MLS-C01 Practice Test 34 — 15 Questions

Question 1

A media company uses SageMaker to train a recommendation model. The training data is stored in an S3 bucket with versioning enabled. The data pipeline updates the training data daily by overwriting objects with new data. Recently, the model's performance degraded, and the team suspects that the training data was corrupted on a specific day. They want to train the model using the data from a previous version. How can the team retrieve the previous version of the training data?

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

Accepted Answer

Use the S3 GET Object Version API to download the specific version of each object.. S3 versioning allows retrieval of any previous version of an object by using the GET Object Version API with the specific version ID. Option A is incorrect because S3 Glacier is an archival storage class, not a feature for accessing previous versions of current objects. Option C is incorrect because S3 Select is used to query data within an object, not for version retrieval. Option D is incorrect because S3 replication copies objects to another bucket but does not provide access to previous versions in the source bucket.

Answer

Restore the bucket from S3 Glacier, which contains the previous version.

Answer

Use S3 Select to query the previous version of the data.

Answer

Enable S3 replication to a different bucket and use the replicated data.

Question 2

A company uses SageMaker to host a real-time inference endpoint. The endpoint is receiving a large number of requests, but the latency is higher than expected. The data scientist observes that the CPU utilization is low but memory utilization is high. Which action should be taken to reduce latency?

Accepted Answer

Switch to an instance type with more memory or optimize the model to reduce memory footprint.. High memory utilization indicates the model is memory-bound. Increasing instance memory or optimizing the model to reduce memory footprint can reduce latency. Option B is wrong because VPC traffic mirroring is used for network diagnostics, not for addressing memory bottlenecks. Option C is wrong because CPU utilization is low, so adding more vCPUs would not help; the bottleneck is memory, not CPU. Option D is wrong because increasing the number of instances can improve throughput but does not directly reduce per-request latency for a memory-bound model; it may also increase cost.

Answer

Enable VPC traffic mirroring to diagnose network issues.

Answer

Use an instance type with more vCPUs.

Answer

Increase the number of instances in the endpoint.

Question 3

A data engineer is performing EDA on a dataset containing user activity logs from a mobile app. The dataset has 10 million rows and includes columns: 'user_id', 'event_type', 'timestamp', 'device_type', and 'session_duration'. The engineer uses Amazon Athena to query the data stored in S3 as CSV files. The engineer runs a query to find the average session_duration per device_type, but the query takes over 5 minutes and scans 100 GB of data. The engineer wants to reduce query cost and improve performance for future EDA. The dataset is not partitioned, and the engineer anticipates frequent queries filtering on 'timestamp' and 'device_type'. Which action will most effectively reduce data scanned?

Accepted Answer

Partition the table by date derived from timestamp and convert to Parquet.. The most effective because it combines partitioning by date (derived from timestamp) and converting to Parquet format. Partitioning by date enables partition pruning for queries filtering on 'timestamp', drastically reducing the amount of data scanned. Parquet provides columnar storage and compression, further minimizing I/O and cost. Option C (Parquet without partitioning) still requires full file scans when filters are applied. Option B (random sampling) sacrifices accuracy for speed, which is undesirable for accurate EDA. Option D (partitioning by device_type) helps only for device_type filters, not for the common timestamp filters mentioned in the scenario.

Answer

Use random sampling to query a subset of data.

Answer

Convert the data to Parquet format and use columnar storage.

Answer

Partition the table by device_type.

Question 4

A SageMaker training job fails with the failure reason shown in the exhibit. What is the most likely cause?

Accepted Answer

There is an error in the custom training script. The failure reason in the exhibit (not shown here but implied by the question) typically indicates a runtime error such as a Python exception, missing module, or syntax error in the custom training script. SageMaker logs the exact error from the container, and when the script itself fails, the training job terminates with a 'ClientError' or 'AlgorithmError' referencing the script, not infrastructure issues.

Answer

The training instance ran out of memory

Answer

The S3 bucket with training data is not accessible

Answer

The SageMaker service limit for the instance type has been exceeded

Question 5

A company is using Amazon SageMaker to train a deep learning model. The training job is failing with an error 'CUDA out of memory'. The training instance is an ml.p3.2xlarge with 16 GB GPU memory. The model architecture and batch size are appropriate for this instance size. What is the most likely cause of this error?

Accepted Answer

Enable automatic mixed precision (AMP) training to reduce memory usage.. Enabling automatic mixed precision (AMP) training reduces GPU memory usage by storing tensors in half-precision (FP16) where possible, while keeping critical operations in full precision (FP32). This directly addresses the 'CUDA out of memory' error on an ml.p3.2xlarge instance (16 GB GPU memory) without changing the model architecture or batch size, which are already appropriate.

Answer

Reduce the number of epochs.

Answer

Increase the number of GPUs by using a distributed training instance type.

Answer

Use a smaller instance type to force lower memory usage.

Question 6

A company is building a recommendation system using matrix factorization. The training data contains user-item interactions. The model performs well on the training set but poorly on the test set. Which regularization technique should be applied to improve generalization?

Accepted Answer

Add L2 regularization to the user and item latent factors. L2 regularization (weight decay) penalizes large values in the user and item latent factor matrices, which helps prevent overfitting by encouraging the model to learn smoother, more generalizable representations. This is the standard regularization technique used in matrix factorization for collaborative filtering, as it directly controls the magnitude of the latent vectors without inducing sparsity.

Answer

Add L1 regularization to the user and item latent factors

Answer

Apply dropout to the latent factors during training

Answer

Use batch normalization on the factors

Question 7

A company is deploying a real-time inference endpoint using SageMaker. The model is a large deep learning model (5 GB) with strict latency requirements (< 100 ms per request). The team expects bursty traffic with up to 1000 requests per second. Which configuration best meets the latency and throughput requirements?

Accepted Answer

Deploy an ml.p3.2xlarge instance with automatic scaling based on a custom metric like 'InvocationsPerInstance'. Deploying on an ml.p3.2xlarge instance with automatic scaling based on 'InvocationsPerInstance' allows the endpoint to handle bursty traffic up to 1000 requests per second while maintaining sub-100 ms latency. The GPU-accelerated p3 instance provides the necessary compute for a 5 GB deep learning model, and custom scaling on invocations per instance ensures that additional instances are provisioned quickly during traffic spikes without over-provisioning.

Answer

Use a multi-model endpoint with ml.c5.4xlarge instances

Answer

Use SageMaker Serverless Inference with a memory size of 6 GB

Answer

Deploy a single ml.p3.16xlarge instance with a production variant

Question 8

A machine learning team is deploying a model using Amazon SageMaker. The model inference code runs on GPUs and requires a custom container. The team wants to minimize cold start latency. Which SageMaker hosting option should they use?

Accepted Answer

Use a real-time endpoint with a single production variant using a GPU instance.. A real-time endpoint with a single production variant using a GPU instance provides a persistent, always-on inference environment. This eliminates cold start latency entirely, as the container and GPU resources are pre-warmed and ready to serve requests immediately, which is critical for minimizing latency in GPU-based custom containers.

Answer

Use a multi-model endpoint with GPU instances.

Answer

Use a serverless inference endpoint.

Answer

Use a real-time endpoint with multiple production variants for redundancy.

Question 9

A data scientist is training a model using SageMaker's built-in XGBoost algorithm. The dataset has 500 features and 1 million rows. The training job is taking too long. The scientist wants to reduce training time without sacrificing accuracy. Which action is LIKELY to be most effective?

Accepted Answer

Apply Principal Component Analysis (PCA) to reduce the number of features. PCA reduces the dimensionality of the dataset from 500 features to a smaller set of principal components, which directly decreases the computational complexity of training the XGBoost model. With fewer features, each tree split requires fewer operations, and the overall training time drops significantly. Since PCA retains the variance in the data, it can preserve model accuracy while speeding up training.

Answer

Use a smaller instance type to reduce time

Answer

Reduce the number of trees in XGBoost

Answer

Use a larger instance type with more vCPUs

Question 10

A company wants to build a data lake on Amazon S3. The data lake should support both batch and real-time data ingestion. Which AWS services should be used for data ingestion? (Choose TWO.)

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a managed ETL service that can handle batch data ingestion into a data lake on Amazon S3. It can be scheduled for periodic batch loads or triggered by events, making it suitable for batch ingestion workflows. Amazon Kinesis Data Firehose is correct because it is a fully managed service for loading streaming data into S3 in near real-time, supporting real-time ingestion with automatic buffering and compression.

Answer

Amazon Redshift

Answer

Amazon Athena

Answer

Amazon SQS

Question 11

A data scientist is analyzing a time-series dataset and wants to check for stationarity. Which EDA technique is most appropriate?

Accepted Answer

Perform the Augmented Dickey-Fuller (ADF) test.. The Augmented Dickey-Fuller (ADF) test is a formal statistical hypothesis test specifically designed to check for stationarity in a time series. It tests the null hypothesis that a unit root is present, indicating non-stationarity, against the alternative of stationarity. This makes it the most appropriate EDA technique for directly assessing stationarity.

Answer

Plot the autocorrelation function (ACF).

Answer

Use time-series cross-validation.

Answer

Create a scatter plot of the series against its lag.

Question 12

A company is training a deep learning model on SageMaker using multiple GPUs. The training is slow due to inefficient data loading. Which TWO actions can improve I/O performance?

Accepted Answer

Use Pipe mode input for training data.. Pipe mode streams training data directly from Amazon S3 to the training algorithm, bypassing the need to download data to disk before training begins. This eliminates disk I/O bottlenecks and reduces data loading latency, which is critical for GPU-intensive training where GPUs may otherwise idle waiting for data.

Answer

Use instance store volumes for data.

Answer

Increase the instance count to a single large instance.

Answer

Use Amazon EBS volumes attached to training instances.

Question 13

An e-commerce company uses Amazon Redshift for analytics. The data engineering team needs to load daily sales data from an S3 bucket that receives new files every hour. The data must be loaded into Redshift with minimal impact on query performance during the day, and they need to handle late-arriving data (files that appear after the daily load). Which approach should they use?

Accepted Answer

Use a staging table to load data incrementally with a MERGE operation, and schedule a late-arriving data job to merge files that arrive after the daily load.. It uses a staging table to incrementally load data with a MERGE operation, which minimizes impact on query performance by avoiding full table overwrites. The separate late-arriving data job handles files that appear after the daily load, ensuring completeness without blocking ongoing queries. This approach aligns with Redshift's best practices for incremental loads and late-arriving data handling.

Answer

Use AWS Glue ETL to copy the data from S3 to Redshift, overwriting the existing data each day.

Answer

Stream the data from S3 using Amazon Kinesis Firehose to load into Redshift continuously.

Answer

Use Amazon Redshift Spectrum to query data directly from S3 and create external tables.

Question 14

A data scientist is training a deep learning model for image segmentation using a U-Net architecture. The model overfits severely. The scientist tries L2 regularization, dropout, and data augmentation, but validation loss remains high while training loss approaches zero. Which additional strategy is most likely to reduce overfitting?

Accepted Answer

Implement early stopping based on validation loss. Early stopping monitors validation loss and halts training when it stops improving, directly addressing overfitting by preventing the model from memorizing noise after it has learned generalizable features. Since the training loss is near zero but validation loss remains high, the model has already started overfitting, and early stopping can cut training at the point just before overfitting worsens.

Answer

Increase the batch size

Answer

Use a larger learning rate

Answer

Add more convolutional layers to increase model capacity

Question 15

A company is deploying a real-time fraud detection model using Amazon SageMaker. The model must make predictions in under 100 milliseconds. The data scientist uses a pre-trained XGBoost model and deploys it to a SageMaker endpoint with an ml.c5.xlarge instance. After load testing, the average latency is 150 ms. Which action should the data scientist take to reduce latency?

Accepted Answer

Enable SageMaker Neo to compile the model for the target instance. SageMaker Neo optimizes trained models for the target hardware platform by compiling them into an efficient runtime. This reduces inference latency without changing the model architecture, making it ideal for meeting the sub-100ms requirement when the current latency is 150ms on an ml.c5.xlarge instance.

Answer

Reduce the number of trees in the XGBoost model

Answer

Deploy multiple instances behind a load balancer

Answer

Use a larger instance type to increase compute capacity