MLA-C01 Practice Test 4 — 25 Questions

Question 1

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?

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

Accepted Answer

ml.p4d.24xlarge. 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.

Answer

ml.trn1.32xlarge

Answer

ml.c5.18xlarge

Answer

ml.g4dn.12xlarge

Question 2

A machine learning engineer uses Amazon SageMaker Data Wrangler to preprocess a dataset. After applying a transform, the engineer wants to export the data to a feature group in Amazon SageMaker Feature Store for reuse in training and inference. Which export option should they choose?

Accepted Answer

Export to Amazon SageMaker Feature Store. SageMaker Data Wrangler can export directly to a feature group in Feature Store, making the features available for both training (offline) and inference (online).

Answer

Export to Amazon DynamoDB

Answer

Export to Amazon S3 as CSV

Answer

Export to Amazon SageMaker Pipelines

Question 3

A company wants to deploy its trained model to edge devices such as cameras and IoT devices. The model must run efficiently with low latency and minimal memory footprint. Which THREE actions should the company take to prepare the model for edge deployment? (Choose THREE.)

Accepted Answer

Use SageMaker Edge Manager to package and manage the model on devices.. SageMaker Edge Manager is purpose-built to package, optimize, and manage machine learning models on edge devices. It provides model packaging, runtime monitoring, and over-the-air updates, ensuring the model runs efficiently with low latency and minimal memory footprint on resource-constrained hardware like cameras and IoT devices.

Answer

Increase the model's complexity to improve accuracy on edge devices.

Answer

Deploy the model directly as a SageMaker endpoint and have the edge devices call it over the internet.

Question 4

A data engineer is building a feature engineering pipeline in AWS Glue ETL to process streaming data from Amazon Kinesis. The data includes a nested JSON structure with arrays. The engineer needs to flatten the nested structures into a tabular format for machine learning. Which THREE approaches are valid for this task? (Choose 3.)

Accepted Answer

Use Python's json.loads in a map function. Python's json.loads can be used within a PySpark map function to parse nested JSON strings from streaming data in AWS Glue ETL. This allows you to extract and flatten nested fields into a tabular structure by iterating over each record and converting the JSON into a flat dictionary, which can then be mapped to DataFrame columns.

Answer

Use Athena's UNNEST function on the raw data

Answer

Use Amazon SageMaker Processing with scikit-learn

Question 5

A company needs to deploy a new model version to a SageMaker real-time endpoint. They want to route 5% of traffic to the new version initially to monitor for errors before full rollout. Which deployment strategy should they use?

Accepted Answer

Canary deployment with production variants. A canary deployment with production variants allows you to route a specific percentage of traffic (e.g., 5%) to the new model version by adjusting the `InitialVariantWeight` parameter in the production variant configuration. This enables gradual traffic shifting while monitoring errors, and you can later increase the weight to 100% for full rollout. SageMaker real-time endpoints support this natively by hosting multiple model variants behind the same endpoint.

Answer

Blue/green deployment

Answer

Shadow testing

Answer

Multi-model endpoint

Question 6

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?

Accepted Answer

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

Answer

Use a single large GPU instance with provisioned concurrency.

Answer

Use a serverless endpoint with GPU support.

Answer

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

Question 7

A team is building a regression model on a dataset with missing values in multiple features. They decide to use a k-Nearest Neighbors (k-NN) imputer. The dataset has 100,000 rows and 50 features. Which step should the team take to ensure the imputation is efficient and accurate?

Accepted Answer

Standardize the features before applying k-NN imputation. Standardizing features before applying k-NN imputation is critical because k-NN relies on distance calculations (e.g., Euclidean distance). If features are on different scales (e.g., one feature ranges 0–1 and another 0–100,000), the distance metric will be dominated by the larger-scale feature, leading to biased neighbor selection and inaccurate imputation. Standardization (e.g., z-score scaling) ensures each feature contributes equally to the distance computation, improving both efficiency and accuracy.

Answer

Set k=1 to minimize bias

Answer

Use all 100,000 rows to find neighbors for each missing value

Answer

Use only the feature with missing values to find neighbors

Question 8

A data scientist is training a linear learner model using SageMaker and notices that the loss is not decreasing. They suspect the issue is exploding gradients. Which SageMaker Debugger rule should they enable to monitor this?

Accepted Answer

ExplodingGradients. The ExplodingGradients rule tracks gradient values and alerts if they exceed a threshold, which is the correct detection for exploding gradients.

Answer

LossNotDecreasing

Answer

VanishingGradients

Answer

Overfit

Question 9

A machine learning engineer is building a pipeline to ingest data from multiple sources into an Amazon S3 data lake. The data includes both batch files and real-time streams. The engineer needs to catalog the data for discovery and querying. Which TWO AWS services should the engineer use to ingest and catalog the data? (Select TWO.)

Accepted Answer

Amazon S3. S3 is the data lake storage; AWS Glue Data Catalog stores metadata for discovery and querying. AWS Glue ETL can ingest batch data; Kinesis Firehose ingests streaming data. Athena is for querying, not ingestion or cataloging; Redshift is a data warehouse.

Answer

Amazon Redshift

Answer

Amazon Athena

Answer

AWS Glue ETL

Question 10

A data scientist is using Amazon SageMaker Data Wrangler to create a data flow for a machine learning project. The source data is in Amazon S3 and contains PII (personally identifiable information) such as email addresses and credit card numbers. The data scientist needs to prepare the data for training while ensuring compliance with data privacy regulations. Which THREE actions should the data scientist take? (Select THREE.)

Accepted Answer

Use Data Wrangler to redact or remove PII columns from the dataset before training.. Amazon SageMaker Data Wrangler provides built-in transforms to redact or remove PII columns, which directly addresses compliance requirements by eliminating sensitive data from the training dataset. This is a straightforward and effective method to prevent PII from being used in model training, reducing the risk of data exposure.

Answer

Include the raw PII in the training dataset and rely on the model to not memorize it.

Answer

Use AWS Glue to copy the data to a separate bucket without any transformations.

Question 11

A company requires that all SageMaker notebook instances be created within a private VPC without internet access. Which configuration step is mandatory?

Accepted Answer

Configure VPC settings when creating the notebook instance, choosing a private subnet.. When creating a SageMaker notebook instance, you must explicitly configure the VPC settings and select a private subnet to ensure the instance is launched within a private VPC without internet access. This is mandatory because SageMaker notebook instances, by default, are created with internet access enabled unless you specify a VPC with no direct internet access. Selecting a private subnet ensures the instance uses only VPC endpoints or NAT gateways for outbound traffic, meeting the no-internet requirement.

Answer

Use a SageMaker Studio notebook instead.

Answer

Enable SageMaker direct internet access.

Answer

Assign a public IP to the notebook instance.

Question 12

Refer to the exhibit. A SageMaker training job failed. Based on the error message, which action should the engineer take?

Accepted Answer

Use a larger instance type. The error message indicates that the training job failed due to insufficient memory (an out-of-memory error). Using a larger instance type (Option B) provides more RAM, which directly resolves the memory exhaustion issue. SageMaker training jobs run on EC2 instances, and the instance type determines the available memory and compute resources.

Answer

Change the algorithm

Answer

Increase the volume size

Answer

Increase the instance count

Question 13

A machine learning engineer is building a pipeline to preprocess text data for a sentiment analysis model. The data consists of customer reviews. The engineer wants to convert the text into numerical features while preserving the semantic meaning of words. Which technique should be used?

Accepted Answer

Word embeddings (e.g., Word2Vec or GloVe). Word embeddings (like Word2Vec or GloVe) are dense vector representations that capture semantic relationships between words based on their context in a large corpus. For sentiment analysis, preserving semantic meaning (e.g., 'good' and 'excellent' having similar vectors) is critical, and embeddings directly encode this, unlike sparse or count-based methods.

Answer

One-hot encoding of each word

Answer

Bag-of-words with TF-IDF

Answer

Hashing vectorizer

Question 14

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?

Accepted Answer

LoRA. 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.

Answer

Full fine-tuning

Answer

Instruction tuning

Answer

RLHF

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. Enabling data capture on the SageMaker endpoint is a prerequisite for monitoring data drift and model quality. Data capture automatically records input requests and output responses from the endpoint, which SageMaker Model Monitor later analyzes against a baseline to detect drift. Without data capture, there is no data to compare against the baseline constraints.

Answer

Deploy a shadow endpoint for comparison

Answer

Use SageMaker Debugger for monitoring

Question 16

A data science team deployed a model on Amazon SageMaker and enabled Model Monitor to detect data drift. After a week, they receive alerts indicating that the distribution of a key feature has shifted significantly. However, the model's accuracy on the recent production data remains high. Which action should the team take next?

Accepted Answer

Investigate the root cause of the drift as it may be benign or may lead to future degradation.. Data drift does not always immediately impact model accuracy; the drift may be benign (e.g., a shift in a non-predictive feature) or may indicate a precursor to future degradation. Amazon SageMaker Model Monitor detects distribution shifts using statistical tests like Kolmogorov-Smirnov or Chi-squared, but the team must investigate the root cause—such as changes in data collection, seasonal patterns, or upstream pipeline issues—before taking corrective action. Disabling alerts or retraining blindly could mask underlying problems or waste resources.

Answer

Disable the data drift alert since accuracy is not affected.

Answer

Increase the sample size for monitoring to reduce false positives.

Answer

Retrain the model immediately because data drift always degrades performance.

Question 17

Refer to the exhibit. A data engineer deploys this Glue job via CloudFormation. When running, the job fails with a timeout after 2 hours. The job processes a large dataset and expected to take 3 hours. Which change would resolve the issue?

Accepted Answer

Increase the Timeout property to 240 minutes. The Glue job failed due to a timeout after 2 hours, but the expected runtime is 3 hours. The default timeout for AWS Glue jobs is 2880 minutes (48 hours), but the CloudFormation template likely set a lower value. Increasing the Timeout property to 240 minutes (4 hours) provides enough time for the job to complete without being prematurely terminated.

Answer

Increase NumberOfWorkers to 20

Answer

Set MaxRetries to 3

Answer

Change WorkerType to G.2X

Question 18

A team is migrating their ML infrastructure to AWS and wants to use infrastructure as code to manage SageMaker Studio domains, user profiles, and associated resources. Which services can they use for this purpose? (Select THREE.)

Accepted Answer

AWS CDK (Cloud Development Kit). AWS CDK (Cloud Development Kit) is correct because it allows you to define AWS infrastructure, including SageMaker Studio domains and user profiles, using familiar programming languages like Python or TypeScript. CDK synthesizes these definitions into CloudFormation templates, enabling infrastructure as code (IaC) for SageMaker resources. This approach provides type safety and high-level abstractions, making it suitable for managing complex ML environments.

Answer

SageMaker Python SDK

Answer

Boto3

Question 19

A company needs to update a model in production without any downtime. They currently have a single real-time endpoint serving traffic. Which approach allows them to deploy a new model version and switch traffic gradually while being able to roll back quickly?

Accepted Answer

Use a canary deployment by creating a new production variant with the new model and shifting traffic incrementally. SageMaker supports production variants with traffic splitting. By creating a new variant with the new model and shifting traffic gradually, the old variant remains available for rollback. Blue/green deployment with a new endpoint and endpoint configuration swap also allows quick rollback. The key is to have both variants active during the transition.

Answer

Use a multi-model endpoint and replace the model file

Answer

Stop the endpoint, update the model, and restart the endpoint

Answer

Update the existing endpoint's model directly using UpdateEndpoint

Question 20

A team is training a deep learning model on Amazon SageMaker using a custom Docker container. Which three practices should they follow to optimize training performance? (Choose three.)

Accepted Answer

Store training data in Amazon S3 in a shuffled and compressed format. Storing training data in Amazon S3 in a shuffled and compressed format (Option A) optimizes training performance because shuffling prevents biased gradient updates during stochastic gradient descent, while compression reduces I/O overhead and network transfer time. SageMaker's Pipe mode can then stream this compressed data directly to the training algorithm without intermediate disk writes, further accelerating throughput.

Answer

Use the largest instance type available

Answer

Increase the number of layers in the model to improve accuracy