanced datasets; it can be artificially high (e.g., 99%) even if the model never identifies any fraud, as it only measures overall correct predictions. Option C is wrong because recall alone (true positive rate) ignores false positives; a model that predicts all transactions as fraud would have perfect recall but terrible precision, leading to many false alarms. Option D is wrong because precision alone ignores false negatives; a model that predicts only one transaction as fraud (and gets it right) could have 100% precision but miss the other 99 fraud cases, failing the detection goal.

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93
Multi-Selecthard

Which TWO of the following are best practices for preparing training data for a machine learning model?

Select 2 answers
A.Handle missing values by imputing or removing them.
B.Split the data into training, validation, and test sets.
C.Remove all outliers to improve model robustness.
D.Use the entire dataset for training to maximize data usage.
E.Avoid shuffling the data to preserve original order.
AnswersA, B

Missing values can cause errors or bias; imputation or removal is a standard practice.

Why this answer

Option A is correct because handling missing values is a critical data preprocessing step. Missing data can introduce bias or cause algorithms to fail. Imputation (e.g., using mean, median, or model-based methods) or removal of rows/columns with missing values ensures the dataset is complete and suitable for training, preventing errors during model fitting.

Exam trap

Cisco often tests the misconception that removing all outliers is always beneficial, when in fact domain knowledge is required to distinguish between noise and legitimate extreme values that may be critical for model accuracy.

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94
Multi-Selecthard

Refer to the exhibit. A data scientist trained an XGBoost model using Amazon SageMaker. Which TWO actions should the data scientist take to improve the model's performance based on the exhibited training job metrics and resource configuration?

Select 2 answers
A.Reduce the VolumeSizeInGB to save costs.
B.Decrease the learning rate and increase the number of training rounds.
C.Increase the maximum number of leaf nodes in the XGBoost algorithm.
D.Use a distributed training strategy by increasing InstanceCount to 4.
E.Switch to a more powerful instance type to reduce training time.
AnswersB, C

A lower learning rate with more rounds typically improves convergence and model performance.

Why this answer

Option A is correct because increasing the maximum number of leaf nodes allows the model to capture more complex patterns, potentially improving AUC. Option C is correct because a lower learning rate with more training rounds often leads to better convergence and performance. Option B (distributed training) primarily reduces training time, not model performance.

Option D (switching instance type) also speeds up training but doesn't directly improve metrics. Option E (reducing volume size) saves cost but does not enhance performance.

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95
Multi-Selecteasy

A company wants to use AWS services to process natural language text. Which TWO AWS services provide natural language processing (NLP) capabilities? (Select TWO.)

Select 2 answers
A.Amazon Translate
B.Amazon Rekognition
C.Amazon Comprehend
D.Amazon Polly
E.Amazon Lex
AnswersC, E

Comprehend provides NLP features like sentiment analysis, entity recognition, and key phrase extraction.

Why this answer

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract insights such as entities, key phrases, sentiment, and syntax from text. Amazon Lex provides NLP capabilities through automatic speech recognition (ASR) and natural language understanding (NLU) to build conversational interfaces like chatbots.

Exam trap

The trap here is that candidates often confuse text-to-speech (Polly) or translation (Translate) with NLP, but these services do not perform language understanding or analysis—they only convert or generate speech/translation without extracting meaning.

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96
MCQmedium

A machine learning engineer is using Amazon SageMaker to train a model and wants to automatically stop the training job if the loss does not improve for 10 consecutive epochs. Which SageMaker feature should be used?

A.SageMaker built-in algorithms with early stopping
B.SageMaker Training Compiler
C.SageMaker Debugger
D.SageMaker Experiments
AnswerA

Built-in algorithms support early stopping parameters.

Why this answer

Amazon SageMaker built-in algorithms support early stopping, which allows you to automatically terminate a training job when a specified metric, such as loss, stops improving for a defined number of consecutive epochs. This feature is configured directly in the algorithm's hyperparameters (e.g., `early_stopping_patience` for the XGBoost algorithm) and helps save compute time and cost by preventing overfitting.

Exam trap

Cisco often tests the distinction between monitoring (Debugger) and automated action (early stopping), leading candidates to mistakenly choose Debugger because it can detect stagnation, but it cannot stop the job without custom code or a separate hook.

How to eliminate wrong answers

Option B is wrong because SageMaker Training Compiler is a feature that accelerates training by optimizing the model graph and memory usage, but it does not provide any mechanism to monitor metrics or stop training based on convergence. Option C is wrong because SageMaker Debugger is used to monitor training in real time, capture tensors, and set rules (e.g., vanishing gradient detection), but it does not automatically stop the training job; it only emits alerts or saves data for analysis. Option D is wrong because SageMaker Experiments is a tool for organizing, tracking, and comparing training runs, but it has no built-in capability to halt a training job based on metric stagnation.

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97
Multi-Selectmedium

Which THREE statements about Amazon SageMaker Ground Truth are correct? (Choose three.)

Select 3 answers
A.It can only be used for text data.
B.It provides built-in workflows for image classification and object detection.
C.It supports automated data labeling using active learning.
D.It integrates with Amazon SageMaker to use the labeled data for training.
E.It can only use a public workforce from Amazon Mechanical Turk.
AnswersB, C, D

Ground Truth supports these tasks.

Why this answer

Amazon SageMaker Ground Truth provides built-in workflows for common tasks like image classification and object detection, which simplifies the setup of labeling jobs. These pre-built templates handle the UI and data formatting, allowing users to focus on the labeling instructions rather than building the labeling interface from scratch.

Exam trap

AWS often tests the misconception that Ground Truth is limited to text data or only supports public workforces, while in reality it handles multiple data modalities and offers flexible workforce options including private and vendor-managed.

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