MLA-C01 Practice Exam 51 — 20 Questions

Question 1

A team needs to deploy a new model version to production while minimizing risk. They want to route 5% of live traffic to the new model and 95% to the current model, and then gradually increase the new model's traffic. Which SageMaker deployment pattern should they use?

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

Accepted Answer

Canary deployment using production variants. Canary deployment uses production variants with weighted traffic allocation. By setting the new model variant to 5% and the current to 95%, and later adjusting weights, the team can gradually shift traffic. Blue/green is a full switch, and shadow testing duplicates traffic without affecting live responses.

Answer

Shadow testing

Answer

Blue/green deployment

Answer

A/B testing with production variants

Question 2

A machine learning engineer is preparing a dataset that contains both numerical and categorical features. The categorical features have high cardinality (e.g., zip code with thousands of unique values). Which technique is most appropriate for encoding these high-cardinality categorical features?

Accepted Answer

Target encoding. Target encoding is the most appropriate technique for high-cardinality categorical features because it replaces each category with the mean of the target variable for that category, effectively capturing the predictive signal while keeping the feature as a single numeric column. This avoids the dimensionality explosion of one-hot encoding and the arbitrary ordinality of label encoding, making it a common choice in gradient boosting frameworks like XGBoost or LightGBM for datasets with thousands of unique categories.

Answer

Label encoding

Answer

One-hot encoding

Answer

Frequency encoding

Question 3

A machine learning engineer needs to deploy a TensorFlow model that requires a custom inference environment with specific system libraries. The model will be used in a real-time application with variable traffic. They want to minimize cold start latency. Which SageMaker hosting option should they choose?

Accepted Answer

SageMaker real-time endpoint with a custom container. SageMaker real-time endpoints with a custom container are the correct choice because they provide persistent, always-on infrastructure that eliminates cold start latency. By packaging the TensorFlow model with required system libraries in a custom Docker image, the engineer ensures the inference environment is ready immediately, and the endpoint can scale to handle variable traffic with minimal delay.

Answer

SageMaker Serverless Inference with a custom container

Answer

SageMaker Multi-Model Endpoint with a custom container

Answer

SageMaker Asynchronous Inference with a custom container

Question 4

A machine learning engineer is building a regression model to predict house prices. The feature 'square_footage' has values ranging from 500 to 10,000, while 'num_bedrooms' ranges from 1 to 10. Which preprocessing step is most critical before training a model that uses gradient descent?

Accepted Answer

Standardize both features to have zero mean and unit variance.. Gradient descent is sensitive to the scale of features because it updates weights proportionally to the feature values. With 'square_footage' (500–10,000) and 'num_bedrooms' (1–10), the large range difference causes the loss function's contours to be elongated, leading to slow or unstable convergence. Standardizing both features to zero mean and unit variance ensures each feature contributes equally to the gradient updates, enabling faster and more reliable optimization.

Answer

Apply a logarithmic transformation to both features.

Answer

Encode the 'num_bedrooms' feature using one-hot encoding.

Answer

Impute missing values using the mean of the feature.

Question 5

A company stores training data in Amazon S3 and uses Amazon SageMaker for model training. They need to ensure data is encrypted at rest. Which THREE encryption options are supported by SageMaker for data stored in S3? (Choose THREE.)

Accepted Answer

SSE-C (customer-provided keys). Amazon SageMaker supports SSE-C (server-side encryption with customer-provided keys) for data stored in S3. When using SSE-C, you manage the encryption keys and provide them to SageMaker during training job configuration. SageMaker uses these keys to decrypt the data on your behalf, ensuring data is encrypted at rest in S3 while you retain control of the encryption keys.

Answer

Client-side encryption

Answer

Amazon CloudFront encryption

Question 6

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

Accepted Answer

AWS CloudTrail. 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.

Answer

Amazon GuardDuty

Answer

AWS Config

Question 7

Refer to the exhibit. The Glue job reads a CSV file and attempts to write to a Parquet table. What is the most likely cause of this error?

Accepted Answer

The 'price' column contains non-numeric values in some rows. The error message indicates a 'NumberFormatException' when parsing the 'price' column, which occurs when Spark attempts to convert a string value to a numeric type. Since the Glue job's schema inference likely detected 'price' as a numeric column based on the majority of rows, any row containing a non-numeric value (e.g., 'N/A', 'null', or a currency symbol) will cause this parsing failure during the write to Parquet.

Answer

The 'price' column is missing from some rows

Answer

The schema inference incorrectly detected the column as String

Answer

The CSV file is compressed and not properly decompressed

Question 8

A data scientist is using Amazon SageMaker Processing to run a feature engineering job. The job requires installing additional Python libraries not included in the default SageMaker containers. Which approach should the data scientist use to include these libraries?

Accepted Answer

Create a custom Docker image with the libraries installed and specify it in the ProcessingInput. SageMaker Processing jobs run in isolated containers that cannot install packages at runtime via pip without internet access or custom images. Creating a custom Docker image with the required libraries pre-installed ensures the environment is consistent, reproducible, and avoids dependency resolution failures during job execution. This approach aligns with SageMaker's best practice for custom dependencies.

Answer

Add the libraries to the `requirements.txt` file in the same S3 bucket as the script

Answer

Use Amazon EFS to store the libraries and mount them to the processing container

Answer

Use the `pip install` command within the processing script at runtime

Question 9

A machine learning team needs to deploy a model that was built using scikit-learn. They want to use SageMaker for hosting. Which approach should they take?

Accepted Answer

Package the model artifacts and use the SageMaker built-in scikit-learn container for inference. SageMaker provides a pre-built, optimized Docker container for scikit-learn that supports inference. By packaging the model artifacts (e.g., a joblib or pickle file) and deploying them using the built-in container, the team avoids the overhead of custom container creation while ensuring compatibility with SageMaker's hosting infrastructure, including automatic scaling and load balancing.

Answer

Create a Jupyter notebook that loads the model and runs predictions on the SageMaker notebook instance

Answer

Create a custom Docker container with scikit-learn and deploy it on SageMaker

Answer

Launch a SageMaker training job with the model and use the training instance as an endpoint

Question 10

A data science team is using Amazon SageMaker Pipelines to orchestrate a multi-step workflow that includes data preprocessing, training, and model evaluation. They want to reuse the preprocessed data across multiple pipeline executions without re-running the preprocessing step if the source data hasn't changed. What should they configure?

Accepted Answer

Use SageMaker Processing steps with caching. SageMaker Processing steps support caching, which allows the pipeline to skip re-execution of the preprocessing step if the input data and pipeline parameters have not changed. This is achieved by configuring a `CacheConfig` with a caching key based on the input data source and step parameters, ensuring that the preprocessed data is reused across multiple pipeline executions without redundant computation.

Answer

Use SageMaker Training steps with checkpointing

Answer

Use SageMaker Feature Store to store the preprocessed features

Answer

Use SageMaker Data Wrangler for the preprocessing

Question 11

A data science team at a financial services company is deploying a real-time fraud detection model using Amazon SageMaker. The model is a gradient boosting classifier trained on historical transaction data. The model is deployed to a SageMaker endpoint with an ML.M5.LARGE instance for real-time inference. After deployment, the team observes that the endpoint's latency spikes to over 2 seconds during peak hours (10:00-12:00 and 14:00-16:00), causing timeouts for client applications. The average latency during off-peak hours is 200 ms. The team has enabled auto-scaling with a target average CPU utilization of 70%, but the endpoint still experiences high latency during peak hours. The instance count never scales beyond 2 instances during peaks. The model size is 500 MB, and each request includes 200 features. The team needs to reduce latency to under 500 ms at the 99th percentile during peak hours without increasing costs beyond the current budget. Which course of action should the team take?

Accepted Answer

Increase the auto-scaling maximum instance count to 10 and set target CPU utilization to 50%.. The root cause of high latency during peak hours is insufficient compute capacity. By increasing the auto-scaling maximum instance count to 10 and lowering the target CPU utilization to 50%, the endpoint will scale out more aggressively during peak traffic, distributing the inference load across more instances. This reduces per-instance CPU utilization and latency without resorting to more expensive GPU instances. The current budget likely supports up to 10 instances, so costs remain within budget. Option C is incorrect because gradient boosting inference is CPU-bound and does not benefit significantly from GPU acceleration; GPU instances are also more expensive, potentially increasing costs.

Answer

Configure SageMaker batch transform for the real-time endpoint to process requests asynchronously.

Answer

Switch the endpoint instance type to a GPU instance such as ml.g4dn.xlarge to accelerate inference.

Answer

Enable data compression on the endpoint to reduce payload size and network latency.

Question 12

A machine learning engineer is training a deep learning model on SageMaker and notices that the training loss decreases rapidly in the first few epochs but then plateaus. The validation loss starts increasing after 10 epochs. Which action should the engineer take to improve generalization?

Accepted Answer

Use early stopping with validation loss monitoring. Early stopping is the correct action because the validation loss increasing after 10 epochs while training loss continues to decrease is a classic sign of overfitting. By monitoring validation loss and halting training when it stops improving (e.g., using a patience parameter), the engineer prevents the model from memorizing noise in the training data, thereby improving generalization. SageMaker's built-in training job features or the `EarlyStopping` callback in frameworks like TensorFlow or PyTorch can implement this directly.

Answer

Add more layers to the model

Answer

Increase the learning rate

Answer

Decrease the batch size

Question 13

A company uses SageMaker Pipelines to automate their ML workflow. They need to add model versioning and approval workflow. Which THREE steps should they include in their pipeline to achieve this? (Choose THREE.)

Accepted Answer

RegisterModel step. The RegisterModel step is correct because it creates a model package in SageMaker Model Registry, which enables versioning and approval workflows. This step registers the trained model artifact along with metadata, allowing the pipeline to track model versions and trigger approval processes for deployment.

Answer

Training step

Answer

Transform step

Question 14

A company uses SageMaker JumpStart to deploy a foundation model for a summarization task. They want to minimize costs while still meeting a latency requirement of under 2 seconds. Which option should they consider?

Accepted Answer

Use SageMaker Inference Recommender to select the cheapest instance that meets latency. SageMaker Inference Recommender runs load tests against your model on various instance types and provides latency and cost metrics. By selecting the cheapest instance that still meets the sub-2-second latency requirement, you directly minimize cost while satisfying the performance constraint. This is the most systematic and cost-effective approach for this scenario.

Answer

Deploy the model on a serverless endpoint

Answer

Enable auto-scaling to handle variable traffic

Answer

Use the largest GPU instance to ensure fast inference

Question 15

An ML team uses SageMaker to deploy a model for real-time inference. They want to monitor and improve cost efficiency. Which THREE actions should they take? (Select THREE.)

Accepted Answer

Use SageMaker Inference Recommender to find the optimal instance type and count. SageMaker Inference Recommender runs load tests against your model to generate instance type and count recommendations that balance performance and cost. By selecting the optimal configuration, you avoid over-provisioned instances that waste money or under-provisioned ones that degrade user experience, directly improving cost efficiency.

Answer

Create a CloudWatch dashboard to monitor endpoint latency

Answer

Use SageMaker Managed Spot Training for endpoint instances

Question 16

A company is using SageMaker Pipelines to automate a multi-step ML workflow. The pipeline includes data preprocessing, training, and model evaluation. The team wants to ensure that if the evaluation step fails, the pipeline stops and sends an alert to the operations team. Which SageMaker Pipelines feature should they use?

Accepted Answer

Use a Condition step to check the evaluation result and route to a Fail step if the result indicates failure. SageMaker Pipelines provides a built-in Condition step that evaluates a boolean expression (e.g., checking if evaluation metrics meet a threshold) and then routes execution to different steps. If the condition fails, you can direct the pipeline to a Fail step, which immediately stops the pipeline and marks it as failed. This is the native, event-driven way to halt a pipeline based on step output without relying on external services.

Answer

Configure an Amazon CloudWatch Events rule to monitor the pipeline execution status and stop it if the evaluation step fails

Answer

Register the model in the Model Registry only if evaluation passes, and configure the pipeline to stop if registration fails

Answer

Add a Lambda step after the evaluation step that checks the evaluation metrics and sends an SNS notification if the metrics are below a threshold

Question 17

A data scientist is preparing text data for natural language processing (NLP). The corpus contains many rare words and typos. To reduce dimensionality and improve generalization, they decide to apply stemming and remove stop words. However, after training, the model performs poorly on domain-specific terms. What is the most likely cause?

Accepted Answer

Both stemming and stop word removal are inappropriate for the domain. Both stemming and stop word removal are inappropriate for this domain. Stemming aggressively reduces words to their root forms, which can conflate distinct domain-specific terms (e.g., 'therapy' and 'therapist' both stem to 'therap'), losing critical semantic nuance. Stop word removal can discard words that carry domain-specific meaning (e.g., 'not' in medical negation or 'up' in 'tune-up' for maintenance), leading to poor generalization on specialized vocabulary.

Answer

The corpus should be lemmatized instead

Answer

Stemming is too aggressive for the domain

Answer

Stop word removal removed important context words

Question 18

Which THREE components are required to set up automated model retraining in response to performance degradation using Amazon SageMaker? (Select THREE.)

Accepted Answer

A CloudWatch alarm that triggers when a quality metric falls below a threshold.. A CloudWatch alarm can monitor a SageMaker Model Monitor quality metric (e.g., accuracy, precision) and trigger an alarm when the metric falls below a defined threshold. This alarm acts as the event source to initiate automated retraining, forming the monitoring and alerting backbone of the retraining pipeline.

Answer

An Amazon SNS topic with a subscription to send a manual approval email.

Answer

A production variant with a canary traffic shift configuration.

Question 19

Which THREE steps should be taken to optimize a large-scale distributed training job on SageMaker? (Choose 3.)

Accepted Answer

Use GPU instances with high bandwidth and memory (e.g., ml.p4d.24xlarge).. GPU instances like ml.p4d.24xlarge provide high-bandwidth GPU memory and NVLink inter-GPU connectivity, which are essential for large-scale distributed training. These instances reduce communication bottlenecks and allow larger batch sizes, directly improving throughput and model convergence speed.

Answer

Attach multiple EBS volumes with throughput provisioning.

Answer

Enable batch transform for offline inference after training.

Question 20

A data engineer notices that an AWS Glue ETL job is failing with an Out of Memory error when processing a large dataset. The dataset is 500 GB in size, and the worker type is G.1X. Which change is MOST likely to resolve the issue?

Accepted Answer

Use a larger worker type like G.2X. The G.1X worker type provides 16 GB of memory per worker. A 500 GB dataset requires sufficient aggregate memory across workers for processing. Increasing the worker type to G.2X (which doubles memory to 32 GB per worker) increases the memory per executor, allowing each task to handle larger data partitions without running out of memory. This directly addresses the Out of Memory error by providing more heap space for Spark operations.

Answer

Partition the input data into smaller files

Answer

Use a Spark DataFrame instead of RDD

Answer

Increase the number of workers