MLA-C01 Practice Test 23 — 15 Questions

Question 1

A financial services company deploys a fraud detection model with a SageMaker endpoint. They need to ensure that all data sent to the endpoint is encrypted in transit and at rest, and that the endpoint cannot be accessed from the public internet. Which combination of settings should they use?

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

Accepted Answer

Enable VPC-only mode, inter-container traffic encryption, and use a KMS key for endpoint encryption. VPC-only mode ensures the endpoint is private. Inter-container traffic encryption is needed for data in transit between containers in multi-model endpoints. KMS encryption secures data at rest on the instance storage.

Answer

Use endpoint data encryption with an AWS managed key and enable public endpoint access

Answer

Enable inter-container traffic encryption and disable VPC-only mode

Answer

Deploy the endpoint in a private subnet without SageMaker VPC-only mode

Question 2

A team wants to secure SageMaker endpoints for a healthcare application. They must ensure data is encrypted at rest and in transit, and that the endpoint can only be accessed from within a VPC. Which THREE steps should they take? (Select THREE)

Accepted Answer

Use AWS KMS to encrypt the model artifacts and endpoint data

Answer

Store encryption keys in a public S3 bucket

Answer

Set the endpoint to use network isolation mode

Question 3

A company deploys a real-time inference endpoint with auto-scaling using a target tracking policy based on average Invocations per instance. They notice that during a traffic spike, the endpoint scales out too late, causing increased latency. They want to scale proactively before the spike. Which strategy should they implement?

Accepted Answer

Use a scheduled scaling action to add capacity before the expected spike. Scheduled scaling with Application Auto Scaling can anticipate traffic spikes. Pre-warming endpoints or using provisioned concurrency are not native SageMaker features. Step scaling reacts to deviations but still after the fact.

Answer

Enable provisioned concurrency on the endpoint

Answer

Pre-warm the endpoint by sending dummy requests

Answer

Switch to a step scaling policy with a higher cooldown period

Question 4

A data science team wants to automate the retraining of a model whenever SageMaker Model Monitor detects a significant drift in data quality. They need the least amount of custom code. Which approach should they use?

Accepted Answer

Set up a CloudWatch Alarm on the Model Monitor violation metric that sends a notification to an SNS topic, which triggers an AWS Lambda function to start a SageMaker pipeline. CloudWatch Alarms can be set on Model Monitor violation metrics. The alarm triggers an SNS topic that invokes a Lambda function to start a retraining pipeline. This requires minimal custom code.

Answer

Write a custom script on an EC2 instance that polls the Model Monitor endpoint every hour and triggers retraining if drift is detected

Answer

Configure an Amazon EventBridge rule that listens for Model Monitor violation events and directly triggers a SageMaker Pipeline execution

Answer

Use SageMaker Autopilot to automatically retrain the model when performance degrades

Question 5

An ML engineer needs to monitor the operational health of a SageMaker endpoint, specifically the time taken for the container to process an inference request and the overhead added by SageMaker. Which two CloudWatch metrics should they examine?

Accepted Answer

ModelLatency and OverheadLatency. ModelLatency is the time taken by the model to respond, and OverheadLatency is the additional time added by SageMaker infrastructure. Invocations is count, not duration; Latency is total latency (ModelLatency + OverheadLatency).

Answer

ModelLatency and 4XXError

Answer

Latency and 5XXError

Answer

Invocations and Latency

Question 6

A machine learning team needs to monitor a deployed model for both data drift and concept drift. Which TWO approaches should they implement? (Select TWO.)

Accepted Answer

Set up SageMaker Model Monitor for data quality monitoring. SageMaker Model Monitor can be configured for data quality (data drift) and model quality (concept drift) monitoring. Data drift monitors input distribution changes, while model quality monitors prediction accuracy against ground truth.

Answer

Use SageMaker Clarify for bias monitoring

Answer

Configure CloudWatch Logs Insights to query inference logs

Answer

Enable SageMaker Debugger during inference

Question 7

A company uses SageMaker Inference Recommender to select the optimal endpoint configuration. After running the recommender, they receive a recommendation for a specific instance type and initial instance count. What should they do next to optimize costs over time?

Accepted Answer

Set up auto-scaling with a target tracking policy based on the recommended metric. SageMaker Inference Recommender provides a baseline configuration. To optimize costs, they should apply auto-scaling with a target tracking policy based on the recommended metric, such as invocation count or latency.

Answer

Use the recommended configuration without changes, as it is already optimal

Answer

Purchase a Savings Plan for the recommended instance type to reduce hourly cost

Answer

Manually adjust the instance count daily based on observed traffic

Question 8

A machine learning engineer is setting up model quality monitoring for a binary classification model. They have ground truth labels available in Amazon S3. Which TWO steps are required to configure model quality monitoring? (Choose two.)

Accepted Answer

Create a schedule for the monitoring job to run at regular intervals. A baseline must be computed from training data and ground truth, and a schedule for monitoring jobs must be defined. The monitoring job then compares production predictions against ground truth.

Answer

Create a CloudWatch Alarm for accuracy degradation

Answer

Use Amazon SageMaker Clarify for bias detection

Answer

Enable data capture on the endpoint to collect predictions

Question 9

A team is monitoring a SageMaker endpoint and notices that the average latency (ModelLatency) is increasing over time, but the number of invocations is steady. They suspect that the model's inference code is becoming slower due to memory leaks. Which metric should they also examine to confirm this hypothesis?

Accepted Answer

MemoryUtilization metric (if custom). SageMaker publishes memory utilization metrics if the container emits them via CloudWatch. A memory leak would show increasing memory usage over time, correlating with increasing latency.

Answer

Invocations metric

Answer

OverheadLatency metric

Answer

5XXError metric

Question 10

A company uses SageMaker Model Monitor to detect bias drift in their real-time inference endpoint. They have collected ground truth labels and want to monitor for bias across different demographic groups. Which type of monitoring should they configure?

Accepted Answer

SageMaker Clarify – Bias Drift Monitoring. SageMaker Clarify offers post-deployment bias monitoring that uses ground truth labels to compute bias metrics (e.g., difference in positive outcome rates) over time.

Answer

SageMaker Model Monitor – Feature Attribution Drift Monitoring

Answer

SageMaker Model Monitor – Model Quality Monitoring

Answer

SageMaker Model Monitor – Data Quality Monitoring

Question 11

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. Using pre-trained word embeddings (e.g., multilingual BERT or FastText) captures semantic meaning across languages without requiring separate pipelines. TF-IDF would produce separate sparse vectors for each language, not capturing cross-lingual similarity.

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 12

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.)

Accepted Answer

Apply SMOTE (Synthetic Minority Oversampling Technique) to the training data using a preprocessing script in SageMaker Processing.. SMOTE generates synthetic samples for the minority class by interpolating between existing minority instances, which directly addresses the class imbalance by creating a more balanced training set. This increases the model's exposure to positive examples, improving recall and precision, and thus the F1 score. Using SageMaker Processing allows this preprocessing step to be integrated into the ML pipeline efficiently.

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 13

A data science team deploys a real-time inference endpoint on Amazon SageMaker. They want to monitor for data drift in the input features over time. Which AWS service should they use to capture and analyze the input data distribution?

Accepted Answer

Amazon SageMaker Model Monitor. Amazon SageMaker Model Monitor is the correct service because it is specifically designed to continuously monitor machine learning models in production for data drift and quality issues. It automatically captures input data distributions from real-time inference endpoints and compares them against a baseline to detect statistical changes, alerting the team when drift occurs.

Answer

Amazon Athena

Answer

AWS CloudTrail

Answer

Amazon CloudWatch Logs

Question 14

A data science team needs to deploy a PyTorch model for real-time inference with low latency. The model requires GPU acceleration. Which SageMaker endpoint configuration should they use?

Accepted Answer

Create a real-time endpoint using an ml.p3.2xlarge instance. Real-time SageMaker endpoints with GPU instances like ml.p3.2xlarge are specifically designed for low-latency, synchronous inference with GPU acceleration. PyTorch models requiring GPU must use instance types that support NVIDIA CUDA, and the ml.p3 family provides the necessary GPU compute for real-time predictions.

Answer

Create a multi-model endpoint using ml.m5.large instances

Answer

Create a serverless endpoint with memory set to 6144 MB

Answer

Create a batch transform job using an ml.c5.xlarge instance

Question 15

Refer to the exhibit. A SageMaker Processing job fails with the following error log. Which change during data preparation would resolve the issue?

Accepted Answer

Modify the preprocessing script to cast 'age' to float using astype(float). The error log indicates a type mismatch when processing the 'age' column, likely due to mixed data types (e.g., strings and numbers) in a column expected to be numeric. By explicitly casting the column to float using astype(float) in the preprocessing script, you ensure consistent numeric type handling, which resolves the failure during SageMaker Processing job execution.

Answer

In SageMaker Data Wrangler, set the 'age' column type to 'number'

Answer

Drop rows with missing values in the 'age' column before training

Answer

Remove the 'age' column from the dataset entirely