MLA-C01 Practice Test 16 — 15 Questions

Question 1

A data scientist is using SageMaker Automatic Model Tuning to find the best hyperparameters for a model. They want to reduce the total tuning time for a given number of training jobs. Which tuning strategy should they choose?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Hyperband. Hyperband is an early-stopping, multi-fidelity tuning strategy that allocates resources to promising configurations and terminates poor performers early, dramatically reducing total tuning time. It is especially effective when many training jobs are needed but only a few hyperparameter combinations are worth full training. This makes it the best choice when the goal is to reduce total tuning time for a fixed number of jobs.

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Grid search

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Random search

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Bayesian optimization

Question 2

A data scientist wants to track hyperparameters, metrics, and artifacts for multiple training runs in SageMaker. They need to compare runs and identify the best performing model. Which SageMaker feature should they use?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is the feature designed to track, organize, and compare machine learning training runs. It automatically captures hyperparameters, metrics, and artifacts for each run, allowing data scientists to analyze and identify the best performing model. It provides a centralized view of experiments and runs, facilitating reproducibility and collaboration.

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SageMaker Model Monitor

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SageMaker Debugger

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SageMaker Autopilot

Question 3

A machine learning engineer wants to automatically track hyperparameters, metrics, and artifacts for multiple training runs. Which SageMaker feature should they use?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is the feature designed to track, organize, and compare machine learning training runs, including hyperparameters, metrics, and artifacts. It automatically logs these elements when integrated with SageMaker training jobs, enabling reproducibility and experiment comparison. This directly matches the requirement to track multiple runs.

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SageMaker Debugger

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SageMaker Model Monitor

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SageMaker Clarify

Question 4

A company is using SageMaker Debugger to monitor a training job for a deep learning model. They want to detect when gradients become extremely large, which may cause training instability. Which built-in rule should they use?

Accepted Answer

ExplodingGradients. SageMaker Debugger provides a built-in rule named ExplodingGradients that monitors gradient tensors during training and triggers when gradient magnitudes exceed a threshold, indicating training instability. It is purpose-built to detect the exact condition described — gradients becoming extremely large. The rule can emit a warning or stop the job based on configuration.

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DeadRelu

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VanishingGradients

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Overfit

Question 5

A data scientist needs to run a hyperparameter tuning job for a PyTorch model using SageMaker. They want to use Hyperband for efficient resource allocation. Which tuning strategy should they select in the HyperparameterTuner?

Accepted Answer

Hyperband. SageMaker's HyperparameterTuner supports Hyperband as a first-class strategy, which implements the multi-armed bandit early-stopping algorithm to allocate more resources to promising configurations and prune poor ones early. Selecting 'Hyperband' as the strategy directly enables this efficient resource allocation. It is distinct from Bayesian, Random, and Grid strategies, which do not perform the successive-halving resource allocation that Hyperband does.

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Bayesian optimization

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Random search

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Grid search

Question 6

A machine learning engineer is developing a text classification model using Amazon SageMaker. The dataset consists of 1 million customer reviews, with labels indicating sentiment (positive, negative, neutral). The engineer uses a pre-trained BERT model from the Hugging Face Model Hub and fine-tunes it on the dataset using SageMaker's Hugging Face estimator with a ml.p3.2xlarge instance. After 2 hours of training, the training job fails with a 'ResourceExhaustedError: CUDA out of memory' error. The error occurs during the forward pass of the first epoch. The engineer confirms that the batch size is set to 32, the maximum sequence length is 512 tokens, and the dataset is stored in a S3 bucket in the same AWS region. The engineer needs to complete fine-tuning without increasing instance costs. Which course of action should the engineer take?

Accepted Answer

Reduce the batch size to 8 and enable gradient accumulation with 4 steps to maintain effective batch size.. Reducing the batch size to 8 directly lowers GPU memory usage per forward pass, and enabling gradient accumulation with 4 steps allows the model to simulate the original effective batch size of 32 (8 × 4 = 32) without increasing memory footprint. This approach resolves the CUDA out-of-memory error while keeping the same instance type (ml.p3.2xlarge) and without incurring additional costs.

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Enable SageMaker Managed Spot Training to reduce costs and use the savings to upgrade to a ml.p3.8xlarge instance.

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Switch to a CPU-based instance like ml.c5.2xlarge to avoid GPU memory constraints.

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Reduce the maximum sequence length to 128 tokens to lower memory consumption.

Question 7

A data scientist is training an object detection model using SageMaker built-in Object Detection algorithm. They want to visualize the bounding boxes on validation images after training. Which approach should they use?

Accepted Answer

Write a custom inference script that saves images with bounding boxes. SageMaker's built-in Object Detection algorithm outputs predictions in JSON (bounding boxes, class labels, scores) via inference; to visualize boxes on validation images, you must write a custom inference script that reads those predictions and draws rectangles on the images. No built-in visualization step exists in the algorithm itself. The other options address debugging, monitoring, or explainability, not bounding-box rendering.

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Use SageMaker Debugger to capture output tensors

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Enable SageMaker Model Monitor

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Use SageMaker Clarify

Question 8

A company wants to reduce costs for a production SageMaker endpoint that has predictable traffic patterns. They have purchased a Savings Plan. What additional step can they take to further optimize costs while maintaining performance?

Accepted Answer

Use SageMaker Inference Recommender to right-size the endpoint. SageMaker Inference Recommender runs load tests against candidate instance types and configurations to identify the most cost-effective endpoint that still meets latency and throughput requirements. Since the Savings Plan already discounts compute, right-sizing the underlying instance fleet is the remaining lever for cost optimization without sacrificing performance. This directly addresses the 'maintaining performance' constraint that rules out simply shrinking capacity.

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Reduce the number of instances to one, regardless of load

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Switch from real-time to batch inference

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Disable auto-scaling

Question 9

Which SageMaker built-in algorithm is designed for time series forecasting?

Accepted Answer

DeepAR. DeepAR is a SageMaker built-in algorithm specifically designed for time series forecasting. It uses recurrent neural networks (RNNs) to predict future values based on historical data, making it ideal for forecasting tasks such as demand prediction or sales forecasting.

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Linear Learner

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Factorisation Machines

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BlazingText

Question 10

A team is using AWS Glue to process streaming data from Amazon Kinesis. The streaming data contains both structured and semi-structured fields. The team needs to flatten the semi-structured fields into columns for downstream ML training. Which Glue feature is BEST suited?

Accepted Answer

Relationalize transform. The Relationalize transform is specifically designed to flatten nested JSON or semi-structured fields into a relational structure, making it ideal for converting complex streaming data from Kinesis into flat columns for ML training. It automatically handles arrays and structs by creating separate tables or columns, which is exactly what the team needs for downstream processing.

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Spigot transform

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ResolveChoice transform

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ApplyMapping transform

Question 11

A company uses SageMaker endpoints with auto-scaling based on CPU utilization. During a flash sale, latency increases despite low CPU. What should be done?

Accepted Answer

Use a custom metric such as memory utilization or request count for auto-scaling. CPU utilization is a poor scaling metric for inference workloads that are I/O or memory-bound. During a flash sale, increased request concurrency can cause queuing and latency spikes even when CPU is low. Using a custom metric like request count per instance or memory utilization directly reflects the load on the inference endpoint, enabling the Application Auto Scaling target tracking policy to scale out proactively before latency degrades.

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Increase the instance size

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Disable auto-scaling and use a larger instance

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Switch to GPU instances

Question 12

A company is building a recommender system using implicit feedback (clicks) and explicit feedback (ratings). They plan to use Amazon SageMaker to train a model. The data includes user ID, item ID, timestamp, and rating (if any). Which TWO data preparation steps should the team perform? (Choose TWO.)

Accepted Answer

Convert user ID and item ID to integer indices for matrix factorization. Matrix factorization algorithms in Amazon SageMaker (e.g., the built-in Factorization Machines algorithm or the Apache Spark-based collaborative filtering) require user and item identifiers to be converted to contiguous integer indices starting from 0. This is necessary for efficient embedding lookup and to avoid memory blowup from sparse categorical features. SageMaker's implementation expects the input data in recordIO-wrapped protobuf format with integer-encoded user and item columns.

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Use target encoding on user ID based on average rating

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Normalize ratings using StandardScaler

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One-hot encode user ID and item ID

Question 13

A data scientist is using SageMaker to train an XGBoost model for a regression problem. After training, they evaluate the model on a test set and get an RMSE of 10 and an R² of 0.85. Which additional metric would give the MOST insight into the model's average prediction error magnitude?

Accepted Answer

Mean Absolute Error (MAE). MAE measures the average absolute difference between predicted and actual values in the same units as the target, so it directly answers 'how far off is the model on average?' RMSE of 10 is also in target units but is dominated by large errors due to squaring, so MAE complements it by showing typical error magnitude without outlier amplification.

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AUC

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Confusion matrix

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F1 score

Question 14

A data scientist is preparing a dataset for a linear regression model. The features have different scales: one feature ranges from 0 to 1000, another from 0 to 1, and a third from -5 to 5. The scientist wants to ensure that all features contribute equally to the model. Which TWO scaling techniques should the scientist consider? (Select TWO.)

Accepted Answer

MinMaxScaler (min-max normalization). MinMaxScaler (A) is correct because it rescales each feature to a fixed range, typically [0, 1], using the formula (x - min) / (max - min), which directly addresses the differing ranges (0-1000, 0-1, -5-5) and puts all features on a comparable scale so they contribute equally to the linear regression. StandardScaler (E) is also correct because it standardizes features by removing the mean and scaling to unit variance using z = (x - μ) / σ, which is a standard and effective way to equalize feature contributions when scales differ. PCA (B) is not a scaling technique; it is a dimensionality-reduction method and does not by itself normalize feature ranges. One-hot encoding (C) is for converting categorical variables into binary vectors, not for rescaling numeric features. Log transformation (D) is a nonlinear transform that can reduce skew but does not guarantee equal scaling across features with different ranges.

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Principal Component Analysis (PCA)

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One-hot encoding

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Log transformation

Question 15

A company wants to reduce costs for a SageMaker real-time endpoint that receives predictable traffic patterns: high during business hours and low at night. The model is a small PyTorch model. Which cost-saving strategy is most suitable?

Accepted Answer

Configure auto-scaling with a scheduled scaling policy to add instances during business hours and reduce at night. Scheduled auto-scaling is designed for predictable traffic patterns: you define a schedule to scale out during business hours and scale in at night, matching capacity to demand and minimizing cost. For a small PyTorch model on a real-time endpoint, this is the most cost-effective and operationally simple strategy.

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Use a single large instance to handle peak load

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Use a multi-model endpoint with multiple models

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Switch to batch transform jobs and run nightly