MLA-C01 Practice Test 34 — 15 Questions

Question 1

A data engineer is using AWS Glue to prepare a dataset for machine learning. The dataset has several columns with outliers. The engineer wants to detect and handle outliers in a scalable manner. Which TWO approaches should the engineer consider? (Select TWO.)

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

Accepted Answer

Use AWS Glue DynamicFrame with Apache Spark to compute interquartile range (IQR) and filter outliers.. Option D is correct because AWS Glue DynamicFrames run on Apache Spark, which scales horizontally across a cluster, and computing the interquartile range (IQR) with Spark SQL/DataFrame operations lets the engineer detect and filter outliers (e.g., values below Q1 - 1.5*IQR or above Q3 + 1.5*IQR) across large datasets efficiently. Option E is correct because Amazon SageMaker Data Wrangler provides built-in outlier detection transforms (such as Robust Standard Deviation, Standard Deviation, and Quantile-based detection) that can be applied to a dataset at scale and exported into a processing or training pipeline, fitting the ML preparation workflow. Option A is not appropriate because manually inspecting data in Amazon S3 does not scale and is error-prone for large datasets. Option B is not appropriate because training a neural network just to remove outliers is overkill, costly, and unnecessary when statistical methods suffice. Option C is not appropriate because pandas in a SageMaker notebook operates in memory on a single instance, so it does not scale to large datasets the way Spark or Data Wrangler do.

Answer

Manually remove outliers by inspecting the data in Amazon S3.

Answer

Train a neural network to identify anomalies and remove them.

Answer

Use pandas in a SageMaker notebook to calculate z-scores and filter outliers.

Question 2

A data scientist is training a model on text data from customer reviews. The dataset contains a mix of English and Spanish reviews. The scientist wants to convert the text into numerical features for a classification model. Which approach is MOST appropriate for this multilingual dataset?

Accepted Answer

Use pre-trained multilingual word embeddings (e.g., multilingual BERT or FastText) to generate feature vectors. Pre-trained multilingual embeddings like multilingual BERT or FastText are trained on corpora spanning many languages and map semantically similar words from different languages into a shared vector space. This allows the classifier to learn from both English and Spanish reviews without translation, preserving language-specific nuance and avoiding translation artifacts. It is the most appropriate approach for a genuinely multilingual dataset.

Answer

Translate all Spanish reviews to English using Amazon Translate, then apply TF-IDF on the English text

Answer

Apply TF-IDF separately for English and Spanish reviews, then concatenate the feature matrices

Answer

Use one-hot encoding on character n-grams for both languages

Question 3

A team is fine-tuning a foundation model using reinforcement learning from human feedback (RLHF) on SageMaker. They have a dataset of human preferences. Which SageMaker capability is most suitable for the reward model training step?

Accepted Answer

SageMaker Training with a custom PyTorch container. Training a reward model for RLHF requires a custom training loop over human preference pairs, typically using a PyTorch model with a regression or ranking loss. SageMaker Training with a custom PyTorch container gives full control over the training script, loss function, and data pipeline needed for this step. The other SageMaker capabilities are higher-level or data-labeling services that do not provide this flexibility.

Answer

SageMaker JumpStart

Answer

SageMaker Ground Truth

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SageMaker Autopilot

Question 4

A team uses SageMaker Pipelines to train and register a model. They want to conditionally run a hyperparameter tuning step only if the data quality check passes. Which pipeline step type should they use to branch the execution?

Accepted Answer

ConditionStep. The ConditionStep allows comparing values and branching to different steps. If data quality passes, the tuning step runs; otherwise, the pipeline stops or runs an alternative step. Other steps do not provide conditional branching.

Answer

TuningStep

Answer

TrainingStep

Answer

TransformStep

Question 5

A team needs to split a time-series dataset for a forecasting model. They want to avoid data leakage and evaluate model performance on future unseen data. Which data splitting strategy should they use?

Accepted Answer

Walk-forward validation. Walk-forward validation is the correct strategy for time-series forecasting because it preserves the temporal order of data, training on past observations and testing on future ones in sequential steps. This avoids data leakage by ensuring that no future information is used to predict past events, which is critical for evaluating model performance on unseen future data.

Answer

Holdout validation with random sampling

Answer

K-fold cross-validation

Answer

Random stratified split

Question 6

A company uses SageMaker to train a model. They want to ensure that training data is encrypted at rest and in transit, and that only authorized users can access the training artifacts. Which three steps should they take? (Choose three.)

Accepted Answer

Configure IAM policies to restrict access to SageMaker resources. Option A is correct because IAM policies are the mechanism that enforces least-privilege authorization over SageMaker resources, including training jobs, endpoints, and the S3 buckets holding training artifacts, ensuring only authorized users can access them. Option C is correct because placing training jobs in a VPC with private subnets and interface VPC endpoints (AWS PrivateLink) for services like S3 and SageMaker keeps traffic on the AWS private network, protecting data in transit and preventing exposure to the public internet. Option D is correct because enabling S3 server-side encryption (SSE-S3, SSE-KMS, or SSE-C) encrypts training data at rest in the bucket where it is stored and read by the training job. Option B is not correct because SageMaker Model Monitor detects data drift and quality issues in deployed models; it does not provide encryption or access control for training data. Option E is not correct because SageMaker Network Isolation disables outbound network access for training containers, which is a containment control rather than a mechanism that encrypts data at rest or in transit.

Answer

Use SageMaker Model Monitor

Answer

Use SageMaker Network Isolation

Question 7

A machine learning engineer is using Amazon SageMaker Data Wrangler to prepare a dataset with a categorical feature that has over 5,000 distinct values (high cardinality). The engineer needs to transform this feature into a form suitable for a gradient boosting model while preserving as much information as possible. Which transform should be applied?

Accepted Answer

Target encoding with smoothing. Target encoding with smoothing is the best choice for high-cardinality categorical features in gradient boosting models. It replaces each category with a smoothed statistic (typically the mean of the target for that category), which preserves predictive information while avoiding the dimensionality explosion of one-hot encoding. Smoothing blends the category mean with the global mean to prevent overfitting on rare categories.

Answer

Label encoding

Answer

One-hot encoding

Answer

Drop the feature

Question 8

Which TWO of the following are best practices for deploying machine learning models on SageMaker? (Select TWO.)

Accepted Answer

Use separate production and staging endpoints to test new models before full rollout. Option B is correct because using separate staging and production endpoints lets you validate a new model version against real traffic patterns and roll back safely before promoting it to the production endpoint, which is a core SageMaker deployment best practice. Option E is correct because SageMaker Data Capture on endpoints logs request/response payloads to Amazon S3, enabling auditing, model monitoring, and drift detection via Model Monitor. Option A is wrong because model artifacts should be stored in Amazon S3 and loaded by the endpoint, not on EBS volumes attached to instances. Option C is wrong because SageMaker Model Registry does exist and is the recommended way to version and track models, not manual tags. Option D is wrong because disabling CloudWatch Logs removes observability and is not a best practice for production inference.

Answer

Store model artifacts in Amazon EBS volumes attached to the endpoint instances

Answer

Manually track model versions using tags because SageMaker Model Registry is not available for deployment

Answer

Disable CloudWatch Logs to reduce costs during inference

Question 9

An ML team is building a time-series forecasting model for daily sales. They need to split the data into training and validation sets without data leakage, and the validation set should be the most recent 30 days. Which splitting strategy should they use?

Accepted Answer

Time-based split with last 30 days as validation. Time-based splitting preserves the chronological order of observations, which is essential for time-series forecasting because future values depend on past values. Using the most recent 30 days as the validation set simulates the real deployment scenario where the model predicts unseen future data. Random, stratified, or k-fold splits would mix past and future observations, causing data leakage and overly optimistic validation metrics.

Answer

Stratified split based on sales volume

Answer

K-fold cross-validation

Answer

Random 80/20 split

Question 10

A financial services company uses Amazon SageMaker to deploy a fraud detection model for real-time inference. The model is deployed on an ml.m5.large instance with a SageMaker real-time endpoint. The endpoint has an auto scaling policy configured using a custom scaling policy based on average CPU utilization, with scale out threshold at 70% and scale in threshold at 30%. During a flash sale event, the traffic to the endpoint spikes tenfold within minutes. The endpoint fails to handle the load, resulting in increased latency and timeouts. The data science team needs to improve the scalability of the endpoint to handle sudden traffic spikes. Which solution should the team implement?

Accepted Answer

Replace the custom scaling policy with a target tracking scaling policy based on the number of invocations per instance, with a target value of 1000.. A target tracking scaling policy based on invocations per instance directly ties scaling to the actual workload metric for a real-time endpoint, allowing faster and more accurate scale-out during sudden traffic spikes. Unlike CPU-based custom scaling, invocation-based target tracking reacts to request volume, which is the true driver of load for inference. This improves the endpoint's ability to handle a tenfold spike.

Answer

Implement a SageMaker Model Ensemble with two additional models to balance the load.

Answer

Implement a SageMaker Inference Pipeline with a pre-processing step to reduce model input size.

Answer

Switch to a GPU instance type, such as ml.p3.2xlarge, to increase compute capacity.

Question 11

An e-commerce company uses a SageMaker endpoint to serve a product recommendation model. The model is retrained every month using batch transforms. The ML team has set up a retraining pipeline using SageMaker Processing jobs and Step Functions. Recently, the Step Functions workflow has been failing at the retraining step with an error: 'AccessDeniedException: User: arn:aws:sts::123456789012:assumed-role/RetrainingRole/abc123 is not authorized to perform: s3:GetObject on resource: arn:aws:s3:::training-data/processed/latest.parquet'. The team confirms that the S3 bucket exists and the object is present. The retraining role has the following policy: { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "s3:GetObject", "s3:PutObject" ], "Resource": "arn:aws:s3:::training-data/*" } ] }. The team also verifies that the bucket policy does not explicitly deny access. What is the MOST likely cause of the AccessDenied error?

Accepted Answer

The training data object uses server-side encryption with AWS KMS (SSE-KMS), and the retraining role lacks kms:Decrypt permission on the KMS key. The error is an AccessDeniedException on s3:GetObject despite the role having s3:GetObject permission on the bucket. When an S3 object is encrypted with SSE-KMS, the caller must also have kms:Decrypt permission on the KMS key used to encrypt the object. The retraining role's policy only grants S3 permissions, not KMS permissions, so the GetObject call fails. This is the most likely cause given the policy shown.

Answer

The Step Functions execution role does not have permission to invoke the SageMaker Processing job

Answer

The path in the error message is misspelled; the actual object is at a different key

Answer

The S3 bucket has a bucket policy that denies access to the retraining role based on a condition like aws:SourceIp

Question 12

A company operates an e-commerce platform that uses a machine learning model to recommend products to users. The model is deployed on an Amazon SageMaker endpoint with automatic scaling enabled based on average CPU utilization. The model was trained on historical data and is updated weekly. Recently, the platform experienced a flash sale event that caused a sudden spike in traffic. During the event, the endpoint's latency increased dramatically, and many requests timed out. After the event, the team reviews the CloudWatch metrics and notices that the CPU utilization never exceeded 70%, and the scaling policy was triggered but instances took several minutes to become available. The team wants to prevent similar issues in future flash sales. Which course of action would be MOST effective?

Accepted Answer

Implement scheduled scaling to add capacity ahead of known flash sales.. Scheduled scaling allows you to proactively add capacity ahead of known traffic events like flash sales, eliminating the cold-start delay that occurs when reactive scaling policies (like those based on CPU utilization) must launch new instances. During the flash sale, the scaling policy was triggered but instances took minutes to become available, causing timeouts; scheduled scaling pre-warms the endpoint by adjusting the desired instance count before the traffic spike hits.

Answer

Use predictive scaling based on historical traffic patterns.

Answer

Lower the CPU utilization threshold for the scaling policy to 40%.

Answer

Switch to larger instance types to handle higher CPU loads.

Question 13

Refer to the exhibit. A data scientist configured SageMaker Debugger to monitor training for overfitting. However, the rule never triggers even though the model appears to be overfitting. What is the most likely reason?

Accepted Answer

The debug hook is not collecting the validation loss. SageMaker Debugger monitors training by collecting tensors (e.g., loss, accuracy) via a debug hook. The rule for detecting overfitting typically compares training loss to validation loss. If the hook is not configured to collect validation loss tensors, the rule has no data to evaluate and will never trigger, even if overfitting occurs. This is the most likely reason because the rule depends on specific tensor names being saved.

Answer

The instance type for the rule is too small

Answer

The S3 output path is not writable

Answer

The rule evaluator image is incorrect

Question 14

A company wants to maintain multiple versions of a trained model in a central repository and track metadata such as training metrics, hyperparameters, and approval status. Which SageMaker feature should they use?

Accepted Answer

SageMaker Model Registry. SageMaker Model Registry is the correct choice because it is specifically designed to serve as a central repository for managing multiple versions of trained models, tracking metadata such as training metrics, hyperparameters, and approval status. It integrates with SageMaker Pipelines and Experiments to automate model governance, enabling versioning, approval workflows, and lineage tracking.

Answer

SageMaker Pipelines

Answer

SageMaker Feature Store

Answer

SageMaker Experiments

Answer

SageMaker Studio

Question 15

A company is running a SageMaker endpoint serving multiple models. They need to monitor for data drift and model quality. Which THREE actions are necessary? (Choose three.)

Accepted Answer

Enable data capture on the endpoint. Option B is correct because SageMaker Model Monitor requires data capture to be enabled on the endpoint (via DataCaptureConfig) so that inference requests and responses are logged to Amazon S3 for drift and quality analysis. Option D is correct because a Model Monitor schedule (created with CreateMonitoringSchedule) is what actually runs the monitoring jobs on a recurring basis against the captured data. Option E is correct because Model Monitor compares live data to a baseline; constraints and statistics generated from the training dataset (via DefaultModelMonitor.suggest_baseline) define the expected schema and thresholds used to detect drift and quality violations. Option A is not required, since a shadow endpoint is for testing a new model variant, not for monitoring drift or quality of the existing endpoint. Option C is not required, because SageMaker Debugger is used for debugging training jobs (tensors, metrics, profiling), not for production data drift or model quality monitoring.

Answer

Deploy a shadow endpoint for comparison

Answer

Use SageMaker Debugger for monitoring