MLS-C01 Mock Exam 91 — 25 Questions

Question 1

A CloudFormation stack creation failed. The SageMaker endpoint resource shows CREATE_FAILED. What is the most likely issue?

Accepted Answer

The S3 bucket 'my-bucket' does not contain the object 'model.tar.gz'.. A CREATE_FAILED status on a SageMaker endpoint resource during CloudFormation stack creation most commonly indicates that the model artifact specified in the Model definition cannot be located. SageMaker requires the S3 bucket and object path (e.g., 's3://my-bucket/model.tar.gz') to exist and be accessible at the time of model creation. If the object is missing, the model resource fails, cascading to the endpoint creation failure.

Answer

The IAM role used by CloudFormation lacks permissions to create endpoints.

Answer

The SageMaker endpoint configuration is invalid.

Answer

The instance type specified for the endpoint is not available in the region.

Question 2

A company wants to centralize logging from multiple AWS accounts and on-premises servers. The logs must be stored cost-effectively and be searchable. Which TWO services should be used? (Choose TWO.)

Accepted Answer

Amazon Athena. Amazon S3 (option E) provides a cost-effective, durable storage layer for centralized logs, while Amazon Athena (option C) enables serverless SQL-based searching directly on the log data stored in S3 without needing to load it into a separate database. Together, they allow you to store logs cheaply and query them on demand, meeting the requirements of cost-effectiveness and searchability.

Answer

Amazon CloudWatch Logs

Answer

Amazon Redshift

Answer

Amazon Kinesis Data Streams

Question 3

A machine learning team is using Amazon SageMaker to train a linear regression model. The team notices that the training loss decreases rapidly initially but then plateaus at a high value. What is the MOST likely cause?

Accepted Answer

The learning rate is set too high. A learning rate set too high causes the optimizer to take excessively large steps, overshooting the minimum of the loss function. This results in rapid initial decrease as the model makes large corrections, but then the loss plateaus at a high value because the parameters oscillate around the optimum without converging. In SageMaker's linear regression (typically using stochastic gradient descent), a high learning rate prevents fine-grained convergence, leading to a high plateau.

Answer

The model uses batch normalization

Answer

The learning rate is set too low

Answer

The model is over-regularized with L2 regularization

Question 4

A data scientist wants to use Amazon SageMaker to train a deep learning model on a large dataset stored in S3. The training job is expected to take several hours. Which storage option should be used to minimize data loading time and cost?

Accepted Answer

Use Pipe mode to stream data directly from S3 during training. Pipe mode is the correct choice because it streams data directly from S3 into the training algorithm without writing to disk, eliminating the time and cost of copying large datasets to the instance's local storage. This minimizes data loading time (streaming starts immediately) and cost (no EBS volume or additional storage charges), making it ideal for large datasets that take hours to train.

Answer

Attach an Amazon EBS volume with the dataset pre-loaded

Answer

Use File mode to copy data to the training instance's local storage

Answer

Mount an Amazon EFS file system to the training instance

Question 5

A company runs a real-time recommendation system on SageMaker with a model that uses a deep neural network. The endpoint uses a single ml.p3.2xlarge instance. Recently, the number of users has grown, and the endpoint's latency has increased from 50ms to 200ms, exceeding the SLA of 100ms. The model inference code is optimized and cannot be improved further. The company wants to reduce latency while minimizing cost. The data scientist has the following options: A. Switch to a larger instance type with more GPU memory, such as ml.p3.8xlarge. B. Use SageMaker's Elastic Inference to attach an EI accelerator to the existing instance. C. Deploy the model on multiple smaller instances (e.g., ml.p3.2xlarge) behind a load balancer and distribute traffic. D. Convert the model to use TensorFlow Lite and deploy on a CPU-based instance. Which option is the MOST cost-effective and meets the latency requirement?

Accepted Answer

Use SageMaker's Elastic Inference. The most cost-effective option is B (Use SageMaker's Elastic Inference) because it provides dedicated GPU acceleration at a fraction of the cost of a full GPU instance, reducing inference latency without requiring a larger instance. Option C (Switch to a larger instance type) would increase cost significantly. Option D (Deploy on multiple smaller instances behind a load balancer) would increase complexity and cost, and may not guarantee latency reduction. Option A (Convert to TensorFlow Lite on CPU) could reduce cost but may not meet the latency requirement as CPU inference is slower than GPU for deep neural networks, and model conversion might impact accuracy.

Answer

Convert to TensorFlow Lite on CPU

Answer

Switch to a larger instance type, e.g., ml.p3.8xlarge

Answer

Deploy on multiple smaller instances behind a load balancer

Question 6

A company is using Amazon Kinesis Data Streams with a Lambda consumer. The Lambda function writes results to an S3 bucket. The team wants to ensure that each record is processed exactly once and in order. Which TWO configurations should the team implement? (Choose 2.)

Accepted Answer

Set the parallelization factor to 1. Setting the parallelization factor to 1 ensures that each shard of the Kinesis Data Stream is processed by only one Lambda instance at a time, preserving the order of records within that shard. Option E is correct because enabling S3 bucket versioning helps track duplicates: if the same record is written multiple times, S3 versioning creates multiple versions, allowing the system to detect and handle duplicates, contributing to exactly-once processing semantics.

Answer

Set the batch size to 1

Answer

Increase the Lambda function's reserved concurrency

Answer

Configure a dead-letter queue for failed records

Question 7

A financial services company uses Amazon SageMaker to train a fraud detection model. The training data is stored in an S3 bucket encrypted with AWS KMS. The SageMaker training job is configured to use a custom Docker container that reads data from S3 and writes model artifacts back to S3. The training job fails with the error: 'Unable to write model artifact to s3://my-bucket/output/model.tar.gz. Access Denied.' The IAM role used by the training job has the following permissions: s3:GetObject and s3:PutObject on the bucket, and kms:Decrypt on the KMS key. The training job is not using a VPC. What is the MOST likely cause of the failure?

Accepted Answer

The IAM role does not have kms:GenerateDataKey permission on the KMS key. The training job needs kms:GenerateDataKey permission to write objects encrypted with the KMS key. The provided IAM role has s3:GetObject, s3:PutObject, and kms:Decrypt, but lacks kms:GenerateDataKey, causing the Access Denied error when writing. Option A is incorrect because a region mismatch would not necessarily cause an Access Denied error if cross-region access is allowed, and the error indicates a permissions issue. Option C is incorrect because S3 Batch Operations is not required for writing artifacts. Option D is incorrect because the role already includes s3:PutObject.

Answer

The S3 bucket is in a different region than the training job

Answer

The S3 bucket requires S3 Batch Operations for writing artifacts

Answer

The IAM role does not have s3:PutObject permission on the output bucket

Question 8

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 speed up training by using distributed training across multiple GPUs. Which SageMaker feature or configuration should the data scientist use?

Accepted Answer

SageMaker Data Parallelism library. The SageMaker Data Parallelism library is specifically designed to distribute training across multiple GPUs by splitting the input data across workers, which reduces per-GPU computation time and accelerates training for deep learning models. This library uses optimized all-reduce algorithms (e.g., Ring AllReduce) to synchronize gradients efficiently, making it ideal for speeding up image classification tasks that are data-intensive.

Answer

SageMaker Debugger

Answer

Model parallelism in SageMaker

Answer

SageMaker hyperparameter tuning

Question 9

A data scientist is building a binary classifier and wants to evaluate model performance. Which THREE metrics are most commonly used?

Accepted Answer

Precision. Precision is a core metric for binary classifiers, measuring the proportion of true positive predictions among all positive predictions. It is especially important when the cost of false positives is high, such as in spam detection or fraud alert systems.

Answer

Mean Absolute Error

Answer

RMSE

Question 10

Refer to the exhibit. A SageMaker training job uses an IAM role with this policy. The training job writes output to s3://my-bucket/output/. Which statement about the policy is true?

Accepted Answer

The training job can write output objects only if server-side encryption with SSE-S3 is used. The policy includes a Deny statement that explicitly denies PutObject requests unless the request includes the `x-amz-server-side-encryption` header set to `AES256`, which corresponds to SSE-S3. The Allow statement grants PutObject permission, but the Deny statement overrides it for any request that does not meet the encryption condition. Therefore, the training job can only write output objects if server-side encryption with SSE-S3 is used.

Answer

The Allow statement allows all PutObject requests regardless of encryption

Answer

The Deny statement blocks all PutObject requests

Answer

The GetObject permission requires the object to be encrypted with SSE-S3

Question 11

A company wants to build a real-time anomaly detection system for IoT sensor data. The data arrives as a stream of numerical values. The model should adapt to concept drift over time. Which approach is most suitable?

Accepted Answer

Train an online learning model, such as stochastic gradient descent (SGD) with a sliding window. Online learning with stochastic gradient descent (SGD) using a sliding window allows the model to continuously update its parameters as new IoT sensor data arrives, adapting to concept drift without retraining from scratch. The sliding window ensures that the model focuses on the most recent data distribution, discarding outdated patterns, which is essential for real-time anomaly detection in streaming environments.

Answer

Use a static deep learning model trained once on historical data

Answer

Use a stateful LSTM with fixed weights

Answer

Batch train a random forest model monthly

Question 12

A data scientist is building a multi-class classification model with 10 classes. The dataset has 100,000 samples. After training a random forest with 100 trees, the model achieves 85% accuracy on the test set. However, the data scientist notices that for one rare class (1% of data), recall is only 5%. Which technique is MOST likely to improve recall for the rare class without significantly reducing overall accuracy?

Accepted Answer

Apply SMOTE to oversample the rare class in the training data. SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the rare class by interpolating between existing minority instances, which directly addresses the class imbalance. This increases the model's exposure to the rare class during training, improving recall without discarding data or significantly altering the overall class distribution, thus preserving overall accuracy.

Answer

Increase the number of trees to 500

Answer

Use stratified sampling only for the test set

Answer

Reduce the decision threshold for the rare class to 0.1

Question 13

A data scientist is training a binary classification model on an imbalanced dataset where the positive class accounts for 5% of the data. The model achieves 95% accuracy but has a recall of only 10% for the positive class. Which metric should the data scientist primarily use to evaluate model performance?

Accepted Answer

F1 Score. The F1 Score is the harmonic mean of precision and recall, making it ideal for imbalanced datasets where accuracy is misleading. With 95% accuracy but only 10% recall, the model is simply predicting the majority class (negative) almost always, so F1 Score captures the trade-off between false positives and false negatives better than accuracy or AUC-ROC.

Answer

RMSE

Answer

Accuracy

Answer

AUC-ROC

Question 14

A company is building a machine learning pipeline on AWS. The pipeline includes data ingestion, preprocessing, training, and deployment. Which THREE AWS services can be used to orchestrate the pipeline? (Choose THREE.)

Accepted Answer

AWS Glue Workflows. AWS Glue Workflows is correct because it provides a visual orchestration tool for designing and managing complex ETL pipelines, including data ingestion and preprocessing steps. It allows you to define dependencies, triggers, and job sequences, making it suitable for orchestrating the data preparation stages of a machine learning pipeline.

Answer

Amazon CloudWatch

Answer

AWS Lambda

Question 15

A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains missing values. Which preprocessing step should be applied before training?

Accepted Answer

Impute missing values with the mean of the column.. Linear regression models in Amazon SageMaker cannot handle missing values natively; they require complete numerical input. Imputing missing values with the column mean is a standard preprocessing technique that preserves the overall distribution and avoids introducing bias, ensuring the SageMaker built-in Linear Learner algorithm can train without errors.

Answer

Ignore missing values; linear regression can handle them.

Answer

Replace missing values with zeros.

Answer

Remove all rows containing missing values.

Question 16

A company uses SageMaker to train a model. The training job is taking too long and the data scientist wants to speed it up. Which THREE strategies should the data scientist consider? (Select THREE.)

Accepted Answer

Use a GPU instance type like ml.p3.2xlarge. GPU instances like ml.p3.2xlarge are optimized for parallel computation, significantly accelerating the training of deep learning models by handling matrix operations more efficiently than CPUs. SageMaker supports a range of GPU instances (e.g., ml.p3, ml.p4, ml.g5) that can reduce training time for compute-intensive workloads.

Answer

Reduce the number of training epochs

Answer

Increase the batch size in the training script

Question 17

A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains outliers. Which THREE techniques can mitigate the impact of outliers?

Accepted Answer

Remove observations with outlier values from the dataset.. Removing observations with outlier values directly eliminates data points that can disproportionately influence the linear regression coefficients, leading to a more stable and representative model. In Amazon SageMaker, this can be done during data preprocessing using built-in algorithms or custom scripts in a SageMaker Processing job.

Answer

Increase the number of layers in the model.

Answer

Standardize the features to have mean zero and unit variance.

Question 18

A company uses AWS Glue ETL jobs to process data from multiple sources. The job fails with the error: 'An error occurred while calling o123.pyWriteDynamicFrame. Insufficient memory.' The job runs on a G.1X worker type with 10 workers. What should be changed to resolve this error?

Accepted Answer

Increase the number of workers to 20.. The error 'Insufficient memory' in AWS Glue ETL jobs typically indicates that the total memory across all executors is insufficient for the data being processed. Increasing the number of workers from 10 to 20 doubles the total memory and compute capacity available, allowing the job to handle larger datasets without running out of memory. This is the most direct and effective fix for a memory exhaustion error when using the G.1X worker type.

Answer

Enable the Spark UI to monitor the job.

Answer

Change the worker type to G.2X.

Answer

Reduce the number of partitions in the DynamicFrame.

Question 19

Match each AWS security service to its function in ML.

Accepted Answer

AWS KMS: Manages encryption keys for securing ML model data at rest and in transit. In ML, AWS KMS handles encryption keys, IAM manages access, CloudTrail provides audit logs. Common confusions arise because these services often work together, but each has a distinct primary function.

Answer

AWS KMS: Provides identity-based access control for ML resources

Answer

AWS IAM: Encrypts data during ML processing

Answer

AWS CloudTrail: Monitors and enforces compliance rules for ML

Question 20

A company stores sensor data in Amazon S3. A data scientist wants to explore the data using SQL without moving it. Which AWS service should they use?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it is a serverless interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL without any data movement or infrastructure management. Athena uses Presto under the hood and charges only for the data scanned per query, making it ideal for ad-hoc exploratory analysis on sensor data stored in S3.

Answer

Amazon EMR

Answer

Amazon Redshift

Answer

Amazon QuickSight