MLA-C01 Practice Test 26 — 15 Questions

Question 1

During a blue/green deployment of a SageMaker endpoint, the team notices that traffic is not being fully shifted to the new variant after the update. The endpoint has two variants with equal initial weights (50% each). The team wants to shift 100% traffic to the new variant. What is the most likely cause?

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

Accepted Answer

The new variant's model container is failing health checks, so traffic is not routed to it. SageMaker endpoints route traffic only to variants that pass health checks. If the new variant's model container fails health checks (e.g., due to a misconfigured inference script or incompatible dependencies), SageMaker will not send any traffic to it, regardless of the weight setting. This explains why traffic remains stuck at 50% on the old variant despite the intended shift to 100%.

Answer

The new variant is using a different instance type that is not supported in the same endpoint

Answer

The new variant's weight was set to 100 but the maximum weight per variant is 50

Answer

The endpoint's load balancer is misconfigured and not forwarding traffic to the new variant

Question 2

A machine learning engineer is using SageMaker to train a model with the built-in LightGBM algorithm. The engineer wants to use early stopping to prevent overfitting. The training job is configured with a validation dataset. Which hyperparameter should be set to enable early stopping?

Accepted Answer

early_stopping_rounds. In SageMaker's built-in LightGBM algorithm, the hyperparameter `early_stopping_rounds` controls early stopping. When a validation dataset is provided, training will stop if the evaluation metric does not improve for the specified number of consecutive rounds, preventing overfitting.

Answer

num_iterations

Answer

early_stopping

Answer

num_boost_round

Question 3

A data scientist is training a large model on SageMaker and wants to reduce training time by using multiple GPUs. The model is small enough to fit on a single GPU but training is slow. Which SageMaker feature should be used?

Accepted Answer

Data parallelism using SageMaker's Distributed Data Parallel. SageMaker's Distributed Data Parallel (DDP) is the correct choice because it splits the mini-batch across multiple GPUs, allowing each GPU to hold a copy of the model and process a subset of the data simultaneously. This reduces training time for models that fit on a single GPU by leveraging data parallelism, where gradients are synchronized across GPUs after each step.

Answer

Use a larger instance with more vCPUs

Answer

Model parallelism using SageMaker's Model Parallel

Answer

Use Elastic Inference

Question 4

A company wants to deploy a PyTorch model on SageMaker using the NVIDIA Triton Inference Server for GPU acceleration. They have an existing Triton configuration. Which approach should they take?

Accepted Answer

Use the SageMaker Triton Inference Server container from the Deep Learning Containers. AWS provides a pre-built SageMaker Triton Inference Server container as part of the Deep Learning Containers (DLCs), which is optimized for GPU acceleration and supports the existing Triton configuration without modification. This container integrates directly with SageMaker hosting endpoints, enabling seamless deployment of PyTorch models with Triton's features like dynamic batching and model concurrency.

Answer

Use SageMaker Neo to compile the model for Triton

Answer

Package Triton as a custom container and use SageMaker batch transform

Answer

Use the standard SageMaker PyTorch container and install Triton at runtime

Question 5

A data engineer needs to ingest streaming data from IoT devices into Amazon S3 for machine learning. The data arrives continuously and must be available for querying within minutes. Which service should be used to collect and deliver the streaming data to S3?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed to ingest streaming data in real time and automatically deliver it to destinations like Amazon S3 with near-real-time latency (typically 60 seconds minimum buffering). It handles data transformation, compression, and partitioning without requiring custom code, making it ideal for continuously arriving IoT data that must be queryable within minutes.

Answer

AWS Database Migration Service (DMS)

Answer

AWS Glue ETL job triggered by an event

Answer

Amazon S3 Transfer Acceleration

Question 6

A data scientist is preparing text data for a sentiment analysis model using Amazon SageMaker. Which two data preprocessing techniques are commonly used when working with text data for natural language processing? (Choose two.)

Accepted Answer

Tokenization. Tokenization is correct because it is a fundamental preprocessing step that splits raw text into smaller units (tokens), such as words or subwords, which are necessary for converting text into a structured format that machine learning models can process. Stop word removal is correct because it filters out common words (e.g., 'the', 'and', 'is') that carry little semantic meaning, reducing noise and improving model performance in sentiment analysis.

Answer

One-hot encoding of all words

Answer

Image resizing

Answer

Principal component analysis (PCA)

Question 7

A company wants to secure access to a SageMaker real-time endpoint. Which TWO actions should be taken? (Select two.)

Accepted Answer

Attach a resource-based policy to the endpoint.. SageMaker real-time endpoints support resource-based policies, which allow you to control access at the endpoint level by specifying which IAM principals can invoke the endpoint. This is similar to how you attach a policy to an S3 bucket or an SQS queue, providing granular access control without requiring the caller to assume a role.

Answer

Use an IAM role with sts:AssumeRole for invocation.

Answer

Enable AWS WAF on the endpoint.

Answer

Use AWS CloudTrail to log all invocations.

Question 8

A team is deploying a real-time inference endpoint in SageMaker. The model requires access to an S3 bucket containing customer data, which is encrypted with SSE-KMS. The team needs to ensure that the endpoint can decrypt the data. Which IAM role configuration is necessary?

Accepted Answer

Add kms:Decrypt permission to the SageMaker execution role for the specific KMS key.. The SageMaker execution role must have the kms:Decrypt permission for the specific KMS key that encrypted the S3 objects. When the endpoint reads data from the S3 bucket, SageMaker uses its execution role to call KMS to decrypt the data. Without this permission, the endpoint will fail with an access denied error, even if the S3 bucket policy allows s3:GetObject.

Answer

Add kms:GenerateDataKey permission to the SageMaker execution role.

Answer

Attach a policy to the S3 bucket granting s3:GetObject to the KMS key.

Answer

Configure the endpoint to assume the S3 bucket's IAM role.

Question 9

A machine learning engineer observes that a SageMaker training job fails with the error shown in the exhibit. What is the most likely cause of the failure?

Accepted Answer

The SageMaker execution role does not have an IAM policy that grants read access to the S3 bucket containing the training data.. The error shown in the exhibit is a standard SageMaker access-denied error, which occurs when the SageMaker execution role lacks the necessary IAM permissions to read the training data from the S3 bucket. SageMaker uses the execution role's IAM policy to determine access to S3 resources; without a policy granting s3:GetObject (and optionally s3:ListBucket) on the bucket and objects, the training job fails at the data-loading stage.

Answer

The training data is stored in an unsupported format like Parquet.

Answer

The training job is using an incorrect AWS Region for the S3 bucket.

Answer

The VPC configuration prevents the training job from reaching the S3 bucket.

Question 10

A team wants to use AWS Step Functions to orchestrate a retraining workflow that is triggered when new data arrives in an S3 bucket. They also need to monitor model drift. Which event-driven approach should they use?

Accepted Answer

Configure EventBridge to capture S3 PutObject events and target an AWS Step Functions state machine that runs the retraining pipeline. AWS EventBridge can capture S3 PutObject events (via S3's default event notifications or a more granular EventBridge rule) and directly target a Step Functions state machine as a target. This creates a fully event-driven, serverless orchestration for the retraining pipeline without polling or custom code. Step Functions then coordinates the retraining steps, including model drift monitoring, in a reliable and auditable manner.

Answer

Use a cron-based Step Function schedule that checks for new data every hour

Answer

Set up an S3 event notification to invoke a Lambda function that starts a SageMaker training job directly

Answer

Use SageMaker Pipelines with a schedule trigger

Question 11

A machine learning team wants to detect concept drift in a production model. Which TWO actions should they take? (Choose TWO)

Accepted Answer

Enable data capture on the endpoint to collect ground truth labels. Concept drift is detected by comparing model predictions to actual outcomes (ground truth). Capturing ground truth and using model quality monitoring is essential. Data quality monitoring would not detect concept drift.

Answer

Set up SageMaker Model Monitor data quality monitoring schedule

Answer

Use SageMaker Clarify for feature attribution drift

Answer

Enable daily retraining to automatically correct drift

Question 12

A company runs an online retail business and wants to build a product recommendation system. They have a dataset of customer purchases stored in Amazon S3 as CSV files. The dataset includes columns: 'customer_id', 'product_id', 'purchase_date', 'quantity', 'price', and 'category'. The data science team plans to use Amazon SageMaker to train a factorization machines model. During data exploration, they discover that the 'category' column has 1,200 unique values, and many categories appear only a few times. The 'product_id' column has 50,000 unique values. They want to include both features in the model. The team is concerned about the high cardinality of these features. Which approach should they take to prepare these features for the factorization machines model?

Accepted Answer

Encode both columns as integer indices and feed them directly to the factorization machines algorithm as categorical features.. Amazon SageMaker's factorization machines algorithm natively supports categorical features encoded as integer indices (0-based). This avoids the explosion of features from one-hot encoding (which would create 51,200 columns) and leverages the algorithm's ability to learn interactions between high-cardinality features via factorized parameters, making it both memory-efficient and effective for sparse data.

Answer

Apply one-hot encoding to both 'product_id' and 'category' columns.

Answer

Drop the 'category' column and only use 'product_id' since it has more granularity.

Answer

Apply principal component analysis (PCA) to reduce the dimensionality of the categorical features.

Question 13

A data scientist needs to label a large dataset of product images for a classification model. They want to reduce labeling costs by prioritizing uncertain samples. Which Amazon SageMaker Ground Truth feature should they use?

Accepted Answer

Active learning. Active learning in Amazon SageMaker Ground Truth automatically selects the most uncertain or informative samples from the unlabeled dataset and sends them to human annotators. This prioritization reduces labeling costs by focusing budget on the samples that will most improve model performance, rather than labeling all data indiscriminately.

Answer

Pre-built worker templates

Answer

Consolidated labeling

Answer

Automated data labeling

Question 14

A data engineer needs to assess the quality of a dataset containing customer information. The dataset has missing values, outliers, and duplicate records. Which TWO AWS services can be used to perform data quality assessment? (Select TWO.)

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew is a visual data preparation tool that provides built-in data quality checks, including profiling, anomaly detection, and duplicate identification, without writing code. It directly addresses the need to assess missing values, outliers, and duplicates through its data quality dashboard and transformation recipes.

Answer

Amazon Athena

Answer

AWS Glue ETL

Answer

Amazon QuickSight

Question 15

A data science team uses SageMaker to train and deploy models. They need to track model lineage, including datasets, training jobs, and model versions, to ensure reproducibility. Which THREE actions should they take? (Select THREE)

Accepted Answer

Enable SageMaker ML Lineage Tracking. A is correct because SageMaker ML Lineage Tracking automatically captures the relationships between datasets, training jobs, and model versions, creating a directed acyclic graph (DAG) of the ML workflow. This enables full reproducibility by allowing you to trace which data and code produced a specific model, without manual intervention.

Answer

Store trained models in a public S3 bucket

Answer

Use SageMaker Experiments to organize training runs