MLA-C01 Practice Test 20 — 15 Questions

Question 1

A data scientist wants to fine-tune a Llama 2 7B model using SageMaker for a text summarization task. The dataset is 10 GB. The budget is limited, so cost efficiency is important. Which THREE steps should the data scientist take? (Choose THREE.)

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

Accepted Answer

Use LoRA to reduce the number of trainable parameters. LoRA reduces trainable parameters, enabling fine-tuning on smaller instances. HuggingFace estimator is the standard for HF models. Spot instances reduce cost. DeepSpeed ZeRO-3 is for large models but not necessary with LoRA. BYOC is overkill.

Answer

Use SageMaker Debugger to reduce training time

Answer

Use the SageMaker built-in BlazingText algorithm

Question 2

A data scientist is using SageMaker Experiments to track multiple training runs for a PyTorch model. They want to compare metrics across runs and identify the best hyperparameters. Which TWO capabilities should they use? (Choose TWO.)

Accepted Answer

SageMaker Experiments list and search API to query runs by metric. SageMaker Experiments automatically tracks hyperparameters and metrics. The SDK allows logging custom metrics. The Experiments list and search interface can compare runs. Autopilot is for AutoML, not for custom PyTorch. Model Monitor is for deployed models.

Answer

SageMaker Autopilot

Answer

SageMaker Clarify

Answer

SageMaker Model Monitor

Question 3

A data scientist is training a model using SageMaker and wants to use spot instances to reduce costs. The training job is checkpointed every 5 minutes. However, the job gets interrupted frequently and never completes. What is the MOST likely cause?

Accepted Answer

The checkpoint interval is too long relative to the interruption frequency. Spot instances can be reclaimed with little notice. If the job checkpoint interval is longer than the average interruption notice, progress may be lost. Using a smaller instance type reduces cost but not interruption frequency. Incorrect checkpoint path causes save failures. Too few max retries causes job to stop after few interruptions.

Answer

The checkpoint S3 URI is incorrect

Answer

The instance type is too small for the training job

Answer

The job is configured with too few max retries

Question 4

A data scientist is using SageMaker Automatic Model Tuning with Hyperband. They want to stop poorly performing trials early to save resources. Which strategy does Hyperband use?

Accepted Answer

Successive Halving. Hyperband uses early stopping by allocating resources to promising configurations and stopping poorly performing ones. Bayesian optimization uses acquisition functions. Random search does not early stop. Grid search exhaustively evaluates all combinations.

Answer

Grid search

Answer

Random search

Answer

Bayesian optimization

Question 5

An ML team is preparing time-series data for a demand forecasting model. They want to evaluate model performance over time without leaking future information into past training windows. Which data splitting strategy is MOST appropriate?

Accepted Answer

Walk-forward validation with an expanding window. Walk-forward validation with an expanding window is the most appropriate strategy because it respects the temporal order of the data, ensuring that each training window contains only past observations and each validation window contains only future observations. This prevents data leakage and provides a realistic evaluation of how the model will perform on unseen future time steps, which is critical for demand forecasting.

Answer

Random k-fold cross-validation

Answer

Single hold-out set with random selection

Answer

Stratified sampling based on the target variable

Question 6

A financial services company is deploying a fraud detection model on SageMaker. To comply with regulations, they must ensure that the model's predictions are not biased against protected groups. They plan to monitor bias drift post-deployment using SageMaker Clarify. Which data inputs are required to configure Clarify's bias drift monitoring?

Accepted Answer

Baseline training data with ground truth labels and inference data with predictions. SageMaker Clarify's bias drift monitoring requires a baseline—specifically, the training data with ground truth labels—to establish the original bias metrics, and the inference data with predictions to compute post-deployment bias metrics. By comparing these two datasets, Clarify detects statistically significant shifts in bias over time, which is essential for regulatory compliance in fraud detection models.

Answer

Only the inference data with predictions

Answer

Only the ground truth labels for recent predictions

Answer

Only the training data with feature attributions

Question 7

A data scientist needs to train a binary classification model on a large tabular dataset stored in Amazon S3. The team wants to minimize training time and cost while using a built-in SageMaker algorithm. Which algorithm should they use?

Accepted Answer

Linear Learner. Linear Learner is a built-in SageMaker algorithm designed for binary classification and regression, and it scales efficiently on large datasets. XGBoost is better for structured data with non-linear relationships, DeepAR is for time series, and BlazingText is for text.

Answer

BlazingText

Answer

DeepAR

Answer

XGBoost

Question 8

A data engineer is building a data pipeline for a machine learning model that requires both structured and unstructured data. The structured data (customer demographics) is in Amazon RDS, and the unstructured data (customer support chat logs) is in Amazon S3 as JSON files. The engineer needs to combine these datasets into a single training dataset stored in S3 in Parquet format. They must also perform feature engineering such as text vectorization on the chat logs. The pipeline should be serverless and cost-effective. Which approach should they use?

Accepted Answer

Use AWS Glue ETL with a Spark script that reads from RDS (via JDBC) and S3, performs transformations, and writes Parquet.. AWS Glue ETL with a Spark script is the correct choice because it natively supports reading from both Amazon RDS (via JDBC) and Amazon S3 (JSON), performing complex transformations like text vectorization, and writing the output as Parquet. Glue is serverless, cost-effective (pay per DPU-hour), and fully managed, making it ideal for batch ETL pipelines that combine structured and unstructured data for ML training.

Answer

Use a SageMaker Processing job with a custom Python script that reads from both sources and writes to S3.

Answer

Use Amazon Athena to join the data from RDS and S3, then export the results as Parquet.

Answer

Use Amazon Kinesis Data Analytics to read from RDS and S3 and produce a continuous stream of processed data.

Question 9

A company deploys a model on Amazon SageMaker for real-time inference. The inference latency is too high. The model is a large deep learning model. The company wants to reduce latency without significantly impacting accuracy. Which approach should the company consider?

Accepted Answer

Use SageMaker Inference Recommender to test different instance types and optimizations.. SageMaker Inference Recommender is designed specifically to automate load testing and benchmarking across various instance types and model optimizations (e.g., Elastic Inference, GPU acceleration, serialization formats). It provides latency and throughput metrics to identify the optimal configuration for reducing inference latency while maintaining accuracy, making it the correct choice for a large deep learning model with high latency.

Answer

Increase the batch size for inference.

Answer

Use a smaller instance type to reduce inference time.

Answer

Enable SageMaker Model Monitor to detect performance issues.

Question 10

An ML team is developing a regression model using Amazon SageMaker. They have a 100 GB CSV dataset stored in Amazon S3. The data is contained in a single large file. They launch a SageMaker training job with an ml.p3.8xlarge instance using a custom Docker container. The training script loads the data using pandas' read_csv from S3 directly. The team observes that the training job takes over 24 hours, and CloudWatch metrics show: GPU utilization is consistently above 90%, but CPU utilization is below 30%. Network I/O is moderate, and disk I/O is low. The team has already tried switching to a larger instance type (ml.p3.16xlarge) with no significant improvement. They need to reduce training time. Which action is MOST likely to achieve this?

Accepted Answer

Split the CSV file into multiple smaller files (e.g., 100 MB each) and update the training script to read from a list of files in S3.. The bottleneck is data loading. The single large CSV file prevents parallelism; SageMaker's Pipe mode streams data directly to the algorithm, but custom containers must support it. However, a simpler and effective approach is to split the data into multiple smaller files, enabling SageMaker's distributed data loading across instances and improving I/O parallelism. Increasing instance count with single file doesn't help because each instance still reads the same file. Changing instance type already tried. Spot instances don't improve speed. EBS volume doesn't matter.

Answer

Use SageMaker Pipe Mode to stream data directly from S3 to the algorithm, bypassing the local file system.

Answer

Use Amazon SageMaker Managed Spot Training to reduce cost, then use the savings to rent a larger instance.

Answer

Increase the number of training instances by using a distributed training configuration with Horovod.

Question 11

A company operates an IoT platform that ingests sensor data from thousands of devices. Data is streamed via Amazon Kinesis Data Streams and stored in an S3 bucket using a Kinesis Firehose delivery stream, which writes data in 5-minute windows. The data is then used to train a machine learning model for anomaly detection. Recently, the data science team noticed that the training dataset is always missing the last 5 minutes of events from the end of each day. The S3 objects show that the last delivery stream buffer window is incomplete. The data engineer checked the Kinesis Firehose metrics and found no delivery errors or data loss, but the 'IncomingBytes' and 'IncomingRecords' metrics show consistent data for all periods. The S3 bucket has Lifecycle policies that do not delete objects. The team suspects the issue is related to the data preparation pipeline. Which course of action would correctly resolve the missing data problem?

Accepted Answer

Increase the buffer size to 10 MB and reduce the buffer interval to 60 seconds in the Firehose delivery stream configuration. The issue is that the last 5-minute buffer window at the end of each day never completes, so Firehose never delivers that final object to S3. By reducing the buffer interval to 60 seconds and increasing the buffer size to 10 MB, Firehose will flush data more frequently, ensuring that even small residual data at the end of the day is delivered before the stream stops. This directly addresses the incomplete last window without requiring reprocessing or changing the pipeline architecture.

Answer

Reprocess the Kinesis stream data from the beginning using a custom application

Answer

Modify the data preparation pipeline to use AWS Lambda to write data to S3 directly from Kinesis

Answer

Increase the buffer interval to 600 seconds to allow more time for data to accumulate

Question 12

A media company uses SageMaker endpoints to serve a model that predicts video engagement. They have two production variants: Variant A (ml.c5.large) for regular traffic and Variant B (ml.c5.xlarge) for burst traffic. They use weighted routing (90% to A, 10% to B). Recently, during peak hours, Variant A's latency increase causes many requests to time out. The metrics show that both variants are under similar CPU load, but the number of concurrent requests to Variant A is very high. The team wants to ensure that burst traffic is handled properly without manual intervention. What should they do?

Accepted Answer

Configure Application Auto Scaling for each variant with a target tracking scaling policy based on the number of concurrent requests per instance.. Changing to target tracking scaling based on the number of concurrent requests (or InvocationsPerInstance) ensures each variant scales based on its load. Option A (swap weights) doesn't fix scaling. Option C (p99 latency alarm) might trigger too late. Option D (separate endpoint) is not necessary.

Answer

Increase the traffic weight to Variant B to 70% and reduce Variant A to 30%.

Answer

Set a CloudWatch alarm on Variant A's p99 latency and trigger a step scaling policy to add instances.

Answer

Create a separate endpoint for burst traffic and route peak traffic to it via DNS.

Question 13

A team is deploying a model using SageMaker real-time endpoint with an ml.m5.large instance. They notice high latency under peak load. They want to reduce latency without increasing instance size. Which THREE actions could help? (Select THREE.)

Accepted Answer

Quantize the model to reduce its size. SageMaker Neo compiles the model for the target hardware, reducing latency. Elastic Inference attaches GPU acceleration to a CPU instance. Model quantization reduces model size and speeds up inference. Increasing instance count does not reduce per-request latency (it increases throughput). Changing to a GPU instance increases instance size.

Answer

Increase the number of instances in the endpoint

Answer

Change the instance type to ml.g4dn.xlarge

Question 14

A company wants to deploy 50 small models (each ~100 MB) for real-time inference. They need to minimize hosting costs while maintaining low latency. Which SageMaker hosting option is most cost-effective?

Accepted Answer

SageMaker Multi-Model Endpoint (MME). Multi-Model Endpoint (MME) allows hosting multiple models on the same instance, sharing resources. Since the models are small, MME is cost-effective. Real-time endpoints would require separate instances. Serverless is for on-demand but may incur cold starts. Asynchronous is for batch-like workloads.

Answer

SageMaker Serverless Inference

Answer

SageMaker Asynchronous Inference

Answer

SageMaker real-time endpoint with one instance per model

Question 15

A company wants to serve a large ensemble of models using NVIDIA Triton Inference Server on SageMaker for high throughput GPU inference. Which SageMaker inference option supports this?

Accepted Answer

Real-time endpoint with a custom container running Triton. SageMaker supports Triton Inference Server through a custom real-time endpoint container, as Triton is optimized for GPU serving on NVIDIA hardware.

Answer

Asynchronous Inference

Answer

Multi-model endpoint

Answer

Serverless Inference