MLS-C01 Practice Exam 65 — 20 Questions

Question 1

A company uses SageMaker to train a model each night. The training data is stored in an S3 bucket with SSE-S3 encryption. The training job fails with an access denied error. Which configuration is needed?

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

Accepted Answer

Create an IAM role with S3 read access and assign it to the SageMaker training job. The training job fails because SageMaker does not have the necessary permissions to read the data from S3. The solution is to create an IAM role with S3 read access (s3:GetObject) and assign it to the SageMaker training job. Option A is incorrect because VPC endpoints are not required for this error and are unrelated to encryption. Option C is incorrect because SSE-S3 encryption is already enabled and does not require KMS. Option D is incorrect because a bucket policy allowing all principals is insecure and not the proper way to grant permissions to SageMaker.

Answer

Configure the training job to run in a VPC with S3 VPC Endpoint

Answer

Enable SSE-KMS on the S3 bucket

Answer

Add a bucket policy allowing s3:GetObject for all principals

Question 2

An organization stores sensitive customer data in S3. A data pipeline uses AWS Glue to transform the data and load it into Amazon Redshift. The security team requires that data be encrypted at rest in S3 and in transit between S3 and Glue, and between Glue and Redshift. Which configuration meets these requirements?

Accepted Answer

Enable S3 default encryption with SSE-KMS, use a VPC endpoint for S3, and configure Glue to use SSL for Redshift connection.. It ensures encryption at rest in S3 via SSE-KMS, encrypts data in transit between S3 and Glue by using a VPC endpoint (which enforces HTTPS/TLS), and encrypts data in transit between Glue and Redshift by configuring SSL for the Redshift connection. SSE-KMS provides envelope encryption with a customer-managed key, while the VPC endpoint and SSL satisfy the in-transit encryption requirements.

Answer

Use S3 client-side encryption, and use VPC Peering between Glue and Redshift.

Answer

Use S3 default encryption with SSE-KMS, and use Network Load Balancer for Redshift.

Answer

Enable S3 server-side encryption with SSE-S3, and use SSL for both Glue connections.

Question 3

A company is using Amazon SageMaker to build a machine learning pipeline. The pipeline includes data preprocessing, training, and evaluation steps. The company wants to ensure that the pipeline is reproducible and that artifacts are versioned. Which TWO actions should be taken? (Choose TWO.)

Accepted Answer

Use SageMaker Pipelines to create the pipeline and enable versioning on the pipeline artifacts.. SageMaker Pipelines provides a native way to define, orchestrate, and version machine learning pipelines. By enabling versioning on pipeline artifacts (e.g., via the `Pipeline` object's `version` parameter or by using SageMaker Model Registry), each pipeline run is tracked with a unique version, ensuring reproducibility. This directly addresses the requirement for reproducible pipelines and versioned artifacts.

Answer

Use a naming convention for training jobs that includes the date.

Answer

Use AWS CodePipeline to trigger the pipeline on code changes.

Answer

Store the training dataset in a versioned S3 bucket.

Question 4

A company is using Amazon DynamoDB to store sensor data. The data is exported to Amazon S3 using DynamoDB Streams and AWS Lambda for long-term archival. Recently, the Lambda function has been failing due to 'ProvisionedThroughputExceededException' on the DynamoDB stream. What is the most likely cause?

Accepted Answer

The Lambda function is processing records too slowly, causing the stream to throttle.. A is correct because the 'ProvisionedThroughputExceededException' on a DynamoDB stream indicates that the stream's read throughput is being throttled. When a Lambda function processes records too slowly, it cannot keep up with the rate of new records being written to the stream, causing the stream shards to throttle the Lambda consumer. This is a common issue when the Lambda function's processing time per record is high or when the function is invoked with a large batch size that exceeds its processing capacity.

Answer

The DynamoDB stream is disabled.

Answer

The DynamoDB table's write capacity is too low.

Answer

The Lambda function does not have enough memory allocated.

Question 5

An ML team is using SageMaker Processing jobs to run feature engineering scripts. The scripts require a specific Python package not included in the default SageMaker image. How should the team provide this package?

Accepted Answer

Create a custom Docker image that includes the package and use it for the Processing job. SageMaker Processing jobs run in isolated Docker containers, and the default SageMaker images only include pre-installed packages. To add a custom Python package, the team must create a custom Docker image that includes the package (e.g., via a Dockerfile with 'pip install <package>'), then specify that image URI in the Processing job configuration. This ensures the package is available in the container environment before the script executes.

Answer

Include 'pip install <package>' in the processing script

Answer

Use the SageMaker prebuilt deep learning container with the package

Answer

Place a requirements.txt file in the input data S3 bucket

Question 6

A company is deploying a machine learning model for real-time fraud detection using Amazon SageMaker. The model must have a p99 inference latency under 50ms. Which TWO actions should the ML team take to meet the latency requirement?

Accepted Answer

Use SageMaker Neo to compile and optimize the model for the target instance type.. SageMaker Neo compiles and optimizes trained models for specific hardware targets, reducing inference latency by up to 2x without sacrificing accuracy. By applying hardware-specific optimizations such as kernel fusion and memory layout tuning, Neo ensures the model runs efficiently on the chosen instance type, directly helping to achieve sub-50ms p99 latency.

Answer

Use a multi-model endpoint to reduce cold starts.

Answer

Use SageMaker Batch Transform for near-real-time inference.

Answer

Configure automatic scaling to add instances based on CPU utilization.

Question 7

A data scientist is training a linear regression model and wants to check for multicollinearity among the features. Which TWO methods can be used to detect multicollinearity? (Choose TWO.)

Accepted Answer

Compute the correlation matrix between features. Computing the correlation matrix between features directly reveals pairwise linear relationships. High correlation coefficients (e.g., >0.8 or <-0.8) between two predictors indicate potential multicollinearity, which can destabilize coefficient estimates in linear regression.

Answer

Examine the R-squared value of the model

Answer

Check the p-values of the coefficients

Answer

Plot the residuals vs. fitted values

Question 8

A team is training an XGBoost model using SageMaker with a large dataset in S3 (100 GB). Training is taking too long. Which change will most likely reduce training time without sacrificing accuracy?

Accepted Answer

Configure Pipe mode for data input. Configuring Pipe mode for data input streams data directly from S3 to the training algorithm, significantly reducing I/O overhead and training time without affecting model accuracy. Option A (reducing the number of training instances) would actually increase training time, not reduce it. Option C (enabling SageMaker Managed Spot Training) is primarily for cost savings and does not reduce training time. Option D (using a larger instance type) may provide some speedup but is less effective than addressing the I/O bottleneck with Pipe mode, and it may increase costs unnecessarily.

Answer

Reduce the number of training instances

Answer

Enable SageMaker Managed Spot Training

Answer

Use a larger instance type with more vCPUs

Question 9

A company uses Amazon Kinesis Data Streams with a shard count of 5. The data producer sends 1000 records per second, each 1 KB in size. The consumer application reads from the stream using the Kinesis Client Library (KCL) and processes records. The consumer is experiencing high latency and falling behind. What is the most effective way to improve consumer throughput?

Accepted Answer

Increase the number of shards in the stream.. The consumer is falling behind because it cannot process records fast enough. The Kinesis Client Library (KCL) typically runs one consumer thread per shard, so with only 5 shards there are only 5 parallel consumers. To improve throughput, the number of shards should be increased to allow more parallel processing. Options like enhanced fan-out would improve read throughput per consumer but do not increase parallelism; the bottleneck here is processing speed, not read throughput. Increasing shards directly increases the number of consumers and thus overall throughput.

Answer

Switch to Kinesis Data Analytics for processing.

Answer

Use enhanced fan-out to dedicate read throughput to the consumer.

Answer

Increase the record size to 5 KB.

Question 10

A company is using Amazon SageMaker to train a model. The training data includes sensitive personally identifiable information (PII). The company needs to ensure that the training data is protected and that the trained model does not inadvertently expose PII. Which TWO actions should the company take? (Choose TWO.)

Accepted Answer

Encrypt the training data in S3 using AWS KMS. Encrypting the training data in S3 using AWS KMS ensures that the data is protected at rest with customer-managed keys, providing a strong security control for sensitive PII. This encryption prevents unauthorized access to the raw data stored in S3, which is a fundamental requirement for data protection.

Answer

Use server-side encryption with S3-managed keys

Answer

Enable AWS CloudTrail to log all access to the data

Answer

Grant public read access to the training data for faster access

Question 11

Refer to the exhibit. A SageMaker endpoint is returning 5xx errors. The logs show the above error. Which change will most likely resolve the issue?

Accepted Answer

Use a larger instance type with more memory. 5xx errors from a SageMaker endpoint typically indicate that the inference container is running out of memory (OOM) or crashing under load. The error log suggests the model or inference process requires more memory than the current instance type provides. Upgrading to a larger instance type with more memory directly addresses the resource exhaustion, allowing the model to load and inference to complete without failure.

Answer

Reduce the batch size in the inference script

Answer

Enable Auto Scaling on the endpoint

Answer

Compress the model artifact

Question 12

A company is building a multiclass classification model using Amazon SageMaker. The dataset has 100 classes and is highly imbalanced. The model currently achieves high accuracy on the majority classes but poor performance on minority classes. Which technique should the data scientist use to improve minority class performance?

Accepted Answer

Use class weights to penalize misclassifications of minority classes. Class weights assign a higher penalty to misclassifications of minority classes, forcing the model to pay more attention to them and improving recall for minority classes. Option A is wrong because random oversampling with replacement can cause overfitting and does not incorporate minority class importance as directly as class weights. Option B is wrong because PCA is a dimensionality reduction technique and does not address class imbalance. Option D is wrong because removing samples from the majority class (undersampling) can discard useful information and is often less effective than using class weights.

Answer

Apply random oversampling with replacement

Answer

Apply principal component analysis (PCA)

Answer

Remove samples from majority classes

Question 13

A machine learning team is using SageMaker to build a model. They need to track hyperparameter tuning experiments, compare results, and visualize metrics. Which SageMaker feature should they use?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is the correct answer because it provides experiment tracking, comparison, and visualization capabilities for hyperparameter tuning and other training runs. SageMaker Hyperparameter Tuning (option D) only automates the tuning process but does not track or compare experiments. SageMaker Debugger (option E) is used for debugging training issues, not for experiment tracking. SageMaker Model Monitor (option C) monitors deployed models for data drift and quality, not for tracking tuning experiments. SageMaker Ground Truth (option B) is for data labeling. Therefore, only SageMaker Experiments meets all the requirements.

Answer

SageMaker Ground Truth

Answer

SageMaker Model Monitor

Answer

SageMaker Hyperparameter Tuning

Answer

SageMaker Debugger

Question 14

Which THREE considerations are important when designing a data lake on Amazon S3?

Accepted Answer

Setting up S3 Lifecycle policies to transition data to colder storage. S3 Lifecycle policies allow you to automatically transition objects to colder storage classes (e.g., S3 Standard-IA, S3 Glacier) based on age or other rules, reducing storage costs for infrequently accessed data in a data lake. This is a key design consideration for managing data lifecycle and cost efficiency at scale.

Answer

Using Provisioned IOPS for S3

Answer

Using a single Availability Zone for data storage

Question 15

A data engineer needs to transform a large dataset stored in Amazon S3 using Apache Spark. The engineer wants to minimize costs and avoid managing infrastructure. Which AWS service should be used?

Accepted Answer

AWS Glue. AWS Glue is the optimal choice because it provides a serverless Apache Spark environment, fully managed, that allows the engineer to run Spark transformations without provisioning or managing clusters. This meets both requirements: using Apache Spark as specified, and minimizing costs and infrastructure management through its pay‑as‑you‑go, serverless model. Amazon Athena is a SQL query service and does not execute Apache Spark code. Amazon EMR provides Spark but typically requires cluster management (unless using EMR Serverless, which is not as straightforward as Glue for serverless Spark). Amazon SageMaker is focused on machine learning, not general ETL transformations.

Answer

Amazon Athena

Answer

Amazon SageMaker

Answer

Amazon EMR

Question 16

A data engineer uses the IAM policy above for an AWS Lambda function that processes data in S3 and triggers an AWS Glue job. The Lambda function is unable to start the Glue job. What is the most likely cause?

Accepted Answer

The policy does not include iam:PassRole permission.. The Lambda function needs to pass an IAM role to AWS Glue when starting a job, which requires the `iam:PassRole` permission. Without this permission, the `StartJobRun` API call fails even if the Lambda has permissions to invoke Glue. The policy shown lacks this critical permission, making option C the correct answer.

Answer

The Glue job name in the resource ARN is misspelled.

Answer

The policy does not allow s3:PutObject on the bucket.

Answer

The policy does not include s3:GetObject on the bucket.

Question 17

A data scientist is using SageMaker to train a deep learning model. The training job runs on a single GPU instance and is taking too long. Which THREE actions can the data scientist take to reduce training time? (Choose three.)

Accepted Answer

Switch to Pipe input mode to reduce I/O waiting time.. Pipe input mode streams training data directly from Amazon S3 to the GPU instance, reducing I/O waiting time compared to the default File mode, which downloads the entire dataset to the EBS volume first. This minimizes the time the GPU spends idle waiting for data, especially for large datasets that cannot fit entirely in memory.

Answer

Increase the size of the EBS volume attached to the instance.

Answer

Increase the number of instances but keep the same total data.

Question 18

A data scientist is training a neural network on a GPU instance in Amazon SageMaker. The training job fails with an 'OutOfMemoryError'. Which action should the data scientist take to resolve this issue?

Accepted Answer

Reduce the batch size in the training script.. An OutOfMemoryError during GPU training indicates that the GPU's memory is exhausted. Reducing the batch size directly decreases the memory footprint per training step, as fewer samples and their corresponding activations are stored simultaneously. This is the most immediate and effective fix without changing the instance type or training architecture.

Answer

Enable automatic hyperparameter tuning.

Answer

Switch to distributed training across multiple instances.

Answer

Use a smaller instance type with less GPU memory.

Question 19

A data scientist is performing EDA on a dataset with 500 features. The dataset has a mix of numeric and categorical features. The scientist wants to identify which features have a strong nonlinear relationship with the target variable. Which technique is most appropriate?

Accepted Answer

Calculate mutual information between each feature and the target.. Mutual information can capture any kind of dependency (including nonlinear) between features and target. Option A (ANOVA) compares means across groups but assumes linearity. Option B (Pearson correlation) only captures linear relationships. Option D (Chi-squared test) is for categorical features, not suitable for the mix of numeric and categorical features.

Answer

Use ANOVA to compare feature means across target classes.

Answer

Compute Pearson correlation coefficients.

Answer

Perform chi-squared tests for each feature.

Question 20

A company is using Amazon SageMaker to train a deep learning model on a large dataset. The training job is taking too long. The team wants to reduce training time without changing the model architecture. Which action should they take?

Accepted Answer

Use SageMaker's distributed training with multiple instances. SageMaker's distributed training with multiple instances splits the dataset and model computations across several machines, enabling parallel processing that significantly reduces wall-clock training time. This approach leverages data parallelism or model parallelism without altering the model architecture, directly addressing the need for faster training.

Answer

Increase the learning rate by a factor of 10

Answer

Reduce the number of epochs

Answer

Reduce the batch size