MLS-C01 Mock Exam 86 — 25 Questions

Question 1

A data engineer uses AWS Glue to run ETL jobs that transform data from JSON to Parquet. The job runs successfully but takes 30 minutes longer than expected. CloudWatch metrics show high memory utilization and disk spills. What is the most likely cause?

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

Accepted Answer

The data is skewed and not evenly distributed across partitions. High memory utilization and disk spills in AWS Glue indicate that the data is not evenly distributed across partitions, causing some executors to handle a disproportionate amount of data. This data skew leads to excessive spilling to disk as memory is exhausted, which significantly slows down the job. Option D directly addresses this root cause, as skewed data prevents efficient parallel processing.

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The number of DPUs is too low

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The sink bucket has insufficient I/O throughput

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The source data format is too large

Question 2

A company is using Amazon SageMaker to train a large language model with billions of parameters. The training job uses multiple GPU instances in a distributed fashion. The training is converging but the loss is not decreasing as expected. The data scientist suspects that the learning rate is too high. Which technique should the data scientist use to automatically adjust the learning rate during training?

Accepted Answer

Implement learning rate scheduling with a cosine annealing schedule. Learning rate scheduling, such as a cosine annealing schedule, can automatically reduce the learning rate over time. This helps the model converge better. SageMaker's built-in algorithms support learning rate scheduling, or the user can implement it in custom training scripts.

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Use a fixed learning rate and train for more epochs

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Increase the batch size to reduce variance

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Use gradient clipping

Question 3

Which TWO of the following are valid approaches to handle missing values in a dataset for a machine learning model?

Accepted Answer

Impute missing values with the mean of the column. Removing rows with missing values is a valid approach (listwise deletion). Imputing with the mean is also valid. Using a neural network to predict missing values is possible but not standard. Standardization does not handle missing values. One-hot encoding is for categorical variables.

Answer

Use a neural network to predict missing values

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Standardize the features to handle missing values

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Apply one-hot encoding to convert missing values

Question 4

A data scientist is training a deep neural network on Amazon SageMaker. The training is taking a long time and the data scientist wants to speed it up. Which THREE actions can help reduce training time?

Accepted Answer

Use GPU instances instead of CPU instances. GPU instances (e.g., P3, P4d) are optimized for the massively parallel matrix operations required by deep neural networks, providing orders-of-magnitude faster computation than CPU instances for training tasks. By offloading tensor operations to GPU cores, the training time is significantly reduced, especially for large models and datasets.

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Increase the batch size

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Use a smaller instance type

Question 5

A data scientist is building a classification model to predict customer churn. The dataset has 10,000 samples with 100 features. After training a logistic regression model, the scientist observes that the model has high variance (overfitting). Which technique can reduce overfitting?

Accepted Answer

Use L2 regularization (Ridge). L2 regularization (Ridge) adds a penalty on large coefficients, reducing overfitting. Removing features may help but is not the best practice. Increasing model complexity (polynomial features) would worsen overfitting. Increasing training data helps but not listed.

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Remove the regularization term

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Add polynomial features

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Use a smaller learning rate

Question 6

A company is using AWS Glue to run ETL jobs that transform data from multiple sources into a data lake on S3. The jobs are scheduled to run hourly. Recently, the jobs have been failing intermittently with 'MemoryError' exceptions. The data volume has grown over time. The data engineer needs to resolve this issue cost-effectively. Which action should be taken?

Accepted Answer

Increase the number of DPUs allocated to the Glue job and use a larger worker type.. The 'MemoryError' exception indicates that the Glue job is running out of memory as data volume grows. Increasing the number of DPUs (Data Processing Units) and using a larger worker type (e.g., from Standard to G.1X or G.2X) provides more memory and compute capacity per worker, allowing the job to handle larger datasets without failing. This is the most cost-effective approach because it scales resources only as needed, avoiding over-provisioning.

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Increase the S3 timeout settings in the Glue job configuration.

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Switch the Glue job type from Spark to Python shell to reduce memory overhead.

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Repartition the data using Spark's repartition method before processing.

Question 7

A data scientist is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket encrypted with AWS KMS. Which TWO actions are necessary to allow SageMaker to access the data?

Accepted Answer

Ensure the SageMaker execution role has s3:GetObject permission.. A is correct because the SageMaker execution role must have the s3:GetObject permission to read objects from the S3 bucket. Without this IAM permission, SageMaker cannot retrieve the training data, even if the bucket is otherwise accessible.

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Enable S3 Transfer Acceleration.

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Set up a VPC endpoint for S3.

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Add a bucket policy allowing SageMaker access.

Question 8

A machine learning team is building a fraud detection system using Amazon SageMaker. The training data is highly imbalanced (99% legitimate, 1% fraudulent). They need to maximize the recall of the fraud class while keeping precision above 90%. Which approach should they take?

Accepted Answer

Train a model using the original data, then adjust the decision threshold on the validation set to maximize recall while precision > 90%. Adjusting the decision threshold on the validation set directly optimizes the trade-off between recall and precision. By lowering the threshold, the model classifies more instances as fraud, increasing recall, while the precision constraint (≥90%) ensures the threshold is set at a point where false positives remain acceptably low. This approach works with any probabilistic classifier and does not alter the training data distribution.

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Undersample the majority class to create a balanced dataset and train a Random Forest

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Train an XGBoost model with scale_pos_weight parameter set to 99

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Use SMOTE to oversample the fraud class and then train a logistic regression

Question 9

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance. A data scientist is trying to invoke the endpoint 'my-endpoint' from the notebook but receives an AccessDenied error. What is the likely cause?

Accepted Answer

The policy is not attached to the IAM role used by the notebook instance.. The error 'AccessDenied' when invoking a SageMaker endpoint from a notebook instance typically indicates that the IAM role attached to the notebook does not have the required permissions. The policy shown in the exhibit grants sagemaker:InvokeEndpoint for the specific endpoint ARN, but if the policy is not attached to the IAM role that the notebook instance is using, the role lacks the permission, resulting in the AccessDenied error. Attaching the policy to the correct IAM role resolves the issue.

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The policy allows InvokeEndpoint only for endpoints with the exact ARN, but the endpoint ARN is different.

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The policy uses a wildcard for CreateEndpoint, which is too permissive.

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The policy does not allow sagemaker:CreateEndpoint for the specific endpoint.

Question 10

A data scientist is evaluating a linear regression model. Which TWO metrics are appropriate for evaluating the model's performance?

Accepted Answer

R-squared. R-squared is a standard metric for linear regression that measures the proportion of variance in the dependent variable explained by the independent variables. It ranges from 0 to 1, with higher values indicating better fit, making it directly appropriate for evaluating regression model performance.

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Precision

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Area Under the ROC Curve (AUC-ROC)

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F1 score

Question 11

A company is using Amazon Kinesis Data Firehose to load streaming data into Amazon S3. The data is in JSON format, and they want to convert it to Parquet before storage. What should they configure?

Accepted Answer

Enable data format conversion in Firehose and specify a Glue table. Amazon Kinesis Data Firehose supports built-in data format conversion from JSON to Parquet or ORC. By enabling this feature and specifying an AWS Glue table that defines the schema, Firehose automatically converts incoming JSON records to Parquet before delivering them to the S3 destination. This eliminates the need for additional compute resources or post-processing steps.

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Use an AWS Lambda function to transform the data

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Run an AWS Glue ETL job after data is in S3

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Use Kinesis Data Analytics for Apache Flink to convert the format

Question 12

A data scientist wants to deploy a PyTorch model for real-time inference. Which SageMaker deployment option provides the lowest latency for single-digit millisecond responses?

Accepted Answer

SageMaker Real-Time Inference endpoint. SageMaker Real-Time Inference endpoints (Option A) are optimized for low-latency, real-time predictions, often achieving single-digit millisecond response times because they maintain a persistent endpoint with pre-warmed instances. Option B (SageMaker Asynchronous Inference) is designed for non-real-time workloads with higher latency due to queuing. Option C (SageMaker Serverless Inference) can introduce cold starts and higher latency, especially for sporadic traffic. Option D (SageMaker Batch Transform) is for offline batch processing and not suitable for real-time inference.

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SageMaker Asynchronous Inference

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SageMaker Serverless Inference

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SageMaker Batch Transform

Question 13

A data scientist is building a recommendation system using matrix factorization. The dataset has 1 million users and 100,000 items, with a sparse user-item interaction matrix. The scientist wants to minimize training time on Amazon SageMaker. Which algorithm would be most appropriate?

Accepted Answer

Factorization Machines. Factorization Machines (B) are specifically designed for sparse, high-dimensional datasets like the user-item interaction matrix in recommendation systems. They extend matrix factorization by modeling pairwise feature interactions, which is ideal for collaborative filtering tasks. On Amazon SageMaker, the built-in Factorization Machines algorithm is optimized for sparse data and can train efficiently on 1 million users and 100,000 items, minimizing training time compared to general-purpose algorithms.

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Linear Learner

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K-Means

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XGBoost

Question 14

A data scientist is building a binary classification model to predict customer churn. The dataset is highly imbalanced, with only 5% of customers churning. The scientist evaluates several models using accuracy, precision, recall, and F1 score. Which metric is most appropriate for comparing model performance in this scenario?

Accepted Answer

F1 score. F1 score is the harmonic mean of precision and recall and is suitable for imbalanced datasets where accuracy can be misleading. Accuracy would be high even if the model predicts no churn ever (95% accuracy). Precision and recall each consider only one aspect, but F1 balances both.

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Accuracy

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Precision

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Recall

Question 15

A data scientist is training a binary classification model on a dataset where the positive class represents only 1% of the data. The model's accuracy is 99%, but the recall for the positive class is 0%. Which metric should the scientist use to evaluate the model's performance effectively?

Accepted Answer

Area under the Precision-Recall curve (PR AUC). In a highly imbalanced dataset where the positive class is only 1%, accuracy is misleading because a model can achieve 99% accuracy by simply predicting the negative class for all samples, resulting in 0% recall for the positive class. The Area under the Precision-Recall curve (PR AUC) is the correct metric because it focuses on the performance of the positive class by evaluating the trade-off between precision and recall, making it sensitive to changes in the minority class. Unlike ROC AUC, which can be overly optimistic in imbalanced settings due to the large number of true negatives, PR AUC provides a more realistic assessment of model performance for rare events.

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Area under the ROC curve (ROC AUC)

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Accuracy

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F1 score

Question 16

A data scientist is building a binary classification model to predict customer churn. The dataset has 10,000 samples with 500 churners (positive class). Which TWO techniques should be used to address the class imbalance? (Choose 2.)

Accepted Answer

Use random undersampling of the majority class. Random undersampling of the majority class (Option C) reduces the number of non-churner samples to balance the dataset, preventing the model from being biased toward the majority class. SMOTE (Option D) generates synthetic samples for the minority class by interpolating between existing minority instances, which increases the representation of churners without simply duplicating data. Both techniques directly address class imbalance by modifying the training data distribution.

Answer

Use a higher learning rate during training

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Use L1 regularization on the model

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Use principal component analysis (PCA) to reduce dimensionality

Question 17

A team is building a data pipeline to process terabytes of log data daily using Amazon EMR. The data arrives in 5-minute windows and must be available for querying within 30 minutes. The data is originally in gzip-compressed CSV files. Which approach will minimize processing time and cost?

Accepted Answer

Use Amazon EMR with Spark to convert data to Parquet and store in S3, using spot instances for task nodes.. Converting gzip-compressed CSV to Parquet reduces storage size and improves query performance due to columnar storage and predicate pushdown. Using spot instances for task nodes significantly lowers compute cost, while the 30-minute SLA is achievable with Spark on EMR processing 5-minute windows of data.

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Use Amazon EMR with Spark to convert data to Parquet and use on-demand instances.

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Use AWS Glue to convert data to gzip-compressed CSV and query with Athena.

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Use Amazon EMR with Hive to transform data to compressed CSV and store in S3.

Question 18

A company has deployed a real-time inference endpoint using SageMaker for a fraud detection model. The model uses a Random Forest classifier. The endpoint receives predictions but the latency is too high. The metric shows p99 latency of 500ms, but the requirement is under 200ms. The team has already optimized the instance type to the maximum allowed by their budget. The data scientist suggests: A) Reducing the number of trees in the Random Forest model. B) Switching to a linear model like Logistic Regression. C) Enabling SageMaker's batch transform instead of real-time endpoint. D) Adding more instances to the endpoint behind a load balancer. Which option will MOST effectively reduce latency while maintaining acceptable accuracy?

Accepted Answer

Reduce the number of trees in the Random Forest model. (Reducing the number of trees) is the most effective method to reduce latency while maintaining acceptable accuracy. Fewer trees directly decrease inference time of the Random Forest model, although it may slightly impact accuracy. Switching to a linear model (Option A) would reduce latency but likely result in significant accuracy loss. Batch transform (Option C) is not suitable for real-time inference. Adding more instances (Option D) improves throughput but not per-request latency.

Answer

Switch to a linear model like Logistic Regression

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Enable SageMaker's batch transform

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Add more instances to the endpoint

Question 19

A company is deploying a machine learning model using Amazon SageMaker. To reduce costs, they want to use SageMaker Managed Spot Training. Which THREE conditions must be met for the training job to use spot instances? (Choose THREE.)

Accepted Answer

The training job must be able to handle interruptions gracefully. For SageMaker Managed Spot Training, the training job must be able to handle interruptions (B) because spot instances can be reclaimed. The chosen instance type must be available in the spot market (C). Additionally, the training script should save checkpoints to S3 periodically (D) to resume training if interrupted. Option A is incorrect because spot training is for training, not deploying endpoints. Option E is not required; training can run inside or outside a VPC.

Answer

The model must be deployed to a serverless endpoint

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The training job must be configured to run in a VPC

Question 20

A company is using Amazon Redshift for data warehousing. The data engineering team observes that query performance degrades over time due to data skew. Which three strategies should the team implement to improve performance?

Accepted Answer

Choose appropriate distribution keys based on join and group-by columns.. Choosing appropriate distribution keys based on join and group-by columns minimizes data movement across nodes during query execution. In Amazon Redshift, data is distributed across compute nodes according to the distribution key; aligning it with frequently joined or aggregated columns ensures that related rows are co-located on the same slice, reducing network shuffling and improving query performance.

Answer

Increase the number of nodes in the Redshift cluster.

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Drop unused indexes on large tables.