MLA-C01 Practice Exam 52 — 20 Questions

Question 1

A data science team uses SageMaker Pipelines to orchestrate their ML workflow. They noticed that even when source data hasn't changed, the pipeline re-runs all steps, wasting compute time. What should they enable to avoid redundant runs?

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

Accepted Answer

Enable pipeline caching by setting the CacheConfig property for each step. SageMaker Pipelines supports step caching via the `CacheConfig` property. When enabled, the pipeline checks if the step's inputs (including source data, parameters, and code) have changed since the last successful run. If no changes are detected, the step is skipped and the previous output is reused, eliminating redundant compute.

Answer

Configure the pipeline to run on a schedule instead of on-demand

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Use the Parameter step to pass previous execution ID

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Use Lambda step to check data changes before running

Question 2

A company is building a time series forecasting model using SageMaker DeepAR. The raw data is a CSV with columns: timestamp, item_id, and value. What is the correct data format required for DeepAR training?

Accepted Answer

JSON Lines files with 'start', 'target', and optional fields per time series. DeepAR requires time series data to be provided in JSON Lines format, where each line represents a single time series with a 'start' timestamp (in ISO 8601 format), a 'target' array of values, and optional fields like 'cat' for categorical features. This structured format allows DeepAR to handle variable-length sequences and missing values natively, which is not possible with simple CSV or wide-format data.

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A wide-format CSV where each column is a different time series

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Parquet files with a schema containing timestamp, item_id, and value

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A single CSV file with columns: timestamp, item_id, value

Question 3

A data engineer needs to ingest streaming clickstream data from a website into an S3 data lake for ML training. The data arrives continuously and must be written to S3 in near real-time. Which AWS service is best suited for this task?

Accepted Answer

Amazon Kinesis Data Firehose with S3 as destination. Amazon Kinesis Data Firehose is the most appropriate service for loading streaming data into S3 with minimal effort and near-real-time latency. It can buffer, transform, and compress data before delivery.

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AWS Lambda function writing to S3 on every click event

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Amazon Athena queries running on the website's source database

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AWS Glue ETL job triggered by a cron job every 5 minutes

Question 4

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.

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

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Use the column as the target variable.

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Encode the column using one-hot encoding.

Question 5

A data science team has trained a PyTorch model using Amazon SageMaker and wants to deploy it with a custom inference container that includes a pre-processing step. The team needs to minimize latency and ensure the pre-processing runs only once per request. Which SageMaker real-time inference option should they use?

Accepted Answer

Create a SageMaker inference pipeline with two containers: one for pre-processing and one for inference.. A SageMaker inference pipeline allows you to chain two containers in a single endpoint, where the first container handles pre-processing and the second runs inference. This ensures that pre-processing runs exactly once per request, minimizing latency by avoiding redundant processing and keeping the request within the same HTTP connection.

Answer

Deploy the model on a multi-model endpoint and include pre-processing in the model code.

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Use a batch transform job with a pre-processing script.

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Package pre-processing and inference in a single container with a custom entry point.

Question 6

A company is deploying a machine learning model using Amazon SageMaker. The model is a large deep learning model that requires GPU for inference. The company expects unpredictable traffic patterns with occasional bursts. They want to minimize cost while ensuring low latency during bursts. Which TWO actions should they take? (Select TWO.)

Accepted Answer

Use a multi-model endpoint with a mix of CPU and GPU instances to handle variable traffic.. A multi-model endpoint with a mix of CPU and GPU instances allows the company to host multiple models on the same endpoint, reducing cost by sharing underlying instances. By including GPU instances, the endpoint can handle the GPU-intensive deep learning inference for the large model, while the CPU instances can serve lighter loads or fallback traffic, ensuring low latency during unpredictable bursts without over-provisioning.

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Use a serverless endpoint configuration to automatically scale.

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Use Spot instances for the endpoint to reduce cost.

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Provision multiple on-demand GPU instances behind a load balancer.

Question 7

A company has 50 small PyTorch models that are used infrequently for inference. They want to minimize costs while maintaining the ability to serve all models from a single endpoint. Which SageMaker feature should they use?

Accepted Answer

Multi-model endpoint. Multi-model endpoints (MME) allow hosting multiple models on a single endpoint, loading models dynamically based on the target model in the request. This reduces cost for many small, infrequently used models by sharing the underlying instance.

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Multi-container endpoint

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Batch transform job

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Real-time endpoint with 50 production variants

Question 8

A team is using Amazon SageMaker for feature engineering. They have a dataset with a column 'TransactionDate' in string format (e.g., '2023-01-15 10:30:00'). They need to create features: year, month, day, hour, and day_of_week. What is the most efficient way to do this in a SageMaker processing job?

Accepted Answer

Use pandas datetime functions and then split. Using pandas datetime functions within a SageMaker processing job is the most efficient approach for this task. SageMaker processing jobs run custom Python scripts, and pandas provides vectorized operations (e.g., `pd.to_datetime()`, `.dt.year`, `.dt.month`, `.dt.day`, `.dt.hour`, `.dt.dayofweek`) that parse the string column and extract all required features in a single pass without external dependencies or data movement.

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Use SageMaker built-in first party algorithms

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Use AWS Glue for transformation

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Use SQL query in Athena on S3 data

Question 9

A financial services company must deploy a SageMaker endpoint that processes sensitive customer data. They require that all traffic between the endpoint and the model containers be encrypted, and that the endpoint cannot be accessed from outside a specific VPC. Which combination of settings should they use?

Accepted Answer

Enable inter-container traffic encryption and configure the endpoint with VPC-only mode. Inter-container traffic encryption ensures that data between the SageMaker endpoint and the model containers is encrypted in transit, typically using TLS. Configuring the endpoint with VPC-only mode restricts all inference traffic to the specified VPC, preventing any access from outside that VPC. This combination directly addresses the requirements for encrypted inter-container traffic and VPC-restricted access.

Answer

Use a private VPC and enable data encryption at rest using KMS

Answer

Enable network isolation mode and inter-container traffic encryption

Answer

Deploy the endpoint in a private subnet and use a VPC endpoint for SageMaker API

Question 10

A financial services company operates a real-time inference endpoint for a fraud detection model on Amazon SageMaker. The model was trained on historical transaction data from 2023. Over the past month, the model's precision has dropped from 92% to 78%, while recall remains high at 95%. The data science team suspects data drift and has already enabled SageMaker Model Monitor with data capture and a baseline from the training data. The latest monitoring report indicates no statistically significant drift in any of the input features. The team also verified that the inference code and model artifact have not changed. Despite the stable feature distributions, the model is misclassifying an increasing number of legitimate transactions as fraudulent (false positives). The business is concerned about the impact on customer experience. What is the best course of action?

Accepted Answer

Investigate recent ground truth labels to check for label drift or changes in the fraud definition.. The scenario describes a drop in precision without feature drift, which indicates label drift – the relationship between features and labels has changed. The most effective next step is to collect and analyze recent ground truth labels to confirm label drift. Retraining on recent data without addressing the root cause may not help if the new labels are also stale or incorrect. Increasing data capture rate will not diagnose the issue. Changing the algorithm is unlikely to help without understanding the cause.

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Replace the model with a more complex algorithm such as a gradient-boosted tree.

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Retrain the model using the most recent 30 days of transaction data with automated retraining pipelines.

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Increase the data capture sampling percentage from 10% to 100% for more detailed analysis.

Question 11

An organization wants to automate ML retraining using an event-driven architecture. Which THREE services should they combine? (Select THREE.)

Accepted Answer

SageMaker (training jobs or pipelines). Amazon SageMaker provides the training jobs and pipelines that execute the ML retraining workflow. Amazon EventBridge acts as the event bus that triggers retraining based on events such as new data arrival or model drift detection. AWS Lambda serves as the lightweight compute layer that can preprocess events, invoke SageMaker APIs, or orchestrate conditional logic before starting a training job.

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AWS Glue

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Amazon CloudWatch Logs

Question 12

An e-commerce company uses Amazon SageMaker to deploy a real-time inference endpoint for product recommendations. The endpoint receives bursty traffic, with occasional spikes. The company wants to minimize cost while ensuring that latency remains under 100 ms. Which approach should the company take?

Accepted Answer

Deploy the model on a multi-model endpoint with automatic scaling and configure a warm-up period for new instances.. A multi-model endpoint with automatic scaling allows multiple models to share a single endpoint, reducing cost while handling bursty traffic. Configuring a warm-up period ensures new instances are fully initialized before receiving traffic, preventing cold-start latency spikes and keeping inference under 100 ms.

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Use an elastic inference accelerator to reduce latency instead of scaling.

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Use a scheduled scaling plan based on historical traffic patterns.

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Deploy the model on one large instance to handle peak load.

Question 13

A company has a SageMaker endpoint that was deployed successfully and is in service. However, when the team sends test inferences using the InvokeEndpoint API, they receive a 500 internal server error. The endpoint logs in CloudWatch show a stack trace indicating 'OutOfMemoryError: Java heap space'. The model is a large XGBoost model (2 GB) and the endpoint is using an ml.m5.large instance with 8 GB of memory. What is the MOST likely cause and solution?

Accepted Answer

The instance type has insufficient memory for the model size; use a larger instance type like ml.m5.xlarge (16 GB) or ml.m5.2xlarge.. The OutOfMemoryError in Java heap space indicates that the model (2 GB) plus the runtime overhead of the XGBoost container and Java-based inference code exceed the available memory on the ml.m5.large instance (8 GB total, but not all is available for the Java heap). The most direct fix is to use a larger instance type, such as ml.m5.xlarge (16 GB) or ml.m5.2xlarge, to provide sufficient heap space for the model and inference operations.

Answer

The endpoint needs to have a smaller batch size configured in the real-time inference request.

Answer

The model is a Transformer model and requires a GPU instance; use ml.g4dn.xlarge instead.

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The SageMaker container is not compatible with XGBoost; switch to a framework container.

Question 14

A company wants to deploy a model on SageMaker serverless inference. Which TWO of the following are limitations of serverless endpoints compared to real-time endpoints? (Choose two.)

Accepted Answer

No support for GPU instances. SageMaker serverless inference does not support GPU instances; it only runs on CPU-based instances. This is a fundamental limitation for workloads requiring GPU acceleration, such as deep learning models. In contrast, real-time endpoints support both CPU and GPU instance types.

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Cold starts can cause increased latency for infrequent requests

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Cannot deploy multiple containers in the same endpoint

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No automatic scaling – must be configured manually

Question 15

A company uses SageMaker Processing jobs to clean customer transaction data. The processing script runs on a single ml.m5.large instance and takes 30 minutes to process 50 GB of data in CSV format. To reduce processing time, the company wants to process 200 GB of data within 1 hour. Which combination of changes should the company make?

Accepted Answer

Increase InstanceCount to 4 and convert the data to Parquet format.. Increasing InstanceCount to 4 allows parallel processing of the 200 GB dataset across multiple ml.m5.large instances, each handling 50 GB, which directly reduces processing time. Converting the data from CSV to Parquet format further accelerates processing by enabling columnar storage and predicate pushdown, reducing I/O and CPU overhead. Together, these changes can achieve the goal of processing 200 GB within 1 hour, as the original 50 GB took 30 minutes on a single instance.

Answer

Run the job in local mode with a larger EBS volume.

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Increase VolumeSizeInGB to 100 and use gzip compression.

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Use a larger instance type (e.g., ml.r5.4xlarge) and keep the same script.

Question 16

A company deploys a SageMaker model using AWS KMS for encryption at rest. They have a compliance requirement to rotate the KMS key every year without causing downtime for the inference endpoint. Which approach should they take?

Accepted Answer

Enable automatic key rotation on the existing KMS key. AWS KMS supports automatic key rotation, which creates new backing keys annually while retaining the same key ID and metadata. This ensures that the SageMaker endpoint continues to use the same KMS key alias and configuration, so no endpoint update or downtime is required. Automatic rotation satisfies the compliance requirement without any manual intervention or endpoint recreation.

Answer

Use AWS Certificate Manager (ACM) for encryption

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Create a new KMS key and update the endpoint configuration

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Manually rotate the key by recreating the endpoint

Question 17

A data science team is building a model to predict fraudulent transactions. The dataset has 1 million legitimate transactions and only 1,000 fraudulent ones. They plan to use Amazon SageMaker to train a model. Which data preparation technique should they apply to address the severe class imbalance before training?

Accepted Answer

Use SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic fraudulent samples.. SMOTE (Synthetic Minority Oversampling Technique) is the correct choice because it generates synthetic fraudulent samples by interpolating between existing minority class instances in feature space, rather than simply duplicating records. This creates more diverse and realistic training data, reducing overfitting risk while addressing the severe 1:1000 class imbalance. Amazon SageMaker's built-in algorithms and data processing capabilities can easily integrate SMOTE-applied datasets for training.

Answer

Apply data augmentation using image transformations because fraud detection is like image classification.

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Randomly oversample the fraudulent class to match the legitimate count by duplicating existing fraud records.

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Randomly undersample the legitimate class to 1,000 samples to create a balanced dataset.

Question 18

A data scientist is trying to create a SageMaker endpoint configuration with 6 instances of ml.c5.large for a production variant. The creation fails with the error shown in the exhibit. Which action should the data scientist take to resolve this issue?

Accepted Answer

Request a service quota increase for ml.c5.large for real-time endpoints from the AWS Service Quotas console.. The error indicates that the requested number of instances exceeds the service quota for ml.c5.large for real-time endpoints. AWS enforces default limits on instance counts per instance type per region. Requesting a quota increase via the Service Quotas console is the correct action to raise the limit and allow the deployment of 6 instances.

Answer

Create two separate endpoint configurations, each with 3 instances, and distribute traffic between them.

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Use a different instance type, such as ml.m5.large, which has a higher limit.

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Delete unused endpoints to free up resources.

Question 19

A machine learning engineer needs to split a dataset for binary classification where the positive class represents only 2% of the data. Which data splitting strategy ensures that both training and test sets maintain the same class proportion as the original dataset?

Accepted Answer

Stratified sampling based on the target variable. Stratified splitting preserves the original class distribution in each split, which is critical for imbalanced datasets.

Answer

Time-series split respecting the timestamp order

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Simple random split with a 80/20 ratio

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K-fold cross-validation without stratification

Question 20

A machine learning team is setting up Model Monitor for a deployed model. Which THREE factors should they consider when configuring the monitoring schedule? (Select three.)

Accepted Answer

The monitoring job can be configured to send notifications via Amazon SNS.. Amazon SageMaker Model Monitor can be configured to send notifications via Amazon SNS when monitoring violations are detected. This allows the team to proactively respond to data drift or quality issues without manually polling the monitoring results.

Answer

The frequency of monitoring should be at least daily.

Answer

The constraints file must be updated after each monitoring run.