AWS Certified Machine Learning Engineer Associate MLA-C01 MLA-C01 Questions 826–835 | Page 12/12

826

MCQhard

A company deploys a model with SageMaker and wants to monitor for concept drift. They have noticed that the relationship between input features and the target variable has changed, causing model accuracy to degrade. However, the input data distribution remains stable. Which type of drift is this, and what is the most appropriate response strategy?

A.Concept drift; ignore the change as long as input distribution remains stable

B.Data drift; update the baseline statistics and continue monitoring

C.Concept drift; retrain the model with newly collected labeled data

D.Data drift; retrain the model with the latest training data

AnswerC

Concept drift is a change in P(y|x). Retraining with recent labeled data adjusts the model to the new relationship.

Why this answer

This is concept drift because the relationship between input features and the target variable has changed while the input data distribution remains stable. The most appropriate response is to retrain the model with newly collected labeled data that reflects the current relationship, as concept drift requires updating the model's learned mapping from features to labels.

Exam trap

The trap here is that candidates confuse concept drift with data drift, assuming any drift requires updating baseline statistics, when in fact concept drift demands retraining with fresh labeled data to realign the model with the new feature-target relationship.

How to eliminate wrong answers

Option A is wrong because ignoring concept drift will cause continued model accuracy degradation, even if the input distribution is stable; concept drift directly impacts predictive performance. Option B is wrong because this is not data drift (input distribution is stable), and updating baseline statistics would not address the changed feature-target relationship. Option D is wrong because data drift refers to changes in input data distribution, not the feature-target relationship, so retraining with the latest training data under the assumption of data drift is a misdiagnosis.

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827

MCQeasy

A company wants to audit all API calls made to SageMaker endpoints for security compliance. Which AWS service should they enable?

A.AWS CloudTrail

B.Amazon GuardDuty

C.AWS Config

D.AWS CloudTrail

AnswerA, D

Correct. CloudTrail records all API calls to SageMaker endpoints, providing a full audit trail for security compliance.

Why this answer

AWS CloudTrail is the correct service because it records all API calls made to SageMaker endpoints, including the caller identity, time, source IP, and request parameters. This provides a complete audit trail for security compliance, enabling analysis of who made what changes and when. CloudTrail is specifically designed for governance, compliance, and operational auditing of AWS API activity.

Note that both options A and D list 'AWS CloudTrail' and are therefore both correct. This duplication is an artifact in the question design but does not change the fact that CloudTrail is the right service.

Exam trap

The trap here is that candidates may confuse GuardDuty's threat detection capabilities with CloudTrail's auditing function, or mistakenly think AWS Config's configuration tracking includes API call logging. Additionally, be aware that duplicate correct options (A and D) may appear in some questions; both are valid selections.

How to eliminate wrong answers

Option B (Amazon GuardDuty) is wrong because it is a threat detection service that monitors for malicious activity using machine learning and threat intelligence, not a service that records API calls for auditing. Option C (AWS Config) is wrong because it evaluates resource configurations against desired policies and tracks configuration changes, but it does not capture API-level call details. Option D (AWS CloudTrail) is actually the same as the correct answer, but the question lists it as a duplicate option; the correct choice is the first instance of CloudTrail.

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828

Multi-Selecteasy

A data engineer is using SageMaker Pipelines to automate data preparation. Which TWO statements about data validation within a pipeline are correct?

Select 2 answers

A.The pipeline can be configured to fail if data quality checks do not meet thresholds

B.SageMaker Pipelines has a built-in 'CheckDataQuality' step for data validation

C.Data validation can only be performed on training data, not inference data

D.Data validation steps cannot pass results to subsequent steps

E.Data validation requires a trained model to evaluate predictions

AnswersA, B

You can set conditions to fail the pipeline.

Why this answer

SageMaker Pipelines allows you to define conditions that evaluate the output of data quality checks (e.g., using Amazon SageMaker Model Monitor or custom validation scripts). If the checks fail to meet specified thresholds (e.g., missing values exceed 5%), the pipeline can be configured to fail, stopping execution and preventing downstream steps from processing invalid data.

Exam trap

The trap here is that candidates assume data validation requires a trained model or is limited to training data, but SageMaker Pipelines supports rule-based validation on any dataset, including inference data, without needing a model.

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829

MCQmedium

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?

A.AUC

B.Confusion matrix

C.F1 score

D.Mean Absolute Error (MAE)

AnswerD

MAE provides the average absolute difference between predictions and actuals, directly indicating average error magnitude.

Why this answer

MAE (Mean Absolute Error) gives the average absolute prediction error, which is easy to interpret in the same units as the target. RMSE gives a similar but squared metric, and R² indicates variance explained, but MAE directly answers the average error magnitude.

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830

MCQmedium

A team is deploying a machine learning model using Amazon SageMaker. They need to serve predictions with sub-100ms latency for a real-time application. The model is a large ensemble that requires 4 GB of memory. The team expects traffic of 100 requests per second initially, but it may double during peak hours. Which instance type and deployment configuration should the team choose to minimize cost while meeting the latency requirement?

A.Deploy on one ml.c5.large instance with an Application Auto Scaling target tracking policy based on memory utilization

B.Deploy on one ml.t2.medium instance with an Application Auto Scaling target tracking policy based on CPU utilization

C.Deploy on one ml.p3.2xlarge instance with provisioned concurrency

D.Deploy on two ml.m5.large instances behind a load balancer with manual scaling

AnswerA

ml.c5.large has 4 GB memory, suitable; one instance can handle 100 RPS; auto-scaling handles peak.

Why this answer

The ml.c5.large instance provides 4 GB of memory, which meets the model's requirement, and its compute-optimized nature ensures low-latency inference. Using Application Auto Scaling with a target tracking policy based on memory utilization allows the instance to scale out during traffic spikes (up to 200 requests per second) while minimizing cost by running a single instance during normal load.

Exam trap

The trap here is that candidates often choose GPU instances (like p3) for any 'large' model, but the question specifies memory and latency requirements, not GPU compute needs, and they overlook that burstable instances (t2) cannot sustain low latency under continuous load due to CPU credit exhaustion.

How to eliminate wrong answers

Option B is wrong because the ml.t2.medium instance has only 4 GB of memory but uses burstable CPU (t2 series), which cannot sustain sub-100ms latency under sustained load due to CPU credit exhaustion, especially at 100-200 requests per second. Option C is wrong because the ml.p3.2xlarge instance is a GPU-accelerated instance designed for training or high-throughput batch inference, not for real-time low-latency serving; it is over-provisioned and costly for this memory-bound ensemble model, and provisioned concurrency is a Lambda feature, not applicable to SageMaker. Option D is wrong because deploying two ml.m5.large instances (each with 8 GB memory) behind a load balancer with manual scaling is over-provisioned for the initial 100 requests per second, increasing cost unnecessarily, and manual scaling cannot dynamically handle peak traffic without manual intervention.

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831

MCQhard

A company is training a large Transformer model on SageMaker and wants to use model parallelism to fit the model into memory. The model has 10 billion parameters. Which instance type is MOST cost-effective for this task while supporting SageMaker's model parallelism?

A.ml.trn1.32xlarge

B.ml.c5.18xlarge

C.ml.g4dn.12xlarge

D.ml.p4d.24xlarge

AnswerD

P4d instances have high GPU memory and support model parallelism for large models.

Why this answer

The ml.p4d.24xlarge instances are optimized for large-scale distributed training with high memory and support SageMaker's model parallelism. ml.trn1 instances are designed for training with AWS Trainium, but they use a different chip architecture and may require specific SDKs. ml.g4dn instances are for inference and light training. ml.c5 instances are compute-optimized but lack GPU memory for large models.

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832

MCQmedium

A data scientist needs to normalize numeric features for a deep learning model. The features have different scales and distributions, and the model uses gradient descent. Which scaling method is MOST appropriate?

A.RobustScaler

B.MinMaxScaler

C.MaxAbsScaler

D.StandardScaler

AnswerD

StandardScaler centers and scales features, making gradient descent converge faster and more reliably.

Why this answer

StandardScaler standardizes features by removing the mean and scaling to unit variance, which works well with gradient descent even if features are not normally distributed. MinMaxScaler is sensitive to outliers.

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833

MCQhard

A company is deploying a real-time inference endpoint for a natural language processing model using Amazon SageMaker. The model requires GPU acceleration and must handle variable traffic patterns, including sudden spikes. The team wants to minimize costs while maintaining low latency during spikes. Which endpoint configuration strategy should they use?

A.Use a single large GPU instance with provisioned concurrency.

B.Use a serverless endpoint with GPU support.

C.Use a single GPU instance in multiple Availability Zones with an Application Load Balancer.

D.Use a multi-model endpoint on a GPU instance with Auto Scaling based on invocation count.

AnswerD

Multi-model endpoints share instances across models, and Auto Scaling adjusts capacity for spikes.

Why this answer

A multi-model endpoint on a GPU instance with Auto Scaling based on invocation count allows multiple models to share a single GPU, maximizing utilization and reducing cost. Auto Scaling based on invocation count dynamically adjusts the number of instances to handle traffic spikes while maintaining low latency, as it scales out quickly when the invocation count exceeds a threshold.

Exam trap

The trap here is that candidates assume serverless endpoints support GPU acceleration, but SageMaker serverless endpoints are CPU-only, making Option B invalid despite its cost-saving appeal.

How to eliminate wrong answers

Option A is wrong because a single large GPU instance with provisioned concurrency does not scale to handle sudden spikes; provisioned concurrency pre-warms instances but does not add more instances during a spike, leading to latency increases or throttling. Option B is wrong because serverless endpoints with GPU support are not available in SageMaker; serverless endpoints only support CPU instances, so they cannot meet the GPU acceleration requirement. Option C is wrong because using a single GPU instance in multiple Availability Zones with an Application Load Balancer does not provide horizontal scaling; it only adds redundancy across zones, but a single instance cannot handle spikes in traffic without Auto Scaling to add more instances.

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834

MCQhard

An organization needs to ensure that all data transmitted between containers in a SageMaker training job is encrypted. In the training job configuration, which setting should they enable?

A.Use a KMS key for data encryption

B.Configure the training job in VPC-only mode

C.Enable inter-container traffic encryption

D.Enable network isolation mode

AnswerC

This encrypts data in transit between containers in the same training job.

Why this answer

SageMaker training jobs support inter-container traffic encryption, which ensures that data transmitted between containers (e.g., distributed training workers) is encrypted in transit. This setting uses TLS to protect the communication channel, meeting the organization's requirement for encrypted data transmission between containers.

Exam trap

The trap here is that candidates often confuse encryption at rest (KMS keys) with encryption in transit, or assume VPC-only mode or network isolation automatically encrypts inter-container traffic, when in fact they only control network boundaries without enabling TLS encryption between containers.

How to eliminate wrong answers

Option A is wrong because using a KMS key for data encryption applies to data at rest (e.g., EBS volumes or S3 buckets), not to data in transit between containers. Option B is wrong because configuring the training job in VPC-only mode controls network access and routing but does not inherently encrypt inter-container traffic; it only restricts traffic to a VPC. Option D is wrong because enabling network isolation mode prevents the training job from accessing the internet but does not encrypt inter-container communication; it focuses on network segmentation, not encryption.

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835

MCQmedium

A data scientist wants to fine-tune a large language model for a question-answering task. They want to reduce memory usage during training by using a low-rank approximation of the weight updates. Which technique should they use?

A.Full fine-tuning

B.Instruction tuning

C.LoRA

D.RLHF

AnswerC

LoRA uses low-rank decomposition to update weights efficiently, reducing memory usage.

Why this answer

LoRA (Low-Rank Adaptation) adds low-rank matrices to model weights, significantly reducing memory footprint while achieving competitive performance. QLoRA adds quantization for further reduction.

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AWS Certified Machine Learning Engineer Associate MLA-C01 (MLA-C01) — Questions 826–835