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AWS Certified Machine Learning Specialty MLS-C01 (MLS-C01) — Questions 901–975

1672 questions total · 23pages · All types, answers revealed

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901

MCQeasy

A data scientist is training a deep learning model for image classification using Amazon SageMaker. The training job is taking too long. The data scientist wants to use distributed training across multiple GPUs to speed up the process. Which SageMaker feature should the data scientist use?

A.SageMaker Distributed Training Libraries

B.SageMaker Managed Spot Training

C.SageMaker Hyperparameter Tuning

D.SageMaker Automatic Model Tuning

AnswerA

Distributed training libraries enable training across multiple GPUs, reducing wall-clock time.

Why this answer

SageMaker Distributed Training Libraries provide optimized implementations of data parallelism and model parallelism that automatically partition the model and data across multiple GPUs, reducing training time for deep learning models. This is the correct choice because the question specifically asks for a feature to enable distributed training across multiple GPUs, which is exactly what these libraries are designed for.

Exam trap

The trap here is that candidates often confuse cost-saving features (like Spot Training) with performance-optimization features (like distributed training), or they mistakenly think hyperparameter tuning can parallelize a single training job across GPUs.

How to eliminate wrong answers

Option B is wrong because SageMaker Managed Spot Training reduces cost by using spare EC2 capacity, not by distributing training across multiple GPUs; it does not inherently speed up training. Option C is wrong because SageMaker Hyperparameter Tuning automates the search for optimal hyperparameters, but it does not distribute a single training job across multiple GPUs. Option D is wrong because SageMaker Automatic Model Tuning is another name for hyperparameter tuning (same as option C) and does not provide distributed training capabilities.

Full explanation →

902

MCQeasy

A data scientist is training a binary classification model for fraud detection. The dataset is highly imbalanced with only 1% fraudulent transactions. The model currently achieves 99% accuracy but only catches 5% of actual fraud cases. Which metric should the data scientist focus on to better evaluate model performance?

A.Precision

B.Accuracy

C.Root Mean Squared Error (RMSE)

D.Recall

AnswerD

Recall measures the ability to find all positive samples, which is crucial for fraud detection.

Why this answer

In fraud detection with highly imbalanced data (1% fraud), accuracy is misleading because a model can achieve 99% accuracy by simply predicting 'not fraud' for all transactions. Recall (true positive rate) measures the proportion of actual fraud cases correctly identified, which is critical when the cost of missing fraud is high. The model currently catches only 5% of fraud, so improving recall is the primary goal to reduce false negatives.

Exam trap

AWS often tests the misconception that accuracy is always the best metric, but in imbalanced classification, recall or precision-recall curves are more informative, and candidates must recognize that high accuracy can mask poor minority class performance.

How to eliminate wrong answers

Option A is wrong because precision measures the proportion of predicted fraud cases that are actually fraud, which is not the primary concern when the model misses 95% of actual fraud; precision focuses on false positives, not false negatives. Option B is wrong because accuracy is dominated by the majority class (99% non-fraud) and does not reflect the model's poor performance on the minority fraud class; a model can have high accuracy while failing to detect fraud. Option C is wrong because RMSE is a regression metric that measures the average magnitude of errors in continuous predictions, not suitable for evaluating binary classification performance, especially with imbalanced classes.

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903

Multi-Selectmedium

Which THREE of the following are common issues that can be identified during exploratory data analysis? (Select THREE.)

Select 3 answers

A.Multicollinearity between features

B.High latency in API endpoints

C.Gradient vanishing in neural networks

D.Class imbalance in the target variable

E.Missing values in features

AnswersA, D, E

High correlation between features can be detected via correlation matrix.

Why this answer

Multicollinearity occurs when two or more features in a dataset are highly correlated, meaning they contain redundant information. During exploratory data analysis (EDA), correlation matrices and variance inflation factor (VIF) calculations can reveal this issue, which can destabilize linear regression models and inflate coefficient standard errors.

Exam trap

The MLS-C01 exam often tests the boundary between data-level issues (EDA) and model training issues, so candidates mistakenly select gradient vanishing (a deep learning optimization problem) or API latency (an operational concern) as EDA findings.

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904

Multi-Selectmedium

A data scientist is performing EDA on a dataset with both numeric and categorical features. Which TWO techniques are appropriate for visualizing the relationship between a numeric feature and a binary categorical target?

Select 2 answers

A.Histogram

B.Stacked bar chart

C.Violin plot grouped by target

D.Box plot grouped by target

E.Scatter plot

AnswersC, D

Violin plots show distribution and density across categories.

Why this answer

Correct options are C (violin plot grouped by target) and D (box plot grouped by target). Both are effective for visualizing the distribution of a numeric feature across two categories of a binary target. A violin plot combines a box plot and a density plot, showing the full distribution shape, while a box plot displays medians, quartiles, and outliers.

Option A (histogram) shows distribution of a single numeric variable but does not directly compare groups. Option B (stacked bar chart) is for categorical vs categorical data. Option E (scatter plot) is for two numeric variables.

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905

MCQhard

A data scientist is working with a dataset that has imbalanced classes (1% positive). They want to explore the data before modeling. Which visualization technique is most appropriate to understand the distribution of features with respect to the target class?

A.Box plots grouped by class

B.Parallel coordinates plot

C.Histograms overlaid by class

D.Scatter plot matrix

AnswerB

Parallel coordinates plot effectively displays patterns across high-dimensional data, allowing comparison of minority and majority class distributions.

Why this answer

Parallel coordinates plot can show feature patterns for minority vs majority class in high dimensions. Option A is wrong because box plots are univariate and do not show interactions between features. Option C is wrong because histograms are univariate and do not show interaction.

Option D is wrong because scatter plot matrices become cluttered with many features.

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906

MCQmedium

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance. Which action will the notebook be able to perform?

A.Create a training job

B.Create a model

C.Read data from S3

D.Invoke a SageMaker endpoint

AnswerD

The policy explicitly allows sagemaker:InvokeEndpoint.

Why this answer

The IAM policy attached to the SageMaker notebook instance grants only the `sagemaker:InvokeEndpoint` action. This action allows the notebook to send inference requests to a deployed SageMaker endpoint. No other SageMaker or S3 actions are permitted, so the notebook can only invoke the endpoint.

Exam trap

The trap here is that candidates may assume a SageMaker notebook instance automatically has broad permissions to perform all SageMaker actions, but the IAM policy explicitly limits the notebook to only `InvokeEndpoint`, so only endpoint invocation is allowed.

How to eliminate wrong answers

Option A is wrong because the policy does not include `sagemaker:CreateTrainingJob`, which is required to create a training job. Option B is wrong because the policy lacks `sagemaker:CreateModel`, which is necessary to create a SageMaker model. Option C is wrong because the policy does not grant any S3 actions (e.g., `s3:GetObject`), so the notebook cannot read data from S3.

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907

MCQeasy

A data scientist is exploring a dataset with a column 'transaction_date'. They want to create features for day of week and month. What is the correct AWS service to schedule a recurring ETL job for this transformation?

A.Amazon Athena

B.AWS Glue

C.Amazon SageMaker

D.AWS Lambda

AnswerB

Glue is a managed ETL service.

Why this answer

AWS Glue is a serverless ETL service that can be scheduled to run recurring jobs for data transformation, such as extracting day of week and month from a date column. Option A is wrong because Amazon Athena is an interactive query service, not an ETL scheduler. Option C is wrong because Amazon SageMaker is a machine learning platform, not an ETL service.

Option D is wrong because AWS Lambda can run code on a schedule but is not a full-fledged ETL service for recurring transformations of large datasets.

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908

MCQeasy

A data scientist is exploring a dataset and wants to identify outliers in a numerical feature. The feature is not normally distributed. Which technique is robust to non-normal distributions?

A.Compute the Median Absolute Deviation (MAD) and flag values with MAD > 3.

B.Use the IQR method: flag values below Q1 - 1.5*IQR or above Q3 + 1.5*IQR.

C.Calculate the Z-score and flag values with |Z| > 3.

D.Flag values more than 3 standard deviations from the mean.

AnswerB

Does not assume normality; uses robust quartiles.

Why this answer

The IQR method, because it does not assume a normal distribution and uses quartiles to identify outliers. Option A (MAD) is robust but compares deviations from the median; however, the IQR method is more commonly used for non-normal data. Option C (Z-score) assumes normality.

Option D (flagging values more than 3 standard deviations from the mean) also assumes normality.

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909

MCQmedium

A company is deploying a real-time fraud detection model using Amazon SageMaker. The model must make predictions in under 100 milliseconds. The data scientist uses a pre-trained XGBoost model and deploys it to a SageMaker endpoint with an ml.c5.xlarge instance. After load testing, the average latency is 150 ms. Which action should the data scientist take to reduce latency?

A.Reduce the number of trees in the XGBoost model

B.Deploy multiple instances behind a load balancer

C.Enable SageMaker Neo to compile the model for the target instance

D.Use a larger instance type to increase compute capacity

AnswerC

Neo optimization can reduce inference latency by optimizing the model for the hardware.

Why this answer

SageMaker Neo optimizes trained models for the target hardware platform by compiling them into an efficient runtime. This reduces inference latency without changing the model architecture, making it ideal for meeting the sub-100ms requirement when the current latency is 150ms on an ml.c5.xlarge instance.

Exam trap

The trap here is that candidates often confuse scaling out (Option B) or scaling up (Option D) with latency reduction, but these primarily address throughput or resource contention, not the per-request inference time on a single instance.

How to eliminate wrong answers

Option A is wrong because reducing the number of trees in the XGBoost model would degrade model accuracy and is not a targeted latency optimization technique; it may also not achieve the required latency reduction without significant accuracy loss. Option B is wrong because deploying multiple instances behind a load balancer improves throughput and availability but does not reduce per-request latency; it may even add network overhead. Option D is wrong because using a larger instance type increases compute capacity but does not guarantee lower latency for a single inference request; it may also increase cost without addressing the root cause of model execution inefficiency.

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910

Multi-Selecteasy

Which TWO of the following are valid methods for handling missing values in a dataset before training a machine learning model?

Select 2 answers

A.Remove rows that contain missing values

B.Use a decision tree algorithm that handles missing values internally

C.Increase the number of trees in a random forest

D.Replace missing values with zero

E.Impute missing values with the mean of the column

AnswersA, E

If the proportion of missing data is small, dropping rows is a valid option.

Why this answer

Removing rows with missing values (listwise deletion) is a straightforward and valid method when the missing data is random and the dataset is large enough that the loss of rows does not significantly reduce statistical power or introduce bias. This approach ensures that only complete cases are used for training, avoiding the need to estimate missing values.

Exam trap

The MLS-C01 exam often tests the misconception that decision tree algorithms inherently handle missing values without any preprocessing, but in practice, they require explicit handling (e.g., surrogate splits) and do not automatically resolve missing data for all model training scenarios.

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911

MCQmedium

A data scientist is using Amazon SageMaker to train a model and wants to use a custom Docker container for training. The container requires access to a private Amazon ECR repository. Which IAM role configuration is needed?

A.Attach an IAM policy to the SageMaker execution role that allows ecr:GetDownloadUrlForLayer, ecr:BatchGetImage, and ecr:GetAuthorizationToken for the ECR repository.

B.Use the AWS account owner's IAM role as the SageMaker execution role.

C.Create a new IAM user with ECR access and store credentials in SageMaker.

D.Add a bucket policy to the ECR repository allowing access from the SageMaker execution role.

AnswerA

These permissions allow SageMaker to pull the container image.

Why this answer

The SageMaker execution role must have an IAM policy that includes ecr:GetDownloadUrlForLayer, ecr:BatchGetImage, and ecr:GetAuthorizationToken for the ECR repository. This is achieved by attaching an IAM policy to the SageMaker execution role, as described in Option A. Option B is incorrect because using the AWS account owner's role is not appropriate and would grant excessive permissions.

Option C is incorrect because IAM users are not used for SageMaker execution roles; roles are used instead. Option D is incorrect because bucket policies apply to S3 buckets, not ECR repositories; ECR uses resource-based policies on the repository itself.

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912

MCQmedium

A machine learning engineer is deploying a custom XGBoost model for real-time inference on Amazon SageMaker. The model was trained using the SageMaker XGBoost built-in algorithm. The endpoint is deployed with an ml.m5.large instance and is receiving around 50 requests per second. The engineer notices that the endpoint's latency is around 200 ms, but the requirement is under 100 ms. The model's serialized format is a .tar.gz file. The engineer wants to reduce inference latency without modifying the model or retraining. What should the engineer do?

A.Configure SageMaker Debugger to optimize the inference code.

B.Use SageMaker Elastic Inference to attach an accelerator.

C.Use SageMaker Neo to compile the model for the target instance.

D.Use SageMaker Batch Transform instead of a real-time endpoint.

AnswerC

SageMaker Neo compiles the trained model to optimize it for the target hardware (ml.m5.large), which can improve inference speed and reduce latency without modifying the model.

Why this answer

SageMaker Neo compiles trained models to optimize them for target hardware, reducing inference latency without modifying the model. Option A is wrong because SageMaker Debugger is used for monitoring training jobs and debugging, not for optimizing inference code. Option B is wrong because SageMaker Elastic Inference attaches GPU acceleration, which is beneficial for deep learning models but not for XGBoost (a tree-based model).

Option D is wrong because SageMaker Batch Transform is designed for batch predictions on large datasets, not for real-time inference with low latency requirements.

Exam trap

Candidates may incorrectly choose Elastic Inference (B) thinking it speeds up all models, but it is only useful for deep learning models, not tree-based XGBoost.

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913

Multi-Selecthard

A company is training a deep learning model for object detection using Amazon SageMaker. The training job is taking too long. Which TWO actions can reduce training time?

Select 2 answers

A.Use distributed training with multiple GPUs

B.Use a larger instance type with more vCPUs

C.Use SageMaker managed spot training

D.Use a smaller batch size initially and increase gradually (warm-up)

E.Increase the number of epochs

AnswersA, D

Correct. Distributed training with multiple GPUs (e.g., SageMaker data parallelism) splits the data or model across devices, reducing per-epoch time.

Why this answer

Distributed training (A) reduces wall-clock time by splitting the workload across multiple GPUs. Using a smaller batch size initially and increasing gradually (warm-up) (D) can help stabilize training and speed convergence by allowing the model to adjust more smoothly early on. Managed spot training (C) reduces cost, not training time, and may increase time due to interruptions.

A larger instance (B) does not necessarily reduce training time if the bottleneck is GPU-related, and increasing the number of epochs (E) increases training time.

Exam trap

A common trap is confusing cost-saving techniques (like spot training) with performance-enhancing techniques (like distributed training).

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914

Multi-Selecthard

Which TWO techniques are used to handle missing values in a dataset before training? (Choose 2.)

Select 2 answers

A.Mean or median imputation.

B.Min-max scaling.

C.Removing rows or columns with missing values.

D.One-hot encoding.

E.Principal component analysis (PCA).

AnswersA, C

Imputation replaces missing values with central tendency.

Why this answer

Mean or median imputation is a common method to fill missing values with a central tendency measure. Option C is correct because removing rows or columns with missing values is a valid approach, especially when the missing data is minimal. Option B (min-max scaling) is for normalizing numerical features, not for missing values.

Option D (one-hot encoding) is for converting categorical variables into numerical format. Option E (PCA) is for dimensionality reduction, not missing value handling.

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915

MCQeasy

A company uses Amazon SageMaker to train a linear regression model on a dataset with 10 million rows and 50 features. The training job takes 8 hours to complete. A data scientist wants to reduce the training time to under 2 hours without changing the dataset size or the model algorithm. The SageMaker instance type currently used is ml.m5.2xlarge. Which action should the data scientist take to achieve the desired training time?

A.Change the instance type to ml.p3.2xlarge (GPU instance).

B.Change the instance type to ml.m5.4xlarge (double the vCPUs and memory).

C.Reduce the number of features from 50 to 25.

D.Use SageMaker's distributed training with 4 ml.m5.2xlarge instances.

AnswerD

Distributed training parallelizes computation across instances, significantly reducing training time.

Why this answer

Using SageMaker's distributed training with multiple instances parallelizes the computation across 4 ml.m5.2xlarge instances, reducing wall-clock time by approximately a factor of 4, which can bring the 8-hour job down to around 2 hours. Option A (GPU instance) is not optimal for linear regression, which is CPU-bound. Option B (doubling vCPUs) provides only a 2x improvement, insufficient to reach under 2 hours.

Option C (reducing features) changes the dataset and is not allowed per the requirement.

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916

MCQhard

A data scientist is using Amazon Athena to query a CSV file stored in S3. The query fails with the error: 'HIVE_CANNOT_OPEN_SPLIT: Number of fields in line 1502 does not match number of fields in the first line.' What is the most likely cause?

A.The CSV file uses a different delimiter than comma.

B.The CSV file is missing a header row.

C.The CSV file is too large for Athena to process.

D.The CSV file has inconsistent number of columns in some rows.

AnswerD

The error indicates row 1502 has 5 fields while header has 4.

Why this answer

The error indicates that a row has more fields than the header, which is exactly what happens when the CSV file has inconsistent number of columns in some rows. Option A is incorrect because the error does not mention delimiter; a different delimiter would cause all rows to have wrong number of fields, not just some. Option B is incorrect because missing header would cause Athena to treat the first row as data, not cause mismatched field counts later.

Option C is incorrect because Athena can handle large files; the error is about schema mismatch, not file size.

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917

MCQmedium

A data scientist is training a neural network on a dataset with 1 million images. The training loss decreases steadily but the validation loss starts to increase after 10 epochs. Which action should the scientist take to improve generalization?

A.Implement early stopping

B.Add more layers to the network

C.Reduce the learning rate

D.Increase the number of epochs

AnswerA

Early stopping halts training when validation loss stops improving, preventing overfitting. This is the most direct solution.

Why this answer

Increasing validation loss while training loss decreases indicates overfitting. Early stopping (Option A) halts training when validation loss stops improving, directly preventing overfitting. Option B (adding more layers) increases model capacity and typically worsens overfitting.

Option C (reducing learning rate) might slow training but does not directly stop overfitting. Option D (increasing epochs) would continue training and likely worsen overfitting.

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918

MCQeasy

A data scientist wants to use a linear regression model to predict house prices. After training, the model shows high bias and low variance. Which action would most likely improve the model's performance?

A.Add polynomial features to capture non-linear relationships.

B.Increase L2 regularization strength.

C.Use a simpler model, such as linear regression without interaction terms.

D.Reduce the amount of training data.

AnswerA

Increasing model complexity reduces bias by better fitting the data.

Why this answer

High bias indicates underfitting, meaning the model is too simple to capture underlying patterns. Adding polynomial features increases model complexity, allowing it to better fit the training data and reduce bias. Option B is incorrect because increasing L2 regularization strength penalizes large coefficients, which increases bias and makes underfitting worse.

Option C is incorrect because using a simpler model (e.g., linear regression without interaction terms) would further increase bias. Option D is incorrect because reducing training data does not address bias; it can increase variance, but bias remains high.

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919

MCQeasy

A data scientist has a dataset with 500 features and wants to reduce dimensionality for visualization. Which technique is most appropriate for identifying the two components that capture the most variance?

A.t-Distributed Stochastic Neighbor Embedding (t-SNE)

B.Linear Discriminant Analysis (LDA)

C.Principal Component Analysis (PCA)

D.K-means clustering

AnswerC

PCA projects data onto directions of maximum variance.

Why this answer

Principal Component Analysis (PCA) is a linear dimensionality reduction technique that finds the directions (principal components) of maximum variance in the data, making it ideal for identifying the two components that capture the most variance for visualization. Option A is incorrect because t-SNE is a non-linear technique focused on preserving local neighborhood structure, not on maximizing global variance. Option B is incorrect because Linear Discriminant Analysis (LDA) is a supervised method that requires class labels to separate classes, not to maximize variance.

Option D is incorrect because K-means clustering is an unsupervised algorithm for grouping data points, not a dimensionality reduction method.

Full explanation →

920

MCQmedium

A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents only 5% of the data. The model currently achieves 95% accuracy but only 10% recall on the positive class. Which metric should the scientist focus on to improve the model's ability to detect the positive class?

A.Recall

B.Accuracy

C.Precision

D.AUC-ROC

AnswerA

Recall measures the proportion of actual positives correctly identified.

Why this answer

(Recall) is the correct focus because recall measures the proportion of actual positive cases correctly identified. With only 10% recall, the model is missing most positive cases despite high accuracy due to class imbalance. Improving recall directly addresses the goal of detecting the positive class.

Option B (Accuracy) is misleading in imbalanced datasets as it can be high even if the model predicts all negatives. Option C (Precision) measures the proportion of positive predictions that are correct, which may not increase recall. Option D (AUC-ROC) is a global metric that may not reflect improvements in recall specifically.

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921

Multi-Selecthard

A machine learning team is using Amazon SageMaker to train a model with a large dataset stored in S3. The training job is taking too long. Which THREE of the following actions can reduce training time? (Choose three.)

Select 3 answers

A.Decrease the batch size.

B.Use a GPU instance with more powerful GPUs.

C.Use distributed training with multiple instances.

D.Use Pipe input mode instead of File mode for the training data.

E.Increase the batch size.

AnswersB, C, D

Faster GPUs reduce computation time.

Why this answer

Using a GPU instance with more powerful GPUs (Option B) reduces training time because it increases the parallel compute capacity for matrix operations, which are the core of deep learning. Amazon SageMaker allows you to select instances like p3.16xlarge with NVIDIA V100 GPUs, which offer significantly higher FLOPS compared to smaller GPU instances, directly accelerating model training.

Exam trap

The trap here is that candidates often confuse batch size adjustments as a primary performance lever, but the exam tests understanding that hardware upgrades (GPU power), parallelism (distributed training), and data streaming (Pipe mode) are the most direct and reliable methods to reduce training time in SageMaker.

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922

Multi-Selecthard

A company is deploying a machine learning model using Amazon SageMaker. The model must be updated frequently without downtime. Which TWO strategies can achieve this? (Choose two.)

Select 2 answers

A.Update the model artifact on the existing endpoint.

B.Delete the existing endpoint and create a new one.

C.Use blue/green deployment with endpoint variants.

D.Use rolling update with multiple instances.

E.Use canary deployment by gradually shifting traffic.

AnswersC, E

Traffic is shifted gradually.

Why this answer

Amazon SageMaker supports blue/green deployment using endpoint variants, where you can deploy a new model version alongside the current one and then shift all traffic to the new variant once validated. This approach ensures zero downtime because the existing endpoint remains active during the transition, and traffic is switched atomically. Option E is correct because canary deployment with SageMaker allows you to gradually shift a small percentage of traffic to a new model variant, monitor its performance, and then ramp up to 100% if successful, all without interrupting the service.

Exam trap

The trap here is that candidates confuse the concept of 'updating' a model with the ability to directly modify an existing endpoint, but SageMaker requires immutable deployments, and only traffic-shifting strategies like blue/green or canary deployments achieve zero-downtime updates.

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923

MCQhard

A machine learning engineer is deploying a model to an Amazon SageMaker endpoint for real-time inference. The model is a large ensemble that requires 4 GB of memory. The engineer wants to minimize cost while ensuring the endpoint can handle up to 100 concurrent requests with a latency under 200 ms. Which instance configuration is most appropriate?

A.Two ml.t3.medium instances behind a load balancer.

B.One ml.c5.xlarge instance with auto-scaling up to 2 instances.

C.One ml.m5.2xlarge instance.

D.One ml.p3.2xlarge instance.

AnswerB

ml.c5.xlarge has 4 GB memory, cost-effective, and auto-scaling handles load.

Why this answer

The ml.c5.xlarge instance provides sufficient compute (4 vCPUs, 8 GB memory) for the 4 GB model, and auto-scaling up to 2 instances allows handling 100 concurrent requests with low latency while minimizing cost during low traffic. The ml.c5 family is optimized for compute-intensive inference, and auto-scaling ensures the endpoint scales out only when needed, avoiding over-provisioning.

Exam trap

The trap here is that candidates often choose a single large instance (like ml.m5.2xlarge) thinking it simplifies management, but auto-scaling with a smaller instance type is more cost-effective and still meets latency requirements under variable load.

How to eliminate wrong answers

Option A is wrong because two ml.t3.medium instances (2 vCPUs, 4 GB memory each) are burstable and may not sustain the required 200 ms latency under load, as t3 instances use CPU credits and can throttle under sustained high concurrency. Option C is wrong because one ml.m5.2xlarge instance (8 vCPUs, 32 GB memory) is over-provisioned for the 4 GB model and 100 concurrent requests, leading to higher cost without benefit. Option D is wrong because one ml.p3.2xlarge instance (8 vCPUs, 61 GB memory, GPU) is designed for GPU-accelerated workloads like deep learning, not for a large ensemble model that only needs 4 GB memory, making it unnecessarily expensive.

Full explanation →

924

MCQeasy

A data analyst is using Amazon SageMaker Studio to perform exploratory data analysis on a dataset stored in S3. The analyst wants to generate summary statistics and visualizations quickly. Which built-in feature of SageMaker Studio should the analyst use?

A.SageMaker Ground Truth

B.SageMaker Data Wrangler

C.SageMaker Autopilot

D.SageMaker Clarify

AnswerB

Data Wrangler provides visual EDA capabilities like summary stats and charts.

Why this answer

SageMaker Data Wrangler is a built-in visual data preparation tool in SageMaker Studio that provides summary statistics, histograms, and correlation matrices without writing code. Option A (SageMaker Ground Truth) is for data labeling, not EDA. Option C (SageMaker Autopilot) automates machine learning model building.

Option D (SageMaker Clarify) is for bias detection and model explainability.

Full explanation →

925

MCQhard

A data scientist is training a recurrent neural network (RNN) for time series forecasting. The model's training loss is not decreasing, and the gradients are vanishing. Which technique should the scientist apply to address vanishing gradients?

A.Apply gradient clipping.

B.Replace the RNN cells with LSTM or GRU units.

C.Add batch normalization layers.

D.Increase the learning rate.

AnswerB

LSTM/GRU have gating mechanisms that help preserve gradients over long sequences.

Why this answer

LSTM and GRU units incorporate gating mechanisms (input, forget, output gates) that regulate the flow of gradients, effectively mitigating the vanishing gradient problem. Option A is wrong because gradient clipping is used to prevent exploding gradients, not vanishing gradients. Option C is wrong because batch normalization helps stabilize training by reducing internal covariate shift, but it does not specifically address vanishing gradients.

Option D is wrong because increasing the learning rate may cause the training to diverge or become unstable, and it does not solve the vanishing gradient issue.

Full explanation →

926

MCQhard

A company is using Amazon SageMaker to train a model on data stored in S3. The training job needs to access data from an S3 bucket in a different AWS account. The data owner has granted cross-account access via a bucket policy. However, the training job fails with an AccessDenied error. What is the MOST likely cause?

A.The data is encrypted with SSE-KMS and the SageMaker role lacks KMS permissions.

B.The SageMaker execution role does not have the necessary permissions to access the S3 bucket.

C.The S3 bucket is not configured with public access.

D.The S3 bucket is in a different region and requires a VPC endpoint.

AnswerB

The SageMaker execution role must have an IAM policy that grants access to the S3 bucket. Without it, even with a bucket policy, the training job will fail.

Why this answer

Even with a bucket policy granting cross-account access, the SageMaker execution role must have an IAM policy that allows s3:GetObject (and any other required actions) on the S3 bucket. Without these permissions, the training job will fail with AccessDenied. Option A is incorrect because SSE-KMS encryption would require KMS permissions, but the issue is specifically about access permissions, not encryption.

Option C is incorrect because the data does not need to be public; cross-account access via bucket policy is sufficient. Option D is incorrect because cross-account access does not require a VPC endpoint.

Full explanation →

927

Multi-Selectmedium

Which THREE techniques can help reduce overfitting in a neural network? (Choose 3)

Select 3 answers

A.Dropout

B.Increasing the number of layers

C.Using a larger learning rate

D.Early stopping

E.L2 regularization

AnswersA, D, E

Dropout randomly drops neurons, reducing overfitting.

Why this answer

Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the network from relying too heavily on any single neuron and forces it to learn more robust features. This reduces overfitting by introducing noise that improves generalization.

Exam trap

The MLS-C01 exam often tests the misconception that increasing model capacity (e.g., more layers) or adjusting the learning rate can reduce overfitting, when in fact these techniques either exacerbate overfitting or address convergence issues rather than regularization.

Full explanation →

928

MCQmedium

A team has trained a deep learning model on Amazon SageMaker using a custom Docker container. They want to deploy the model to a SageMaker endpoint for real-time inference. Which format should the model artifacts be in?

A.A single .tar.gz file containing the model files.

B.A folder on S3 with the model files.

C.No format requirement; any file works.

D.A .zip file containing the model files.

AnswerA

SageMaker requires model artifacts as a tarball.

Why this answer

Amazon SageMaker requires model artifacts to be packaged as a single .tar.gz file when using a custom Docker container for real-time inference. This compressed archive must contain the model files (e.g., model.pth, model.h5) and any necessary inference code, as SageMaker extracts the archive to the /opt/ml/model directory during deployment. The .tar.gz format ensures consistent extraction and compatibility with SageMaker's inference pipeline.

Exam trap

The trap here is that candidates may assume SageMaker accepts common archive formats like .zip or any file structure, but the exam specifically tests the requirement for a single .tar.gz file as the only supported format for model artifacts in custom container deployments.

How to eliminate wrong answers

Option B is wrong because a folder on S3 with model files is not a valid format; SageMaker expects a single compressed archive, not a directory structure, to ensure atomic deployment and consistent extraction. Option C is wrong because SageMaker does impose a format requirement: the model artifacts must be a .tar.gz file; arbitrary files would break the deployment process. Option D is wrong because a .zip file is not supported by SageMaker for model artifacts; only .tar.gz is accepted, as SageMaker's extraction logic is built around tar-based archives.

Full explanation →

929

MCQeasy

A machine learning team is using Amazon SageMaker to tune hyperparameters for a neural network. They have defined a hyperparameter tuning job with a random search strategy. The training time per job is very long. Which strategy can reduce the total tuning time?

A.Enable early stopping to terminate poorly performing jobs.

B.Use a larger instance type for each training job.

C.Switch to Bayesian optimization.

D.Increase the number of training jobs.

AnswerA

Early stops poor trials early, saving compute time.

Why this answer

Enabling early stopping allows SageMaker to terminate training jobs that are unlikely to produce better results based on the objective metric, which directly reduces total tuning time by freeing up compute resources for more promising hyperparameter combinations. This is especially effective with random search, where many trials may converge slowly or plateau.

Exam trap

The trap here is that candidates often confuse early stopping with reducing training time per job (Option B) or assume Bayesian optimization always converges faster, but in practice, early stopping directly cuts wasted time on poor trials, which is the most effective strategy when individual training jobs are very long.

How to eliminate wrong answers

Option B is wrong because using a larger instance type speeds up individual training jobs but does not reduce the number of jobs or the time wasted on poor performers, and may increase cost without proportional benefit. Option C is wrong because switching to Bayesian optimization typically requires more initial jobs to build a surrogate model and can be less effective with very long training times per job, as it still waits for each job to complete before suggesting the next. Option D is wrong because increasing the number of training jobs would increase total tuning time, not reduce it, since each job still takes a long time to run.

Full explanation →

930

MCQmedium

A data scientist is using Amazon SageMaker to train a model. The training dataset contains missing values in several features. The data scientist wants to impute missing values using the median of each feature. Which approach is most appropriate?

A.Drop all rows that contain missing values

B.Compute the median on the entire dataset, then split into training and test sets

C.Impute missing values with zero for all features before splitting

D.Compute the median of each feature on the training set only, then impute both training and test sets using that median

AnswerD

Computing the median on the training set only avoids data leakage, and applying that median to both training and test sets ensures consistent imputation without using test set information.

Why this answer

Computing the median on the training set only avoids data leakage, and applying that median to both training and test sets ensures consistent imputation without using test set information. Option A is incorrect because dropping rows with missing values discards potentially useful data and is not imputation. Option B is incorrect because computing the median on the entire dataset before splitting introduces data leakage, as the test set influences the imputation values.

Option C is incorrect because imputing with zero is arbitrary and does not use the median; also, doing it before splitting would use the entire dataset, causing leakage.

Full explanation →

931

MCQmedium

A company uses AWS Glue ETL jobs to transform data from Amazon RDS for MySQL to Amazon S3. The transformation includes aggregations and joins. The job runs daily and processes approximately 100 GB of data. Recently, the job started failing with memory errors on the worker nodes. Which approach would MOST effectively resolve the issue without changing the logic?

A.Switch from a Spark ETL job to a Python shell job

B.Decrease the number of workers to reduce overhead

C.Change the worker type from G.2X to G.1X to increase memory per worker

D.Increase the number of workers in the job configuration

AnswerD

More workers distribute the data processing, reducing memory per node.

Why this answer

Increasing the number of workers distributes the memory load across more nodes, which directly addresses memory errors in a Spark ETL job without altering the transformation logic. AWS Glue Spark jobs process data in memory across workers, and insufficient total memory causes out-of-memory errors when handling 100 GB of data with aggregations and joins.

Exam trap

The trap here is that candidates might confuse worker type (memory per worker) with number of workers (total cluster memory), incorrectly assuming a larger worker type always helps, when in fact increasing the number of workers is the direct fix for memory errors in distributed Spark jobs.

How to eliminate wrong answers

Option A is wrong because a Python shell job runs on a single node with limited memory and cannot handle 100 GB of data or Spark-based aggregations and joins. Option B is wrong because decreasing the number of workers reduces total cluster memory, worsening the memory errors. Option C is wrong because changing from G.2X (16 GB memory per worker) to G.1X (8 GB memory per worker) decreases memory per worker, which would exacerbate memory issues rather than resolve them.

Full explanation →

932

MCQhard

A company uses SageMaker to train a model that processes sensitive customer data. Due to compliance, the training data must be encrypted at rest and in transit, and the model artifacts must be stored in a secured S3 bucket with encryption. Which combination of actions is REQUIRED?

A.Store data in an S3 bucket with AWS CloudHSM integration

B.Use an S3 bucket with SSE-S3 and enable SageMaker Internet-facing mode

C.Use an S3 bucket with default encryption (SSE-S3) and enable SSL for all connections

D.Enable AWS KMS encryption for the SageMaker notebook and training job, and use an S3 bucket with default encryption using AWS KMS

AnswerD

KMS encryption ensures encryption at rest and in transit for SageMaker and S3.

Why this answer

It ensures end-to-end encryption: AWS KMS encryption for the SageMaker notebook and training job encrypts data in transit and at rest within the SageMaker environment, while an S3 bucket with default encryption using AWS KMS encrypts the training data and model artifacts at rest. This combination meets compliance requirements for encryption at rest and in transit, as KMS provides envelope encryption with customer-managed keys, and SageMaker automatically uses TLS for data in transit when KMS is enabled.

Exam trap

The trap here is that candidates often assume SSE-S3 alone is sufficient for compliance, but it does not cover encryption in transit or SageMaker-specific encryption, and they overlook the requirement for KMS to encrypt the SageMaker environment itself.

How to eliminate wrong answers

Option A is wrong because AWS CloudHSM integration is not a direct encryption method for S3 buckets; it provides hardware security modules for key storage but does not inherently encrypt data at rest in S3 or in transit, and it is not a required action for SageMaker encryption. Option B is wrong because SSE-S3 encrypts data at rest but does not address encryption in transit, and enabling SageMaker Internet-facing mode exposes the endpoint to the internet without ensuring SSL/TLS for all connections, violating compliance. Option C is wrong because SSE-S3 encrypts data at rest but does not provide encryption for SageMaker notebook instances or training jobs; enabling SSL for all connections is a best practice but not a specific SageMaker configuration, and it does not cover encryption of model artifacts in transit between SageMaker and S3 without KMS integration.

Full explanation →

933

MCQeasy

A data engineer needs to transform raw clickstream data (JSON files) stored in S3 into a partitioned Parquet dataset for querying with Athena. The transformation includes cleaning, deduplication, and enrichment. The pipeline should run daily. Which solution is MOST cost-effective and requires the least operational overhead?

A.Launch an Amazon EMR cluster with Spark, transform the data, and terminate the cluster after completion.

B.Use an AWS Glue ETL job with a schedule trigger to perform the transformation and write to S3.

C.Use AWS Lambda functions triggered by S3 events to transform each file incrementally.

D.Use Amazon Athena to run CTAS queries to convert and partition the data daily.

AnswerB

Glue ETL is serverless, can handle complex transformations, and scheduling is built-in.

Why this answer

AWS Glue ETL jobs are serverless, require no cluster management, and can be easily scheduled for daily runs. Glue also integrates with the Data Catalog for partitioning. Option A (Amazon EMR) is not the most cost-effective or least operational overhead because it requires managing a cluster, even if it can be terminated after completion.

Option C (AWS Lambda) is not suitable for large-scale clickstream data due to execution time limits and lack of built-in support for complex transformations like deduplication. Option D (Amazon Athena CTAS) is not appropriate because Athena is primarily a query engine, not a transformation tool; CTAS queries are good for converting data formats but lack the flexibility for cleaning and enrichment.

Full explanation →

934

MCQhard

A company uses SageMaker Ground Truth to label images for object detection. After labeling, they notice that the bounding boxes are often misaligned with the objects. Which action should they take to improve label quality?

A.Use a pre-built annotation tool that enforces bounding box alignment

B.Use automated labeling with a pre-trained model

C.Increase the number of workers per task

D.Adjust the confidence threshold for the model

AnswerA

Tool constraints improve consistency.

Why this answer

SageMaker Ground Truth offers pre-built annotation tools, such as the bounding box tool, which includes features like snap-to-grid or edge alignment that enforce precise bounding box placement. Using this tool directly improves label quality by reducing human error in manual drawing, ensuring boxes tightly fit objects without manual guesswork.

Exam trap

The trap here is that candidates often confuse label quality improvement strategies (e.g., using consensus voting or automated labeling) with the specific need to enforce geometric precision in bounding box annotations, leading them to select options that address general accuracy rather than alignment accuracy.

How to eliminate wrong answers

Option B is wrong because automated labeling with a pre-trained model would generate bounding boxes based on the model's predictions, which may also be misaligned if the model is not fine-tuned or if the objects differ from the training data; it does not address the root cause of misalignment in human-drawn boxes. Option C is wrong because increasing the number of workers per task (using a consensus-based approach) can reduce random errors but does not fix systematic misalignment caused by imprecise manual drawing; workers may still produce misaligned boxes if the tool lacks alignment enforcement. Option D is wrong because adjusting the confidence threshold for the model is relevant only when using automated labeling or model inference, not for improving the quality of human-annotated bounding boxes in Ground Truth.

Full explanation →

935

MCQeasy

A machine learning team is preparing a dataset for model training. The data is stored in an Amazon S3 bucket with objects that are each approximately 100 MB in size. The team wants to use Amazon SageMaker for training. To optimize training performance, which data format and storage configuration should be used?

A.Store data as RecordIO-Protobuf files and use SageMaker File input mode

B.Store data as RecordIO-Protobuf files and use SageMaker Pipe input mode

C.Store data as CSV files and use SageMaker Pipe input mode

D.Store data as CSV files and use SageMaker File input mode

AnswerB

Pipe mode streams data directly from S3, and RecordIO-Protobuf provides efficient binary format.

Why this answer

RecordIO-Protobuf is the optimal format for SageMaker because it stores data in a binary, sharded structure that allows for efficient random access and parallel I/O. Pipe input mode streams data directly from S3 to the training algorithm, eliminating disk writes and reducing startup latency, which is critical for large datasets with 100 MB objects.

Exam trap

The trap here is that candidates often assume 'File input mode' is always faster because it loads data locally, but they overlook that Pipe mode's streaming avoids disk I/O bottlenecks and is specifically optimized for binary formats like RecordIO-Protobuf.

How to eliminate wrong answers

Option A is wrong because File input mode downloads the entire dataset to the training instance's local disk before training begins, which adds significant startup time and I/O overhead for large objects. Option C is wrong because CSV files are text-based and require parsing line by line, which is slower than binary formats and does not support the sharded, parallel access that Pipe mode leverages. Option D is wrong because CSV files with File input mode combine the worst of both: text parsing overhead and full-disk download latency, making it the least performant choice.

Full explanation →

936

Multi-Selecthard

Which THREE are common techniques for detecting outliers in a univariate dataset? (Select THREE.)

Select 3 answers

A.Cook's distance

B.DBSCAN clustering

C.Z-score

D.Interquartile range (IQR) method

E.Modified Z-score using median absolute deviation (MAD)

AnswersC, D, E

Z-score measures how many standard deviations an observation is from the mean.

Why this answer

Options C, D, and E are correct. Z-score (C) identifies outliers by standard deviations from mean, IQR method (D) uses quartiles to detect outliers beyond 1.5×IQR, and Modified Z-score using MAD (E) is a robust alternative. Cook's distance (A) is a regression diagnostic for influential points, not univariate outlier detection.

DBSCAN (B) is a multivariate clustering algorithm.

Full explanation →

937

Multi-Selecteasy

A data scientist is evaluating a binary classification model. The model's confusion matrix shows: True Positives=80, False Positives=20, True Negatives=900, False Negatives=0. Which THREE metrics can be calculated from this confusion matrix? (Choose three.)

Select 3 answers

A.Precision

B.Recall

C.AUC-ROC

D.Accuracy

E.Root Mean Squared Error (RMSE)

AnswersA, B, D

Precision = TP/(TP+FP).

Why this answer

Precision is calculated as TP/(TP+FP) = 80/(80+20) = 0.80. This metric measures the proportion of positive identifications that were actually correct, which is directly derivable from the confusion matrix values.

Exam trap

The trap here is that candidates often assume AUC-ROC can be derived from a single confusion matrix, but it actually requires the full distribution of predicted probabilities to plot the ROC curve and calculate the area under it.

Full explanation →

938

MCQhard

A company uses Amazon SageMaker to train a model for fraud detection. The training data is highly imbalanced. The data scientist uses SMOTE to oversample the minority class. However, the model still has poor recall on the minority class. Which additional technique should the data scientist consider?

A.One-vs-rest encoding

B.Use class weights in the loss function

C.L1 regularization

D.Principal component analysis (PCA)

AnswerB

Class weights penalize minority errors more.

Why this answer

SMOTE generates synthetic samples for the minority class, but it does not directly address the model's tendency to prioritize the majority class during training. By assigning higher class weights to the minority class in the loss function, the model penalizes misclassifications of minority samples more heavily, which directly improves recall on that class. This technique is especially effective when combined with oversampling, as it forces the optimizer to focus on the underrepresented class during gradient updates.

Exam trap

The trap here is that candidates assume SMOTE alone is sufficient to fix imbalance, but the exam tests the understanding that oversampling must be paired with a cost-sensitive learning technique like class weighting to directly influence the model's optimization objective.

How to eliminate wrong answers

Option A is wrong because one-vs-rest encoding is a strategy for multi-class classification, not for handling class imbalance in binary fraud detection; it would decompose the problem into multiple binary classifiers without addressing the imbalance. Option C is wrong because L1 regularization adds a penalty on the absolute magnitude of weights to promote sparsity, which can help with overfitting but does not directly improve recall on the minority class. Option D is wrong because PCA is an unsupervised dimensionality reduction technique that may discard variance useful for distinguishing the minority class, potentially worsening recall rather than improving it.

Full explanation →

939

Multi-Selecteasy

A data scientist is working with a dataset that contains both numerical and categorical features. The target variable is continuous. Which TWO EDA techniques should the scientist use to understand relationships between features and the target?

Select 2 answers

A.Generate a confusion matrix for the target variable.

B.Compute the silhouette score for each feature.

C.Create scatter plots of numerical features against the target variable.

D.Use box plots to compare target distribution across categorical feature categories.

E.Plot a histogram of the target variable.

AnswersC, D

Correct. Scatter plots reveal relationships between numerical features and a continuous target.

Why this answer

Scatter plots (C) are appropriate for visualizing relationships between numerical features and a continuous target. Box plots (D) are appropriate for comparing target distribution across categories of categorical features. Option A (confusion matrix) is used for classification, not regression.

Option B (silhouette score) is for evaluating clustering. Option E (histogram of target) is univariate and does not show feature-target relationships.

Full explanation →

940

MCQhard

Refer to the exhibit. A SageMaker endpoint is returning 5xx errors. The logs show the above error. Which change will most likely resolve the issue?

A.Reduce the batch size in the inference script

B.Enable Auto Scaling on the endpoint

C.Compress the model artifact

D.Use a larger instance type with more memory

AnswerD

More memory solves OutOfMemoryError.

Why this answer

5xx errors from a SageMaker endpoint typically indicate that the inference container is running out of memory (OOM) or crashing under load. The error log suggests the model or inference process requires more memory than the current instance type provides. Upgrading to a larger instance type with more memory directly addresses the resource exhaustion, allowing the model to load and inference to complete without failure.

Exam trap

The MLS-C01 exam often tests the misconception that Auto Scaling or batch size adjustments can fix resource exhaustion errors, when in fact only vertical scaling (larger instance) addresses the root cause of insufficient memory per instance.

How to eliminate wrong answers

Option A is wrong because reducing the batch size might reduce memory usage per request, but if the model itself is too large for the instance (e.g., cannot even load), batch size changes won't fix the underlying memory exhaustion. Option B is wrong because Auto Scaling adds more instances to handle increased traffic, but it does not increase the memory per instance; if each instance is already OOM, adding more instances will just replicate the failure. Option C is wrong because compressing the model artifact only reduces storage and download time, not the runtime memory footprint; the model must still be decompressed and loaded into memory, so the OOM error persists.

Full explanation →

941

MCQeasy

Refer to the exhibit. A data scientist runs the AWS CLI command to create a SageMaker training job. The training job fails because the input data is not accessible. Which step should the data scientist take to fix the issue?

A.Attach an IAM policy to SageMakerRole that grants s3:GetObject on the bucket

B.Add a VpcConfig to the training job

C.Modify the bucket policy to allow s3:GetObject for any principal

D.Increase VolumeSizeInGB to 50

AnswerA

The role needs explicit S3 read permissions.

Why this answer

The training job fails because the SageMaker execution role (SageMakerRole) lacks the necessary IAM permissions to read the input data from the S3 bucket. By attaching an IAM policy that grants s3:GetObject on the specific bucket, the role gains the required access. SageMaker uses the execution role to access S3 data, so the role's permissions must explicitly allow the GetObject action.

Exam trap

The trap here is that candidates may confuse IAM role permissions with bucket policies or network configurations, thinking that VPC settings or storage size can fix an S3 access denied error, when the root cause is always a missing IAM action on the execution role.

How to eliminate wrong answers

Option B is wrong because adding a VpcConfig configures the training job to run within a VPC, which addresses network isolation or private subnet access, but does not resolve missing S3 read permissions. Option C is wrong because modifying the bucket policy to allow s3:GetObject for any principal is a security risk and unnecessary; the correct approach is to grant permissions to the specific IAM role used by SageMaker. Option D is wrong because increasing VolumeSizeInGB increases the size of the local training instance storage, which does not affect the ability to read input data from S3.

Full explanation →

942

Multi-Selectmedium

A data scientist is using SageMaker to train a model and wants to track experiments, including hyperparameters and metrics. Which TWO actions should the scientist take to set up experiment tracking? (Choose TWO.)

Select 2 answers

A.Use the SageMaker Experiments Python SDK to create an experiment and log runs.

B.Enable SageMaker Model Monitor to track training metrics.

C.Configure CloudWatch Logs to store experiment data.

D.Create a trial component in the experiment to log hyperparameters and metrics.

E.Enable SageMaker Studio to automatically capture experiments.

AnswersA, D

Directly supports experiment tracking.

Why this answer

The SageMaker Experiments Python SDK provides the primary interface for creating and managing experiments, allowing the data scientist to log runs, hyperparameters, and metrics in a structured way. This SDK directly integrates with SageMaker training jobs and notebook executions to capture experiment metadata.

Exam trap

The MLS-C01 exam often tests the distinction between monitoring (Model Monitor) and experiment tracking (Experiments SDK), and the trap here is that candidates confuse CloudWatch Logs or Model Monitor as valid tools for structured experiment metadata capture when they are not designed for that purpose.

Full explanation →

943

Multi-Selectmedium

Which TWO statements about handling missing data during EDA are correct? (Select TWO.)

Select 2 answers

A.Dropping columns with >50% missing values is always recommended.

B.Mean imputation preserves the variance of the original distribution.

C.If data are missing completely at random (MCAR), listwise deletion yields unbiased estimates.

D.Multiple imputation (MICE) is always the safest method regardless of missing data mechanism.

E.Imputing with the median is more robust to outliers than imputing with the mean.

AnswersC, E

Under MCAR, missingness is independent of data, so deletion is unbiased.

Why this answer

Options C and E are correct. Option C is correct because when data are Missing Completely at Random (MCAR), the missingness is independent of both observed and unobserved data, so listwise deletion (removing rows with missing values) does not introduce bias; the remaining sample is still a random subsample. Option E is correct because the median is not influenced by extreme values, making it a more robust imputation method compared to the mean, which can be skewed by outliers.

Option A is incorrect because dropping columns with >50% missing values is not always recommended; it depends on the importance of the variable and the analysis goals. Option B is incorrect because mean imputation reduces the variance of the imputed variable, as it forces imputed values to the center. Option D is incorrect because Multiple Imputation by Chained Equations (MICE) is not always the safest; it assumes data are Missing at Random (MAR) and can be complex or inappropriate for other missingness mechanisms.

Full explanation →

944

MCQeasy

A data scientist is using Amazon SageMaker to train a model using a built-in algorithm. The training job is taking a long time, and the data scientist wants to improve performance by using a larger instance type with more vCPUs. The training job is currently using an ml.m5.large instance. The data scientist changes the instance type to ml.m5.4xlarge and resubmits the training job. However, the training time does not decrease significantly. What is the MOST likely reason?

A.The algorithm is single-threaded and cannot use multiple vCPUs.

B.The built-in algorithm is not designed to scale with additional vCPUs.

C.The training job is I/O bound, and increasing vCPUs does not help.

D.The training dataset is too small to benefit from more vCPUs.

AnswerB

The built-in algorithm may not be able to utilize additional vCPUs effectively if it is not parallelized. This is the most likely reason.

Why this answer

The most likely reason the training time did not decrease significantly is that the built-in algorithm may not be designed to scale effectively with additional vCPUs. Option B correctly identifies this. Option A is less likely because, while single-threaded algorithms cannot use multiple vCPUs, many SageMaker built-in algorithms are parallelized but still have limited scalability due to overhead.

Option C is possible but less common; the question's most likely reason is inherent scalability limitations. Option D is incorrect because if the dataset were too small, training time would already be low, and increasing vCPUs could still help, but the lack of improvement here points to scalability issues, not dataset size.

Full explanation →

945

MCQhard

A data scientist is performing EDA on a high-dimensional dataset with 500 features. They want to visualize the data in 2D to check for clusters. They first apply PCA and get a 2D projection that shows no clear structure. They suspect that the data lies on a non-linear manifold. Which of the following techniques should they try next?

A.Use Independent Component Analysis (ICA).

B.Use Linear Discriminant Analysis (LDA).

C.Apply PCA again with more components.

D.Use t-distributed Stochastic Neighbor Embedding (t-SNE).

AnswerD

t-SNE is a non-linear technique that preserves local structure and is widely used for visualizing high-dimensional data in 2D or 3D, making it ideal for detecting clusters in non-linear manifolds.

Why this answer

T-SNE is a non-linear dimensionality reduction technique specifically designed for visualization. Option A (ICA) is a linear method for separating independent components, not for capturing non-linear manifolds. Option B (LDA) is a supervised linear method that maximizes class separation, unsuitable for unsupervised non-linear structure.

Option C (PCA with more components) remains linear, so adding components does not help with non-linear manifolds.

Full explanation →

946

MCQmedium

A company uses AWS Glue to run ETL jobs that process data from Amazon RDS for PostgreSQL and load it into Amazon Redshift. The Glue job runs nightly and takes 6 hours to complete. The Redshift cluster is a single dc2.large node. The team needs to reduce the load time to under 3 hours. The data volume is 200 GB per night. The team is considering using Amazon Redshift Spectrum to query data directly from S3 instead of loading it. However, the data transformation logic is complex and requires multiple joins and aggregations that are currently performed in Glue. Which approach should the team recommend to meet the time requirement?

A.Use Redshift Spectrum to create external tables and run the transformations directly in Redshift, bypassing the Glue job.

B.Increase the Redshift cluster to a multi-node cluster with dc2.8xlarge nodes to improve COPY and query performance.

C.Split the Glue job into multiple parallel jobs that each load a portion of the data into separate Redshift tables, then use UNION ALL views.

D.Stage the data in S3 in Parquet format and use a COPY command with the PARQUET option to load data faster.

AnswerB

More nodes increase parallelism for loading and any post-load transformations.

Why this answer

Increasing the Redshift cluster to a multi-node configuration with dc2.8xlarge nodes significantly improves COPY performance by parallelizing data loading across multiple slices. The current single dc2.large node may be the bottleneck for the data loading portion of the Glue job. Faster loading can reduce the total 6-hour runtime to under 3 hours if loading is the dominant factor.

Note: the complex transformations remain in AWS Glue, not on Redshift. Option A is incorrect because Redshift Spectrum allows querying external tables without loading but does not accelerate the complex ETL transformations. Option C splits the load but does not address the single-node ingestion bottleneck and adds complexity.

Option D improves load speed with Parquet but the cluster's ingestion capacity is still the limiting factor.

Full explanation →

947

MCQeasy

A data scientist is building a text classification model. The dataset contains 10,000 documents, each labeled with one of 5 categories. Which algorithm is most suitable for this task?

A.Principal Component Analysis (PCA)

B.Naive Bayes

C.Linear regression

D.k-means clustering

AnswerB

Naive Bayes is effective for text classification and small datasets.

Why this answer

Naive Bayes is highly suitable for text classification because it models the probability of each category given the document's word features using Bayes' theorem with a strong independence assumption. It performs well on high-dimensional sparse data like bag-of-words or TF-IDF representations, and it is particularly effective when the number of documents (10,000) is moderate relative to the vocabulary size, as it requires relatively little training data to estimate parameters.

Exam trap

The MLS-C01 exam often tests the distinction between supervised and unsupervised learning, leading candidates to mistakenly choose k-means clustering (an unsupervised method) for a labeled classification task, or to confuse PCA with a classification algorithm because it is used for feature reduction before modeling.

How to eliminate wrong answers

Option A is wrong because Principal Component Analysis (PCA) is an unsupervised dimensionality reduction technique that finds orthogonal components maximizing variance; it does not perform classification and ignores the category labels entirely. Option C is wrong because linear regression predicts a continuous numeric output, not a discrete categorical label; applying it to multiclass classification would require inappropriate thresholding and violates the assumption of normally distributed errors. Option D is wrong because k-means clustering is an unsupervised algorithm that partitions data into clusters based on distance, without using label information; it cannot assign documents to predefined categories and requires post-hoc mapping of clusters to labels.

Full explanation →

948

MCQhard

A data scientist is using Amazon SageMaker Autopilot to automatically build a model. The dataset contains a mix of numerical and categorical features. After the experiment completes, Autopilot provides several candidate pipelines. Which pipeline is MOST likely to be ranked highest by Autopilot?

A.The pipeline with the lowest validation loss

B.The pipeline with the simplest model (e.g., linear classifier)

C.The pipeline with the fastest training time

D.The pipeline with the lowest training loss

AnswerA

Autopilot ranks candidates by validation performance.

Why this answer

Amazon SageMaker Autopilot ranks candidate pipelines by their objective metric on the validation dataset, which is typically the validation loss (e.g., cross-entropy for classification or mean squared error for regression). The pipeline with the lowest validation loss generalizes best to unseen data, making it the highest-ranked candidate. Autopilot uses hold-out validation or cross-validation to compute this metric, ensuring the ranking reflects out-of-sample performance rather than overfitting to the training set.

Exam trap

The trap here is that candidates often confuse training loss with validation loss, mistakenly thinking that a lower training loss indicates a better model, but Autopilot explicitly ranks by validation performance to prevent overfitting.

How to eliminate wrong answers

Option B is wrong because Autopilot does not prioritize model simplicity; it optimizes for predictive performance, and a more complex model (e.g., ensemble or gradient-boosted tree) often achieves lower validation loss than a linear classifier. Option C is wrong because training time is not a ranking criterion; Autopilot focuses on accuracy, not computational speed, and a faster pipeline may sacrifice performance. Option D is wrong because training loss is an in-sample metric that can be misleadingly low due to overfitting; Autopilot uses validation loss to avoid this bias and ensure generalization.

Full explanation →

949

MCQmedium

A data scientist is exploring a dataset and finds that the variance of a feature is 0. What should be done with this feature?

A.Remove the feature from the dataset

B.Create interaction terms with other features

C.Apply Min-Max scaling to normalize the feature

D.Impute missing values using the mean

AnswerA

Constant feature provides no predictive power.

Why this answer

When a feature has zero variance, it means all values are identical (constant). Such a feature provides no discriminative information for machine learning models and can be safely removed. Removing it reduces dimensionality without losing any information.

Option A is correct. Option B (creating interaction terms) is incorrect because interacting a constant with any feature yields a constant. Option C (min-max scaling) does not change the fact that the feature is constant; it remains constant after scaling.

Option D (imputing missing values) is irrelevant since zero variance does not imply missing values.

Full explanation →

950

MCQmedium

A company has deployed a model on SageMaker for real-time inference. The endpoint is experiencing high latency during traffic spikes. Which action should the company take to reduce latency?

A.Use a larger instance type for the endpoint

B.Attach SageMaker Elastic Inference to the endpoint

C.Enable SageMaker endpoint auto-scaling

D.Use SageMaker Neo to compile the model

E.Switch to SageMaker batch transform

AnswerC

Auto-scaling adds instances during spikes, reducing latency.

Why this answer

Enabling SageMaker endpoint auto-scaling allows the endpoint to dynamically adjust the number of instances based on incoming traffic, which directly reduces latency during spikes by ensuring sufficient compute capacity is available. Auto-scaling uses CloudWatch metrics (e.g., InvocationsPerInstance or latency) to trigger scale-out events, preventing queue buildup and response time degradation.

Exam trap

AWS often tests the misconception that improving per-request performance (e.g., via larger instances, Elastic Inference, or Neo compilation) is the solution for handling traffic spikes, when the actual need is horizontal scaling to increase request throughput capacity.

How to eliminate wrong answers

Option A is wrong because using a larger instance type increases per-instance compute capacity but does not address the root cause of traffic spikes—it only shifts the bottleneck and may still result in high latency if the single instance is overwhelmed; it also increases cost without elasticity. Option B is wrong because SageMaker Elastic Inference accelerates model inference by attaching a GPU accelerator, but it does not help with traffic spikes—it only reduces per-request latency for a fixed number of requests, not handle increased request volume. Option D is wrong because SageMaker Neo compiles models to optimize inference performance on specific hardware, which can reduce per-request latency but does not scale the endpoint to handle traffic spikes.

Option E is wrong because switching to batch transform is for offline, asynchronous processing of large datasets and is not suitable for real-time inference; it would increase latency for real-time use cases.

Full explanation →

951

MCQmedium

A machine learning engineer is performing exploratory data analysis on a dataset containing customer transactions. They notice that the target variable is highly imbalanced: 99% of samples belong to class 0 and 1% to class 1. Which technique should they use to address this imbalance before training a classification model?

A.Train the model on the raw data without any modification.

B.Apply SMOTE to generate synthetic samples for the minority class.

C.Use accuracy as the evaluation metric and train on the raw data.

D.Under-sample the majority class to match the minority class size.

AnswerB

SMOTE creates synthetic minority samples, helping balance the dataset.

Why this answer

SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, which helps balance the dataset and improves model performance on the minority class without losing information from the majority class. Option A is wrong: training on raw data without addressing imbalance will cause the model to be biased toward the majority class and perform poorly on the minority class. Option C is wrong: accuracy is not a suitable evaluation metric for imbalanced datasets because a model that always predicts the majority class will achieve 99% accuracy, masking poor performance on the minority class; instead, metrics like precision, recall, F1-score, or AUC should be used.

Option D is wrong: under-sampling the majority class to match the minority class size discards a large amount of data, potentially losing valuable patterns and reducing model performance.

Full explanation →

952

Multi-Selecteasy

A company wants to monitor SageMaker endpoints for data drift. Which TWO services can be used together to detect and alert on drift?

Select 2 answers

A.SageMaker Data Wrangler

B.SageMaker Model Monitor

C.AWS CodePipeline

D.Amazon CloudWatch Alarms

E.Amazon CloudWatch Logs

AnswersB, D

Model Monitor detects drift in real-time.

Why this answer

SageMaker Model Monitor (option B) continuously monitors models for data and quality drift. Amazon CloudWatch Alarms (option D) can be set up on Model Monitor's metrics to trigger alerts when drift is detected. SageMaker Data Wrangler (option A) is for data preparation, not monitoring.

AWS CodePipeline (option C) is for CI/CD. Amazon CloudWatch Logs (option E) is for log storage and analysis, not for alerting on drift.

Full explanation →

953

MCQhard

A machine learning engineer is tuning hyperparameters for a gradient boosting model using Amazon SageMaker Automatic Model Tuning. The objective metric is validation accuracy. After several tuning jobs, the best accuracy achieved is 0.85, but the engineer suspects the model is overfitting. Which hyperparameter adjustment is most likely to reduce overfitting?

A.Increase the regularization parameter (e.g., lambda or alpha)

B.Increase the maximum depth of trees

C.Increase the subsample ratio

D.Increase the learning rate

AnswerA

Regularization penalizes large weights, reducing overfitting.

Why this answer

Increasing the regularization parameter (e.g., lambda or alpha in XGBoost) penalizes model complexity and helps reduce overfitting, making option A correct. Option B is incorrect because increasing maximum depth increases model complexity, leading to overfitting. Option C is incorrect because increasing subsample ratio (using more data per tree) can increase overfitting, while decreasing it often reduces overfitting.

Option D is incorrect because increasing learning rate makes the model learn faster, which can lead to overfitting.

Full explanation →

954

Multi-Selecthard

A company is using AWS Glue to catalog data stored in Amazon S3. The data is partitioned by year, month, day, and hour. The company runs hourly ETL jobs that add new partitions. The Glue crawler is scheduled to run every hour to update the Data Catalog. However, the crawler is taking longer than expected and is not completing before the next crawler run starts. Which action could the company take to resolve this issue?

Select 1 answer

A.Increase the throughput of the crawler by configuring the 'Schema updates' option

B.Enable partition indexing on the table to speed up the crawler

C.Decrease the crawler schedule frequency to every 2 hours to avoid overlapping runs

D.Use multiple crawlers, each configured to crawl a different path (e.g., one for year=2023, one for year=2024)

E.Increase the number of crawler instances by configuring the 'Crawler queue' to process multiple partitions in parallel

AnswersD

Correct. Using multiple crawlers configured to crawl different paths (e.g., by year) parallelizes the crawling work, reducing overall time and preventing overlaps.

Why this answer

Using multiple crawlers to crawl different paths allows parallel processing of partitions, reducing crawler time. Option A is incorrect because the 'Schema updates' option does not increase throughput. Option B is incorrect because partition indexing speeds up queries, not the crawler itself.

Option C is incorrect because reducing frequency does not speed up the crawler. Option E is incorrect because AWS Glue does not have a 'Crawler queue' feature; to increase parallelism, you would increase DPUs.

Full explanation →

955

Multi-Selectmedium

Which TWO of the following are valid techniques for handling missing values in a dataset for machine learning?

Select 2 answers

A.Replace missing values with the maximum value of the feature

B.Remove rows with missing values

C.Replace missing values with random noise

D.Convert missing values to the string 'missing'

E.Replace missing values with the mean of the feature

AnswersB, E

Removing rows with missing values is a valid technique, especially when the missing data is few and random.

Why this answer

The valid techniques for handling missing values are removing rows with missing values and replacing missing values with the mean of the feature. Options B and E are correct. Option A (maximum value) introduces bias, option C (random noise) distorts distribution, and option D (string conversion) is inappropriate for numerical data.

Full explanation →

956

Multi-Selectmedium

A company is building a data pipeline that uses Amazon Kinesis Data Streams to ingest real-time events. The pipeline then uses AWS Lambda to process the events and store results in Amazon DynamoDB. The company wants to ensure that the Lambda function can process all events without data loss and without duplicating processing. Which TWO configuration steps should the company take?

Select 2 answers

A.Increase the data retention period of the Kinesis stream to 7 days to allow reprocessing

B.Set the Lambda function's batch window to a small value (e.g., 1 second) to reduce processing latency

C.Enable the 'iterator age' metric in Amazon CloudWatch to monitor consumer lag

D.Use a single shard for the Kinesis stream to ensure order and avoid parallel processing issues

E.Configure the Lambda function to disable retries on failure to avoid duplicate processing

AnswersA, E

Correct. Increasing retention allows reprocessing of events, ensuring no data loss.

Why this answer

To ensure all events are processed without data loss and without duplicating processing, the company should increase the Kinesis stream retention to 7 days (option A). This allows reprocessing of events that fail initial processing, thus preventing data loss. The company should also configure the Lambda function to disable retries on failure (option E).

Automatic retries can cause the same batch of events to be processed multiple times, leading to duplication unless the Lambda function is idempotent. By disabling retries, the company can handle failures manually, ensuring exactly-once processing when combined with idempotent reprocessing logic. Option B (reducing batch window) does not prevent duplicates and may increase invocations.

Option C (enabling iterator age metric) is for monitoring, not preventing loss/duplication. Option D (single shard) does not prevent duplication and can cause throughput limitations.

Full explanation →

957

MCQeasy

A data analyst is examining a scatter plot of two variables and notices a strong positive correlation. Which of the following is a valid conclusion?

A.The relationship is linear

B.One variable causes the other

C.The two variables are related, but causation cannot be inferred

D.The relationship can be used to accurately predict one variable from the other

AnswerC

Correlation does not imply causation.

Why this answer

A strong positive correlation indicates that the two variables are related, but correlation alone does not imply causation. Option A is incorrect because correlation does not necessarily imply a linear relationship; it could be non-linear or monotonic. Option B is incorrect because correlation does not imply causation.

Option D is incorrect because correlation does not guarantee accurate prediction; prediction requires a well-fitted model and additional validation.

Full explanation →

958

MCQeasy

Refer to the exhibit. A data scientist creates a SageMaker notebook instance using this Terraform configuration. The notebook fails to start. The logs indicate 'The IAM role does not have the necessary permissions'. Which addition to the IAM role policy is MOST likely needed?

A.cloudwatch:PutMetricData

B.s3:GetObject on the notebook bucket

C.sagemaker:CreatePresignedNotebookInstanceUrl

D.sagemaker:CreateTrainingJob

AnswerC

Required for notebook access.

Why this answer

The SageMaker notebook instance requires the `sagemaker:CreatePresignedNotebookInstanceUrl` permission to generate a presigned URL, which is used to access the notebook's Jupyter interface. Without this permission, the notebook fails to start because the IAM role cannot create the necessary URL for the user to connect, as indicated by the 'The IAM role does not have the necessary permissions' log error.

Exam trap

AWS often tests the specific permission required for notebook instance access, and the trap here is that candidates confuse general SageMaker permissions (like training or S3 access) with the precise `CreatePresignedNotebookInstanceUrl` action needed for the notebook to start.

How to eliminate wrong answers

Option A is wrong because `cloudwatch:PutMetricData` is used for publishing custom metrics to CloudWatch, which is not required for starting a SageMaker notebook instance; the notebook startup process does not depend on CloudWatch permissions. Option B is wrong because `s3:GetObject` on the notebook bucket is typically needed for accessing data or artifacts, but the notebook instance itself does not require S3 read access to start; the startup failure is due to missing permissions for generating the presigned URL, not S3 access. Option D is wrong because `sagemaker:CreateTrainingJob` is a permission for launching training jobs, which is unrelated to the notebook instance lifecycle; the notebook startup does not involve creating training jobs.

Full explanation →

959

MCQmedium

An e-commerce company wants to build a recommendation system. They have user-item interaction data (clicks, purchases) and user demographic data. The goal is to recommend items that a user is likely to purchase. Which approach should be used?

A.Linear regression on user and item features.

B.Collaborative filtering using matrix factorization.

C.Factorization Machines using user-item interactions and user features.

D.Content-based filtering using item features.

AnswerC

Handles sparse data and side features effectively.

Why this answer

Factorization Machines are designed for high-dimensional sparse data and can effectively combine user-item interactions with side features like user demographics. Option A is wrong because linear regression is not suitable for implicit feedback or modeling interactions. Option B is wrong because collaborative filtering (e.g., matrix factorization) does not naturally incorporate user demographic features.

Option D is wrong because content-based filtering only uses item features and ignores user-item interaction patterns.

Full explanation →

960

MCQhard

A data engineering team is designing a data lake on Amazon S3. Raw data is ingested in JSON format and must be partitioned by year, month, and day. The team expects high query performance for recent data but infrequent queries for older data. The data is immutable. Which storage tier configuration minimizes costs while meeting performance requirements?

A.Store all data in S3 Standard, then move to S3 Glacier after 30 days using a lifecycle policy

B.Store recent partitions in S3 Standard, older partitions in S3 One Zone-IA

C.Keep all data in S3 Standard because query performance is critical

D.Use S3 Intelligent-Tiering for the entire data lake

AnswerD

Intelligent-Tiering automatically moves data between access tiers based on usage, optimizing cost without retrieval delays.

Why this answer

S3 Intelligent-Tiering automatically moves objects between access tiers (frequent, infrequent, and archive instant access) based on changing access patterns, without performance impact or lifecycle management overhead. This matches the workload: recent data is queried frequently (automatic frequent tier), older data is queried rarely (automatic infrequent/archive instant tiers), and data is immutable, so no write/delete penalties apply. It minimizes cost by charging only for the tiers actually used, while maintaining millisecond latency for all tiers.

Exam trap

The trap here is that candidates assume S3 Standard is required for all queryable data, overlooking that S3 Intelligent-Tiering provides the same low-latency performance as S3 Standard for all tiers (including Infrequent Access and Archive Instant Access) while automatically reducing storage costs for infrequently accessed data.

How to eliminate wrong answers

Option A is wrong because moving all data to S3 Glacier after 30 days would cause high retrieval latency (minutes to hours) for any queries on data older than 30 days, violating the requirement for high query performance on recent data (which is fine) but also failing to provide acceptable performance for the infrequent queries on older data. Option B is wrong because S3 One Zone-IA does not provide the same durability (99.999999999% vs 99.99%) and availability as S3 Standard, and it is not designed for data that may be accessed infrequently but still requires low-latency retrieval; also, manually managing partitions across tiers is error-prone and does not adapt to changing access patterns. Option C is wrong because storing all data in S3 Standard incurs unnecessary costs for older data that is queried infrequently, as S3 Standard charges a higher per-GB storage price than infrequent access tiers, and the requirement explicitly asks to minimize costs.

Full explanation →

961

Multi-Selecteasy

A data scientist is performing feature engineering for a machine learning model. The dataset contains categorical features with high cardinality. Which THREE techniques are appropriate for encoding high-cardinality categorical features?

Select 3 answers

A.Target encoding

B.Binary encoding

C.Label encoding

D.Count encoding

E.One-hot encoding with pruning of rare categories

AnswersA, D, E

Replaces category with target mean.

Why this answer

Target encoding (A) replaces each category with the mean of the target variable, which captures predictive signal and is efficient for high cardinality. Count encoding (D) uses the frequency of each category, providing a simple numeric representation. One-hot encoding with pruning of rare categories (E) reduces dimensionality by creating dummy variables only for frequent categories, avoiding the curse of dimensionality.

Binary encoding (B) is not among the most recommended techniques; it converts categories to binary numbers but can be less intuitive and may not handle rare categories well. Label encoding (C) assigns integers arbitrarily, implying ordinal relationships that may mislead the model, making it unsuitable for nominal high-cardinality features.

Full explanation →

962

MCQeasy

A data scientist wants to understand the distribution of a continuous feature before training a model. Which visualization is most appropriate?

A.Scatter plot

B.Box plot

C.Histogram

D.Bar chart

AnswerC

A histogram is the most appropriate visualization for understanding the distribution of a continuous feature because it shows the frequency of data points within bins.

Why this answer

A histogram is the standard tool for showing the distribution of a single continuous variable. Option A is wrong because scatter plots compare two variables. Option B is wrong because box plots show summary statistics, not the full distribution shape.

Option D is wrong because bar charts are for categorical data.

Full explanation →

963

Multi-Selectmedium

A team is building a regression model to predict house prices. They observe that the model performs well on training data but poorly on validation data. Which THREE actions can help reduce overfitting? (Choose THREE.)

Select 3 answers

A.Reduce model complexity by selecting fewer features

B.Increase regularization strength (e.g., L1, L2)

C.Collect more training data if possible

D.Increase the maximum depth of decision trees

E.Add more interaction features

AnswersA, B, C

Simpler models generalize better.

Why this answer

(reduce model complexity by selecting fewer features) reduces overfitting by limiting the model's capacity to learn noise. Option B (increase regularization strength) penalizes large coefficients, discouraging complex fits. Option C (collect more training data) provides more examples, helping the model generalize.

Option D (increase maximum depth) increases model complexity, worsening overfitting. Option E (adding interaction features) increases complexity, likely increasing overfitting.

Full explanation →

964

MCQeasy

A data scientist is analyzing a dataset with 10,000 rows and 50 columns. The target variable is binary. Which technique is most appropriate for identifying the most important features for predicting the target?

A.Use t-SNE to reduce dimensionality and inspect clusters

B.Run K-means clustering and examine cluster centroids

C.Train a Random Forest classifier and use feature_importances_

D.Apply PCA and select components with highest variance

AnswerC

Random Forest provides feature importance scores based on impurity reduction.

Why this answer

The most appropriate technique for identifying the most important features for predicting a binary target is to train a Random Forest classifier and use the built-in feature_importances_ attribute (Option C). Random Forest is a supervised ensemble method that provides a ranking of feature importance based on how much each feature reduces impurity (e.g., Gini impurity) across all trees. Option A (t-SNE) is a nonlinear dimensionality reduction technique primarily used for visualization in 2D/3D; it does not provide feature importance.

Option B (K-means clustering) is an unsupervised clustering algorithm that does not use the target variable and cannot identify predictive features. Option D (PCA) is an unsupervised dimensionality reduction method that finds principal components maximizing variance, but these components are not directly interpretable as feature importance for a specific target variable.

Full explanation →

965

MCQeasy

Refer to the exhibit. A data scientist checks the status of a SageMaker endpoint and sees the output above. What does this indicate?

A.The endpoint has failed

B.The endpoint is running at full capacity

C.The endpoint is out of service

D.The endpoint is scaling up to meet desired capacity

AnswerD

Current is less than desired, so scaling up.

Why this answer

The endpoint shows InService status with current instance count (2) less than desired count (5), indicating it is scaling up to meet desired capacity. Option D correctly describes this state. Option A is wrong because the status is InService, not OutOfService.

Option B is incorrect because the endpoint is not at full capacity; it is under-provisioned. Option C is wrong because the endpoint is operational, not failed.

Full explanation →

966

Multi-Selectmedium

Which THREE techniques are commonly used for feature engineering in exploratory data analysis? (Select THREE.)

Select 3 answers

A.Extracting date/time components like day of week or hour.

B.Using principal component analysis (PCA) to create new features.

C.Applying one-hot encoding to numerical features.

D.Creating interaction features between variables.

E.Binning continuous variables into discrete intervals.

AnswersA, D, E

Temporal features often reveal patterns.

Why this answer

Extracting date/time components such as day of week, hour, or month from a timestamp is a standard feature engineering technique. It transforms a single datetime column into multiple categorical or cyclical features that can reveal temporal patterns like weekly seasonality or peak hours, which are often critical for time-series models.

Exam trap

The MLS-C01 exam often tests the distinction between feature engineering (creating new features from existing data) and dimensionality reduction (PCA) or encoding (one-hot encoding), leading candidates to mistakenly select PCA as a feature engineering technique when it is actually a preprocessing step for reducing feature space.

Full explanation →

967

MCQeasy

A data scientist trains a convolutional neural network (CNN) for image classification. The training loss decreases steadily, but the validation loss starts increasing after 10 epochs. Which technique should the data scientist use to address this problem?

A.Add more data augmentation to the training set.

B.Use early stopping to halt training when validation loss stops decreasing.

C.Increase the number of training epochs.

D.Add more convolutional layers to increase model capacity.

E.Increase the learning rate.

AnswerB

Early stopping prevents overfitting by stopping at the optimal point.

Why this answer

Early stopping halts training when validation loss stops improving, preventing overfitting. Option A (increasing data augmentation) may help reduce overfitting but does not directly address the already occurring validation loss increase. Option C (more epochs) would worsen overfitting.

Option D (more convolutional layers) increases model capacity, likely worsening overfitting. Option E (higher learning rate) may cause divergence or instability.

Full explanation →

968

Multi-Selecthard

A data scientist is exploring a dataset with mixed data types (numeric, categorical, text). The dataset has 5 million rows. The scientist wants to understand the relationships between variables and identify potential data quality issues. Which THREE tools are suitable for this analysis?

Select 3 answers

A.AWS Glue DataBrew

B.AWS Data Pipeline

C.Amazon SageMaker Data Wrangler

D.Amazon Athena

E.Amazon Kinesis Data Analytics

AnswersA, C, D

Data profiling and visualization.

Why this answer

Options A, C, and D are correct. AWS Glue DataBrew can profile data, visualize distributions, and detect anomalies. Amazon SageMaker Data Wrangler provides interactive data preparation and visualization.

Amazon Athena can be used to run SQL queries for data quality checks. Option B (AWS Data Pipeline) is wrong because it is for workflow orchestration, not EDA. Option E (Amazon Kinesis Data Analytics) is wrong because it is for streaming data, not batch EDA.

Full explanation →

969

MCQhard

A company is building a recommendation system using collaborative filtering on Amazon SageMaker. The dataset contains user-item interactions with a long-tail distribution: a few items have millions of interactions, while most items have very few. The model currently uses matrix factorization with ALS. The recall@20 metric is low for niche items. Which modification would most likely improve recall for long-tail items?

A.Increase the regularization parameter to prevent overfitting

B.Add explicit features like item category and user demographics

C.Increase the number of latent factors in the matrix

D.Use implicit feedback with confidence weighting to downweight popular items

AnswerD

Confidence weighting reduces the influence of overly popular items, allowing the model to learn patterns for niche items.

Why this answer

Implicit feedback models can incorporate confidence weights that downweight popular items, helping the model focus on less frequent items. Adding explicit features would not directly address the long-tail. Increasing the number of factors might help but could also overfit.

Regularization is already present; adjusting it might not target the issue specifically.

Full explanation →

970

MCQhard

A company is using SageMaker to host a model that performs real-time fraud detection. The model receives high request volumes with occasional spikes. The company wants to ensure that the endpoint can handle spikes without throttling while minimizing cost. Which scaling strategy should be used?

A.Use a target tracking scaling policy with a target value of 70% for the SageMakerVariantInvocationsPerInstance metric.

B.Use a simple scaling policy with a step adjustment based on the InvocationsPerInstance metric.

C.Manually adjust the instance count based on monitoring dashboards.

D.Use a scheduled scaling action to add instances during peak hours.

AnswerA

Automatically scales based on utilization.

Why this answer

A target tracking scaling policy with the SageMakerVariantInvocationsPerInstance metric is the correct choice because it automatically adjusts the instance count to maintain a target utilization (e.g., 70%), handling spikes without manual intervention while minimizing cost by scaling down during low traffic. This is the recommended approach for real-time endpoints with variable traffic, as it aligns with AWS best practices for dynamic scaling.

Exam trap

The trap here is that candidates often confuse simple scaling (step adjustments) with target tracking, assuming any metric-based policy works, but target tracking is specifically designed for maintaining a utilization target and is the only option that handles irregular spikes without manual or scheduled intervention.

How to eliminate wrong answers

Option B is wrong because simple scaling policies with step adjustments require predefined thresholds and cooldown periods, which can lead to over-provisioning or under-provisioning during sudden spikes, lacking the smooth, proportional response of target tracking. Option C is wrong because manually adjusting instance count based on dashboards is reactive, error-prone, and cannot handle rapid spikes without causing throttling or waste, defeating the goal of cost minimization. Option D is wrong because scheduled scaling only works for predictable traffic patterns, not for occasional spikes that occur at irregular times, leading to either throttling during unscheduled surges or unnecessary cost during off-peak hours.

Full explanation →

971

Multi-Selecthard

Which THREE techniques help reduce overfitting in a neural network? (Select THREE.)

Select 3 answers

A.Dropout

B.L2 Regularization

C.Increasing the number of layers

D.Using a larger batch size

E.Early Stopping

AnswersA, B, E

Dropout is a regularization technique that reduces overfitting.

Why this answer

Dropout randomly drops units during training, L2 regularization penalizes large weights, and early stopping halts training when validation error increases. Data augmentation can also help but is not listed. Batch normalization may help but primarily for training stability.

Full explanation →

972

Multi-Selectmedium

A data scientist is training a binary classification model to predict customer churn. The dataset has 10,000 samples with 500 churners (5% positive class). Which TWO techniques should the scientist use to address the class imbalance? (Choose TWO.)

Select 2 answers

A.Use SMOTE to oversample the minority class

B.Tune the decision threshold after training

C.Randomly undersample the majority class to match minority size

D.Oversample the minority class by duplicating existing samples

E.Set class_weight='balanced' in the classifier

AnswersA, E

SMOTE creates synthetic samples to balance classes.

Why this answer

(SMOTE) generates synthetic samples for the minority class, effectively balancing the dataset. Option E (class_weight='balanced') adjusts the loss function to penalize misclassifications of the minority class more heavily. Option B (tuning threshold after training) is a post-processing step, not a technique to address imbalance during training.

Option C (random undersampling) can discard useful data, leading to loss of information. Option D (oversampling by duplication) can cause overfitting due to repeated copies of the same samples.

Full explanation →

973

Multi-Selectmedium

A data engineer needs to transform and move 2 TB of data from an Amazon RDS for PostgreSQL instance to Amazon S3 daily. The transformation includes filtering, joining with data in S3, and aggregating. Which AWS services can be used together to accomplish this with minimal operational overhead? (Choose THREE.)

Select 3 answers

A.Amazon EMR

B.Amazon Redshift

C.Amazon S3

D.AWS Glue Data Catalog

E.AWS Glue

AnswersC, D, E

Target storage for transformed data.

Why this answer

Amazon S3 is correct because it serves as the target storage location for the transformed data. The daily 2 TB output from the ETL pipeline must be stored durably and cost-effectively, and S3 provides the ideal object storage layer for this purpose, especially when combined with AWS Glue for the transformation logic.

Exam trap

The trap here is that candidates often assume Amazon EMR or Redshift are necessary for large-scale data processing, but AWS Glue's serverless Spark engine can handle 2 TB daily without any cluster management, making it the lower-overhead choice.

Full explanation →

974

MCQeasy

A data scientist is analyzing a dataset with 100 features and wants to identify which features are most correlated with the target variable. Which AWS service is most appropriate for this task?

A.Amazon QuickSight

B.Amazon Athena

C.AWS Glue DataBrew

D.Amazon SageMaker Data Wrangler

AnswerD

Data Wrangler provides data analysis and feature correlation within SageMaker Studio.

Why this answer

Amazon SageMaker Data Wrangler provides built-in data analysis and visualization capabilities, including correlation analysis, making it suitable for this task. Amazon QuickSight is a BI tool for dashboards, not for feature correlation analysis. Amazon Athena is a query service for data in S3, not for embedded data wrangling.

AWS Glue DataBrew is a visual data preparation tool, but SageMaker Data Wrangler is more directly suited for correlation analysis.

Full explanation →

975

Multi-Selecteasy

Which TWO actions are best practices for securing a SageMaker notebook instance? (Select TWO.)

Select 2 answers

A.Disable direct internet access for the notebook instance.

B.Enable root access for users to install packages.

C.Launch the notebook instance in a private subnet in a VPC.

D.Store data in the notebook's local storage for performance.

E.Use a shared IAM user for all data scientists.

AnswersA, C

Disabling internet access prevents data exfiltration.

Why this answer

Disabling direct internet access for a SageMaker notebook instance (Option A) is a best practice because it prevents the instance from reaching the public internet, reducing the attack surface. This forces all outbound traffic through a VPC, allowing you to control egress via NAT gateways or VPC endpoints, and ensures data does not traverse the public internet. It is a fundamental security hardening step for sensitive workloads.

Exam trap

The trap here is that candidates often confuse 'disabling direct internet access' with 'blocking all internet access' and think it will break SageMaker's ability to download libraries, not realizing that VPC endpoints or a NAT gateway can still provide controlled access to AWS services and the internet.

Full explanation →

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