MLS-C01 Practice Test 35 — 15 Questions

Question 1

A machine learning engineer is deploying a model to an Amazon SageMaker endpoint. The model requires GPU for inference. Which instance type should be selected?

Accepted Answer

ml.p3.2xlarge. Ml.p3.2xlarge is a GPU-enabled instance (part of the P3 family) suitable for inference requiring GPU acceleration. Options B, C, and D (ml.m5.large, ml.c5.xlarge, ml.r5.large) are CPU-only instances and do not provide GPU capabilities.

Answer

ml.m5.large

Answer

ml.c5.xlarge

Answer

ml.r5.large

Question 2

A data engineer is designing a data pipeline that ingests data from a relational database into a data lake on Amazon S3. The data must be incrementally loaded daily. Which TWO AWS services can be used together to achieve this?

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a managed ETL service that can extract data from a relational database using JDBC connections, transform it, and write it incrementally to Amazon S3. Glue's built-in job bookmarking feature tracks processed data, enabling incremental loads by automatically skipping already-processed records during subsequent runs.

Answer

Amazon Kinesis Data Streams

Answer

Amazon Athena

Answer

Amazon Redshift

Question 3

A company is using SageMaker to host a model that makes predictions on streaming data from Amazon Kinesis. The model must provide predictions with sub-second latency. Which approach should the company use?

Accepted Answer

Use a SageMaker real-time endpoint and invoke it from an AWS Lambda function that is triggered by Kinesis. A SageMaker real-time endpoint provides sub-second latency for individual predictions, and invoking it from an AWS Lambda function triggered by Kinesis allows each streaming record to be processed synchronously with low overhead. This architecture meets the requirement for low-latency predictions on streaming data.

Answer

Use SageMaker asynchronous inference with a Kinesis trigger

Answer

Use Amazon Kinesis Data Analytics with a built-in ML model

Answer

Use SageMaker batch transform to process batches of records from Kinesis

Question 4

A data scientist is analyzing a dataset containing customer reviews. The data scientist wants to understand the most common words used in positive and negative reviews. Which AWS service is most suitable for this task?

Accepted Answer

Amazon Comprehend. Amazon Comprehend can perform sentiment analysis and extract key phrases. Option A is wrong because Amazon Rekognition is for image/video analysis. Option C is wrong because Amazon Polly is a text-to-speech service. Option D is wrong because Amazon Transcribe is for speech-to-text.

Answer

Amazon Rekognition

Answer

Amazon Polly

Answer

Amazon Transcribe

Question 5

A DevOps engineer created a SageMaker notebook instance using the Terraform configuration shown. The notebook instance is in a VPC with a public subnet. However, the notebook instance cannot access the internet. What is the most likely cause?

Accepted Answer

The subnet does not have a route to an internet gateway.. A SageMaker notebook instance in a VPC with a public subnet requires a route to an internet gateway (IGW) in the subnet's route table to access the internet. Without that route, traffic from the notebook cannot reach the internet, even if `direct_internet_access` is enabled. The Terraform configuration likely omitted the route to the IGW, causing the connectivity failure.

Answer

The role_arn is incorrect or missing permissions.

Answer

The instance type ml.t2.medium does not support internet access.

Answer

The direct_internet_access parameter is set to 'Enabled' but should be 'Disabled'.

Question 6

A company is using SageMaker's built-in image classification algorithm to classify product images into 100 categories. The training takes 3 hours on a single p3.2xlarge instance. They need to reduce training time to under 1 hour. They have access to a cluster of 4 p3.2xlarge instances. Which approach should they take?

Accepted Answer

Use SageMaker's distributed training with data parallelism using Horovod. SageMaker's built-in image classification algorithm supports distributed training with data parallelism using Horovod, which splits the mini-batch across multiple GPUs and synchronizes gradients via allreduce. With 4 p3.2xlarge instances (each with 1 GPU), this reduces per-iteration time proportionally, enabling the 3-hour job to complete in under 1 hour when scaling batch size and learning rate appropriately.

Answer

Use SageMaker's hyperparameter tuning to find faster convergence

Answer

Use a smaller batch size on each instance

Answer

Use SageMaker's managed spot training with checkpointing

Question 7

Refer to the exhibit. An ML engineer applies this bucket policy to an S3 bucket. The SageMaker execution role MySageMakerRole is used to train a model. The training data is located in s3://my-bucket/data/. The SageMaker training job fails with an access error. What is the most likely cause?

Accepted Answer

The Deny statement on s3:ListBucket prevents the role from listing objects in the bucket.. The Deny statement on s3:ListBucket explicitly denies the s3:ListBucket action for the MySageMakerRole. SageMaker training jobs require the ability to list objects in the bucket to discover and read training data, even if the GetObject permission is granted. The explicit Deny overrides any Allow, causing the access error.

Answer

The policy allows GetObject only from the data/ prefix, but the training job uses a different prefix.

Answer

The role is not in the same AWS account as the bucket.

Answer

The bucket has default encryption enabled, causing a conflict.

Question 8

Which TWO actions are best practices for securing a SageMaker notebook instance? (Select TWO.)

Accepted Answer

Disable direct internet access for the notebook instance.. Disabling direct internet access for a SageMaker notebook instance (Option A) is a best practice because it prevents the instance from reaching the public internet, reducing the attack surface. This forces all outbound traffic through a VPC, allowing you to control egress via NAT gateways or VPC endpoints, and ensures data does not traverse the public internet. It is a fundamental security hardening step for sensitive workloads.

Answer

Enable root access for users to install packages.

Answer

Store data in the notebook's local storage for performance.

Answer

Use a shared IAM user for all data scientists.

Question 9

A company is using Amazon SageMaker to train machine learning models. The training data is stored in Amazon S3, but the data includes personally identifiable information (PII) that must be anonymized before training. What is the most efficient way to anonymize the data?

Accepted Answer

Use an AWS Glue ETL job to read from S3, apply anonymization, and write to another S3 bucket.. AWS Glue ETL jobs are purpose-built for serverless data transformation at scale, making them the most efficient choice for anonymizing PII in S3 before training. Glue can read directly from S3, apply built-in or custom anonymization transforms (e.g., masking, hashing) using PySpark or Scala, and write the cleaned data to a separate S3 bucket without provisioning any infrastructure. This approach decouples the data preparation from SageMaker, avoids unnecessary compute costs during training, and scales automatically with data volume.

Answer

Use Amazon Athena to query the data and apply anonymization functions.

Answer

Use Amazon Redshift Spectrum to query and anonymize data in S3.

Answer

Use a SageMaker Processing job to read from S3 and apply anonymization.

Question 10

A data scientist is analyzing a dataset with 500 features and 100,000 observations. The target variable is binary. The dataset contains highly correlated features and some categorical variables with high cardinality. Which combination of techniques should the data scientist use to reduce dimensionality while preserving interpretability for EDA?

Accepted Answer

Apply correlation-based feature selection to remove highly correlated pairs, then use target encoding for high-cardinality categorical variables.. Correlation-based feature selection removes highly correlated features, reducing redundancy without distorting the original feature space, and target encoding converts high-cardinality categorical variables into numeric values based on the target mean, which preserves interpretability and avoids dimensionality explosion. Option A is incorrect because PCA reduces interpretability by transforming features into principal components and does not handle categorical variables directly. Option B is incorrect because mutual information is a feature selection method, but label encoding for high-cardinality categoricals can impose arbitrary ordinal relationships. Option C is incorrect because chi-squared test requires categorical features and is not suitable for high-dimensional numerical data; also, one-hot encoding high-cardinality categoricals leads to a drastic increase in dimensionality.

Answer

Apply Principal Component Analysis (PCA) to all features and then train a model on the top 50 components.

Answer

Use mutual information to select top features and apply label encoding to categorical variables.

Answer

Use chi-squared test to select top features and one-hot encode categorical variables.

Question 11

A company is deploying a real-time inference endpoint using SageMaker. The model has a high memory footprint and requires GPU acceleration. Which instance type and configuration should be used to minimize cost while meeting latency requirements?

Accepted Answer

ml.p3.2xlarge with 1 GPU. (ml.p3.2xlarge with 1 GPU) is correct because it provides the required GPU acceleration for the high-memory-footprint model while using the smallest instance in the P3 family, which minimizes cost. The P3 instances use NVIDIA V100 GPUs with high memory bandwidth, suitable for real-time inference with low latency, and the 2xlarge size offers sufficient GPU memory without over-provisioning.

Answer

ml.g4dn.xlarge with 1 GPU

Answer

ml.c5.xlarge with no GPU

Answer

ml.p3.16xlarge with 8 GPUs

Question 12

Refer to the exhibit. A data scientist wants to use SageMaker to train a model using data stored in 'my-bucket'. The training job fails with an access denied error. What is the MOST likely cause?

Accepted Answer

The training job is not requesting server-side encryption with AES256. SageMaker training jobs require server-side encryption with AES256 when the S3 bucket uses default encryption with AES256. If the training job does not explicitly request SSE-S3 (AES256) in its S3 data source configuration, the S3 service denies access, resulting in an 'access denied' error even if the IAM role has full S3 permissions.

Answer

The bucket uses AWS KMS key encryption instead of AES256

Answer

The bucket name in the policy does not match the actual bucket name

Answer

The bucket is publicly accessible but the IAM role lacks permissions

Question 13

Drag and drop the steps to set up cross-validation in a SageMaker training job using the built-in XGBoost algorithm in the correct order.

Accepted Answer

Split the data into training and validation folds, then configure the SageMaker estimator with cross-validation parameters (e.g., num_round, eval_metric, num_cv_folds), then execute the training job (fit), then select the best model based on average CV metrics.. Cross-validation requires data splitting, job configuration with CV parameters, execution, and model selection.

Answer

Configure the SageMaker estimator with cross-validation parameters, then split the data into folds, then execute the training job, then select the best model.

Answer

Split the data into folds, then execute the training job, then configure the estimator, then select the best model.

Answer

Execute the training job, then split the data into folds, then configure the estimator, then select the best model.

Question 14

A data scientist is tuning a neural network on a small dataset and observes that the training loss decreases but validation loss increases after a few epochs. Which technique should be applied to mitigate this issue?

Accepted Answer

Add dropout layers to the model.. The observed behavior—training loss decreasing while validation loss increasing—is a classic sign of overfitting. Adding dropout layers randomly drops a fraction of neurons during training, which forces the network to learn more robust features and reduces co-adaptation, thereby mitigating overfitting. This technique is specifically designed to improve generalization on small datasets.

Answer

Increase the learning rate.

Answer

Remove regularization terms from the loss function.

Answer

Increase the number of epochs.

Question 15

A data scientist is training a deep learning model for image classification using Amazon SageMaker. The training job is taking too long. The data scientist wants to use distributed training across multiple GPUs to speed up the process. Which SageMaker feature should the data scientist use?

Accepted Answer

SageMaker Distributed Training Libraries. SageMaker Distributed Training Libraries provide optimized implementations of data parallelism and model parallelism that automatically partition the model and data across multiple GPUs, reducing training time for deep learning models. This is the correct choice because the question specifically asks for a feature to enable distributed training across multiple GPUs, which is exactly what these libraries are designed for.

Answer

SageMaker Managed Spot Training

Answer

SageMaker Hyperparameter Tuning

Answer

SageMaker Automatic Model Tuning