MLA-C01 Practice Test 31 — 15 Questions

Question 1

A data scientist is training a binary classification model using Amazon SageMaker. The dataset is highly imbalanced (95% negative class, 5% positive class). The model is evaluated on a held-out test set, and the F1 score is 0.12. The data scientist wants to improve the F1 score. Which two actions should the data scientist take? (Choose two.)

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

Accepted Answer

Apply SMOTE (Synthetic Minority Oversampling Technique) to the training data using a preprocessing script in SageMaker Processing.. Option B is correct because SMOTE generates synthetic examples of the minority (positive) class, rebalancing the 95/5 training distribution so the model learns the minority class decision boundary instead of being dominated by the negative class, which directly improves precision and recall (and thus F1) on the held-out test set; this can be implemented via a SageMaker Processing job with an imbalanced-learn script. Option E is correct because setting scale_pos_weight in the SageMaker XGBoost estimator to the ratio of negative to positive samples (roughly 95/5 = 19) upweights the minority class in the gradient boosting loss, making the model penalize minority-class misclassifications more heavily and improving F1 without altering the data. Option A is not appropriate because reducing neural network depth addresses overfitting, not class imbalance, and could even underfit and lower F1. Option C is wrong because raising the decision threshold reduces recall and typically lowers F1 in a heavily imbalanced setting. Option D is wrong because switching the evaluation metric to recall does not improve the model's F1 score; it only changes what is reported.

Answer

Reduce the model complexity by decreasing the number of layers in a deep neural network.

Answer

Increase the decision threshold to reduce false positives.

Answer

Use recall as the primary evaluation metric instead of F1.

Question 2

An ML team uses AWS Step Functions to orchestrate a multi-step inference pipeline: data preprocessing, model inference, and postprocessing. The pipeline runs on demand for single records. The team notices that the pipeline occasionally fails due to timeouts in the preprocessing step. They want to implement retries with exponential backoff and a maximum retry count of 3 for that step. How should they configure this?

Accepted Answer

Modify the Step Functions state machine definition to add a Retry field on the preprocessing state with a maximum retry count of 3 and an exponential backoff rate of 2.0.. AWS Step Functions natively supports retry logic with exponential backoff directly in the state machine definition. By adding a `Retry` field on the preprocessing state with `MaxAttempts: 3` and `BackoffRate: 2.0`, the service automatically retries the step on specified errors (e.g., `States.Timeout` or `Lambda.ServiceException`) with exponentially increasing wait times, without requiring custom code or external orchestration.

Answer

Implement retry logic inside the preprocessing Lambda function code.

Answer

Wrap the preprocessing step in a SageMaker Pipeline step with retry policy.

Answer

Add a Catch in the state machine to rerun the entire pipeline if preprocessing fails.

Question 3

A data scientist is preparing a dataset for a machine learning model that predicts customer churn. The dataset contains a column 'CustomerID' that is a unique identifier. What should the data scientist do with this column before training the model?

Accepted Answer

Remove the column from the feature set.. 'CustomerID' is a unique identifier with no predictive power for churn. Including it as a feature would cause the model to memorize individual customers rather than learn generalizable patterns, leading to overfitting and poor performance on unseen data. In machine learning, such columns should be removed during data preparation to ensure the model learns from meaningful features.

Answer

Keep the column as a feature because it uniquely identifies each customer.

Answer

Use the column as the target variable.

Answer

Encode the column using one-hot encoding.

Question 4

A financial institution uses SageMaker to train and deploy models. They need to track every experiment, model version, and deployment step for audit purposes. Which SageMaker feature should they use to capture the full lineage of artifacts, actions, and contexts?

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking is the feature designed to capture the full lineage of artifacts, actions, and contexts, providing an end-to-end audit trail of the machine learning workflow. It automatically records relationships between data, models, and experiments.

Answer

SageMaker Clarify

Answer

SageMaker Model Registry

Answer

SageMaker Experiments

Question 5

A company wants to deploy a machine learning model that was trained on-premises using TensorFlow. The model is a TensorFlow SavedModel. The company uses AWS and wants to minimize operational overhead. Which deployment option meets these requirements?

Accepted Answer

Deploy the model using Amazon SageMaker with a TensorFlow inference container.. Amazon SageMaker provides a fully managed TensorFlow inference container that directly supports TensorFlow SavedModel format, enabling deployment without any custom infrastructure management. This minimizes operational overhead compared to self-managed options like ECS or Lambda, as SageMaker handles scaling, load balancing, and model updates automatically.

Answer

Deploy the model on Amazon ECS using a custom Docker image.

Answer

Deploy the model as an AWS Lambda function with the TensorFlow runtime.

Answer

Deploy the model using Amazon SageMaker Studio.

Question 6

A company is building a real-time inference pipeline for an ML model. The raw data arrives in JSON format via Amazon Kinesis Data Streams. Before invoking the SageMaker endpoint, the data must be preprocessed to match the training data format. Which THREE steps should be included in the preprocessing function? (Select THREE)

Accepted Answer

Ensure that missing values are handled consistently with the training phase. The preprocessing function must first parse the JSON payload (E), because the raw records arrive from Kinesis Data Streams in JSON format and the individual feature fields cannot be accessed until the JSON is decoded into a usable structure. It must also apply the same feature engineering transformations used during training (C), such as scaling and encoding, since the SageMaker endpoint expects inputs in the exact distribution and representation the model learned; using different transformations would cause training-serving skew and degrade predictions. Missing values must be handled consistently with the training phase (A), because imputation or drop logic applied at inference must mirror training-time behavior to keep the feature semantics identical. Option B is not required because the model's input format is not stated to be CSV, and forcing a CSV string could conflict with the actual serialization the endpoint expects. Option D is incorrect because retraining the model is a separate MLOps activity, not part of the per-record preprocessing function invoked before calling the endpoint.

Answer

Convert the data to a CSV string for model input

Answer

Re-train the model periodically using new data

Question 7

A data scientist runs this pipeline but the Train step fails with "ResourceLimitExceeded". What is the most likely cause?

Accepted Answer

The account has a limit of 0 for ml.p3.2xlarge instances.. The 'ResourceLimitExceeded' error indicates that the requested instance type (ml.p3.2xlarge) exceeds the account's service quota for that specific instance family. In AWS SageMaker, each account has a default limit of 0 for certain GPU instance types like ml.p3.2xlarge unless a quota increase has been requested and approved. This error occurs at the Train step because SageMaker attempts to launch the training job with an instance type that is not allowed by the current quota.

Answer

The volume size is too small for training.

Answer

The Preprocess step did not complete successfully.

Answer

The training image is not accessible.

Question 8

Refer to the exhibit. A data scientist reviews the CloudWatch Logs from an Amazon SageMaker real-time endpoint. What is the MOST likely root cause of the NaN output?

Accepted Answer

The input data contains out-of-range values not seen during training, causing the model to output NaN.. The NaN (Not a Number) output from a SageMaker real-time endpoint is most commonly caused by input data containing values outside the range seen during training. This can lead to numerical instability in the model's forward pass, such as division by zero, log of zero, or exponent overflow, which propagates through layers and results in NaN predictions.

Answer

The model weights became corrupted due to a disk write error.

Answer

The endpoint is overloaded and returning a default NaN response.

Answer

The model artifact failed to load correctly, resulting in NaN weights.

Question 9

A data scientist has trained a model that achieves 95% accuracy on the training set but only 70% on the test set. Which of the following is the most likely cause?

Accepted Answer

Overfitting. A large gap between high training accuracy (95%) and much lower test accuracy (70%) is the classic signature of overfitting: the model has memorized the training data, including its noise, and fails to generalize to unseen data. The model has learned patterns specific to the training set rather than the underlying distribution. Regularization, more data, or a simpler model would typically help.

Answer

Data leakage

Answer

Convergence to local minimum

Answer

Underfitting

Question 10

A data scientist wants to quickly build a binary classification model without writing any code. Which SageMaker feature is MOST suitable?

Accepted Answer

SageMaker Autopilot. SageMaker Autopilot is a no-code AutoML feature that automatically explores data, selects algorithms, tunes hyperparameters, and builds the best model for a given tabular dataset, including binary classification. It requires no model-building code from the user, making it the most suitable choice. The other options are operational/monitoring or labeling tools, not model-building features.

Answer

SageMaker Debugger

Answer

SageMaker Model Monitor

Answer

SageMaker Ground Truth

Question 11

A company wants to detect anomalies in login events from a large user base, focusing on unusual patterns that may indicate compromised accounts. Which SageMaker built-in algorithm is most suitable for this task?

Accepted Answer

IP Insights. IP Insights is a SageMaker built-in unsupervised algorithm designed to learn the relationship between user entities and IP addresses, making it ideal for detecting anomalous login events such as a user logging in from an unusual IP or a compromised account accessing from a new location. It is specifically built for the login-anomaly use case described.

Answer

K-Means

Answer

DeepAR

Answer

Factorisation Machines

Question 12

A data scientist trains a binary classification model using SageMaker and obtains an AUC of 0.95 on the test set. However, the precision-recall curve shows low precision for high recall thresholds. The business requires a model that performs well on the minority class. Which metric should the team primarily optimize during hyperparameter tuning?

Accepted Answer

F1-score on the validation set. The F1-score is the harmonic mean of precision and recall, so it directly penalizes models that achieve high recall at the cost of low precision — exactly the failure mode described. Since the business cares about the minority class, optimizing F1 during hyperparameter tuning forces the model to balance both false positives and false negatives on that class. AUC-ROC can remain deceptively high (0.95) even when precision collapses at high recall because it aggregates performance across all thresholds and is dominated by the majority class.

Answer

Accuracy

Answer

AUC (Area Under the ROC Curve)

Answer

Log loss

Question 13

A company uses SageMaker Model Monitor for feature attribution drift monitoring with SHAP. Which THREE prerequisites must be in place before starting the monitoring schedule? (Select THREE)

Accepted Answer

A SageMaker Clarify processing job that computes SHAP values on the captured data. SageMaker Model Monitor requires a Clarify processing job to compute SHAP values on the captured data as part of the feature attribution drift monitoring setup. This job generates the necessary SHAP explainability values that are compared against the baseline to detect drift.

Answer

Ground truth labels for the inference data

Answer

Baseline constraints file for data quality

Question 14

A data science team wants to host 50 different models for a recommendation engine. Each model is small (under 100 MB) and traffic patterns are unpredictable. They need to minimize cost and operational overhead. Which approach should they take?

Accepted Answer

Use a single multi-model endpoint (MME). A single multi-model endpoint (MME) allows hosting multiple models (up to thousands) on the same endpoint, sharing the underlying compute instance. This minimizes cost and operational overhead for small models (under 100 MB) with unpredictable traffic, as the endpoint dynamically loads and unloads models from Amazon S3 into memory based on incoming requests, eliminating the need for separate endpoints or idle compute.

Answer

Deploy each model to its own real-time endpoint

Answer

Use SageMaker serverless inference for each model

Answer

Use a single multi-container endpoint

Question 15

A machine learning engineer is building a real-time fraud detection pipeline using Amazon Kinesis Data Streams. The data must be prepared (e.g., feature engineering, normalization) before being fed into a SageMaker endpoint. Which TWO steps should the engineer implement to ensure low-latency data preparation?

Accepted Answer

Use AWS Lambda functions to apply feature transformations on each record as it arrives.. Option A is correct because AWS Lambda can be invoked per record or micro-batch directly from Kinesis Data Streams as an event source, applying feature engineering and normalization inline with minimal latency and no server management. Option D is correct because Amazon Kinesis Data Analytics (Kinesis Data Analytics for SQL/Flink) performs continuous SQL-based transformations on the stream itself, enabling real-time normalization and feature computation before records reach the SageMaker endpoint. Option B is not suitable because SageMaker batch transform jobs run on a schedule against stored data, introducing hourly latency and requiring data to be landed first. Option C is not suitable because AWS Glue ETL jobs on a recurring schedule are batch-oriented and cannot transform records in real time. Option E is not suitable because SageMaker Processing jobs are batch jobs that read from storage (typically S3) and write results to S3, not a low-latency streaming transformation path from Kinesis.

Answer

Use SageMaker batch transform jobs scheduled every hour to process the streaming data.

Answer

Use AWS Glue ETL jobs running on a recurring schedule to transform the data.

Answer

Use SageMaker Processing jobs to read from Kinesis and write transformed data to S3.