MLS-C01 Practice Test 4 — 10 Questions

Question 1

A training job log shows this error. The training instance is an ml.m5.large with 8 GB EBS storage. The training data is 500 MB, and the model size is expected to be 200 MB. What is the most likely cause?

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

Accepted Answer

The training process is generating large temporary files that fill the instance's local storage. The training job runs on an ml.m5.large instance with only 8 GB of EBS storage. Given that the training data is 500 MB and the model size is 200 MB, there is ample space for those, but the error indicates a lack of storage space. This is most likely caused by temporary files (e.g., checkpoints, logs, or intermediate data) generated during training that fill up the remaining storage. Option A is incorrect because a download issue would show errors like 'Download failed' or 'NoSuchKey', not a storage error. Option B is incorrect because S3 permission issues would result in 'AccessDenied' errors. Option C is incorrect because insufficient RAM would cause a 'MemoryError' or out-of-memory kill, not a storage space error.

Answer

The training data is not fully downloaded from S3 before processing

Answer

The S3 bucket does not have write permissions

Answer

The training instance does not have enough RAM

Question 2

A financial services company is developing a fraud detection model using gradient boosting. The dataset contains 10 million transactions with 0.1% fraudulent. The model is trained on a SageMaker ml.m5.2xlarge instance and takes 8 hours. The team needs to reduce training time without sacrificing model performance. They have permission to use up to 4 instances. What should they do?

Accepted Answer

Switch to a built-in XGBoost with GPU support and use a p3.2xlarge instance. GPU instances like p3.2xlarge accelerate XGBoost training substantially.

Answer

Use SageMaker hyperparameter tuning to find faster hyperparameters

Answer

Use SageMaker's distributed training with data parallelism across 4 ml.m5.2xlarge instances

Answer

Use SageMaker managed spot training with checkpointing

Question 3

A machine learning engineer is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket encrypted with AWS KMS. The SageMaker training job fails with an AccessDenied error when trying to read the data. Which IAM policy addition should resolve the issue?

Accepted Answer

Add kms:Decrypt permission for the KMS key.. When an S3 bucket is encrypted with AWS KMS, the SageMaker training job's execution role must have the `kms:Decrypt` permission for the specific KMS key to read the encrypted objects. Without this permission, the job fails with an AccessDenied error, even if `s3:GetObject` is granted, because SageMaker must decrypt the data before reading it.

Answer

Add s3:GetObject permission for the bucket.

Answer

Add kms:GenerateDataKey permission for the key.

Answer

Attach the AmazonSageMakerFullAccess policy.

Question 4

A company wants to build a machine learning model to predict customer churn. The dataset includes customer demographics, usage patterns, and support interactions. The data is stored in Amazon S3. The data scientist needs to perform feature engineering, including creating aggregate features from support interactions and encoding categorical variables. Which AWS service is most suitable for building the feature engineering pipeline?

Accepted Answer

Amazon SageMaker Processing. Amazon SageMaker Processing is the most suitable service because it is purpose-built for data preprocessing and feature engineering within the SageMaker ecosystem. It allows you to run custom Python scripts (e.g., using pandas or PySpark) on managed infrastructure to create aggregate features from support interactions and encode categorical variables, and it integrates seamlessly with SageMaker for model training and deployment.

Answer

AWS Glue

Answer

Amazon EMR

Answer

AWS Batch

Question 5

A company is building a data lake on Amazon S3 and wants to use AWS Glue to catalog the data. The data includes CSV, Parquet, and JSON files. The team wants to ensure that the Glue crawler can infer the schema correctly and update the Data Catalog when new partitions are added. Which crawler configuration should be used?

Accepted Answer

Create a single crawler that includes all file extensions and set the 'Update all new and existing partitions' option.. A single AWS Glue crawler can handle multiple file formats (CSV, Parquet, JSON) in a data lake on Amazon S3, and enabling 'Update all new and existing partitions' ensures the Data Catalog is refreshed with both new partitions and any schema changes in existing partitions. This configuration maintains a consistent and up-to-date catalog without manual intervention, which is essential for downstream analytics and machine learning workloads.

Answer

Create separate crawlers for each file format and schedule them at different times.

Answer

Use a crawler that only catalogs Parquet files because they are more efficient.

Answer

Use a crawler with 'Update all new and existing partitions' disabled to avoid schema conflicts.

Question 6

A data scientist is analyzing a dataset with a continuous target variable and suspects that the relationship between a predictor and the target is non-linear. Which THREE techniques can the scientist use to explore and model this non-linearity?

Accepted Answer

Add polynomial features (e.g., x^2, x^3) and check if model performance improves.. Options C, D, and E are correct. Adding polynomial features (e.g., x^2, x^3) allows a linear model to capture non-linear relationships. Decision tree regressors naturally model non-linear interactions between predictors and the target. A scatter plot with a LOESS smooth curve visually reveals non-linear patterns in the data. Option A (logistic regression) is incorrect because it is for binary classification, not for exploring non-linearity with a continuous target. Option B (Pearson correlation) only measures linear relationships, so it is not suitable for detecting non-linearity.

Answer

Apply logistic regression to binarize the target.

Answer

Compute the Pearson correlation coefficient between the predictor and target.

Question 7

Match each AWS AI service to its capability.

Accepted Answer

Amazon Comprehend: Extract insights and relationships from text. The correct matches are: Amazon Comprehend for extracting insights from text, Amazon Lex for building conversational interfaces, and Amazon Polly for text-to-speech. Distractors swap capabilities between services.

Answer

Amazon Lex: Translate text between languages

Answer

Amazon Polly: Convert speech to text

Question 8

A data scientist is training a binary classification model on an imbalanced dataset (95% negative class, 5% positive class). The model achieves 95% accuracy but only predicts the negative class for all examples. Which metric should the scientist use to evaluate model performance more appropriately?

Accepted Answer

AUC-ROC. AUC-ROC is robust to class imbalance because it evaluates the model's ability to discriminate between positive and negative classes across all classification thresholds, rather than relying on a single threshold. In this scenario, the model predicts only the negative class, so its true positive rate is 0 and false positive rate is 0, yielding an AUC-ROC of 0.5 (random performance), which correctly reflects the model's lack of predictive power.

Answer

F1 score

Answer

Mean squared error

Answer

Accuracy

Question 9

A company is building a near-real-time dashboard using data from multiple sources. They need to aggregate millions of events per second with sub-second latency. The architecture must be fully managed and minimize operational overhead. Which service should they use for the aggregation layer?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink.. Amazon Kinesis Data Analytics for Apache Flink is the correct choice because it provides a fully managed, serverless runtime for Apache Flink, which is designed for stateful stream processing at scale. It can aggregate millions of events per second with sub-second latency using exactly-once semantics and built-in checkpointing, meeting the near-real-time dashboard requirements without any infrastructure management.

Answer

AWS Lambda functions triggered by Kinesis Data Streams.

Answer

Amazon EMR with Spark Streaming.

Answer

Amazon Redshift with materialized views refreshed frequently.

Question 10

A company is building a data pipeline to process sensitive customer data. The pipeline uses AWS Glue for ETL and stores results in Amazon S3. The security team requires that all data be encrypted at rest in S3 using customer-managed AWS KMS keys. Additionally, the Glue job must be able to write encrypted data to S3. What should the data engineer do to meet these requirements?

Accepted Answer

Attach a policy to the Glue job's IAM role that includes kms:GenerateDataKey and kms:Decrypt actions for the KMS key.. AWS Glue jobs use an IAM role to interact with AWS services. To write encrypted data to S3 using a customer-managed AWS KMS key, the IAM role must have permissions for `kms:GenerateDataKey` (to request a data key for encryption) and `kms:Decrypt` (to decrypt the data key when reading or writing). This allows the Glue job to encrypt objects at rest in S3 with the specified KMS key, meeting the security team's requirement.

Answer

Use S3 server-side encryption with customer-provided keys (SSE-C).

Answer

Use S3 server-side encryption with SSE-S3, which is enabled by default.

Answer

Configure an S3 bucket policy to enforce encryption and attach it to the Glue job's IAM role.