MLS-C01 Practice Test 5 — 25 Questions

Question 1

A company is using AWS Glue to catalog metadata from various data sources. The crawler is configured to run daily. However, the catalog is not reflecting new partitions added to an S3 bucket during the day. What is the MOST likely cause?

Accepted Answer

The crawler is not scheduled frequently enough to capture changes. Option C is correct because the crawler is configured to run daily, but new partitions are being added to the S3 bucket throughout the day. Since the crawler only runs once per day, it will not detect and catalog those new partitions until its next scheduled run. To capture changes more frequently, the crawler schedule should be increased or an event-driven trigger (e.g., using Amazon S3 Events and AWS Lambda) should be implemented.

Answer

The S3 bucket has insufficient permissions for the Glue crawler

Answer

The table schema has changed and the crawler does not update it

Answer

The data format is not supported by AWS Glue

Question 2

A company is using AWS Glue ETL jobs to transform data. The jobs are failing due to insufficient memory. The data processing involves complex joins and aggregations. Which THREE actions can improve job performance and reduce memory usage?

Accepted Answer

Filter and project data early in the transformation to reduce data volume. Option A is correct because filtering and projecting data early in the transformation reduces the volume of data that must be processed in subsequent operations like joins and aggregations. By using pushdown predicates and selecting only necessary columns, you minimize the data shuffled across the cluster, which directly reduces memory pressure and improves job performance in AWS Glue ETL.

Answer

Decrease the number of DPUs allocated to the job

Answer

Use a single node cluster to avoid shuffle overhead

Question 3

A company is building a real-time fraud detection system using Amazon SageMaker. The model is a gradient boosting classifier trained on 500 GB of transactional data. The inference endpoint is deployed as a SageMaker real-time endpoint using an ml.c5.9xlarge instance. The model is serialized using the native format of the framework (XGBoost). The endpoint receives about 100 requests per second with an average payload size of 10 KB. The company observes that the endpoint's latency is around 200 ms, but they need under 100 ms. The data scientist profiles the endpoint and finds that the model inference time is 50 ms, but the remaining time is spent on data preprocessing and serialization/deserialization. The preprocessing involves converting JSON input to a NumPy array and then to a DMatrix. Which action is most likely to reduce latency to meet the requirement?

Accepted Answer

Use a more efficient serialization format such as Apache Arrow or Protocol Buffers for the input data. Option A is correct because the bottleneck is data preprocessing and serialization/deserialization, not model inference. Using a more efficient serialization format like Apache Arrow or Protocol Buffers reduces the overhead of converting JSON to NumPy arrays and DMatrix, directly cutting the 150 ms spent outside inference. This targets the root cause without changing the model or infrastructure.

Answer

Switch to SageMaker Batch Transform to process requests in batches

Answer

Use a larger instance type such as ml.c5.18xlarge

Answer

Reduce the number of trees in the model

Question 4

A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data must be processed and stored in S3 in near real-time. Which THREE services can be used together to achieve this?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics is correct because it can process streaming clickstream data in real-time using SQL or Apache Flink, enabling transformations, aggregations, and filtering before the data is delivered downstream. It integrates directly with Kinesis Data Streams as a source and can output processed records to Kinesis Data Firehose for storage in Amazon S3, achieving near real-time processing and storage.

Answer

AWS Glue ETL

Answer

Amazon EMR

Question 5

A data engineer is designing a data pipeline that uses Amazon S3 events to trigger an AWS Lambda function for processing. The pipeline must handle high throughput with low latency. Which TWO configurations should be applied?

Accepted Answer

Configure Lambda with reserved concurrency. Reserved concurrency ensures that the Lambda function always has a guaranteed number of concurrent executions available, preventing it from being throttled by other functions in the same AWS account. This is critical for high-throughput, low-latency pipelines because S3 event notifications can burst many invocations simultaneously, and without reserved concurrency, the function might hit the account-level concurrency limit and drop events.

Answer

Use an SQS queue between S3 and Lambda

Answer

Place Lambda in a VPC to reduce network latency

Answer

Use Amazon Kinesis Data Streams as an intermediary

Question 6

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

Question 7

A data scientist is examining a dataset for a binary classification problem. The target variable has a 1:1000 imbalance. Which technique should be used to assess model performance during exploratory data analysis?

Accepted Answer

Area under the Precision-Recall curve. With a 1:1000 class imbalance, the positive class is extremely rare. The Area Under the Precision-Recall curve (AUPRC) focuses on the performance of the positive class and is sensitive to changes in precision and recall, making it a robust metric for imbalanced datasets. Unlike ROC AUC, which can be overly optimistic when negatives dominate, AUPRC provides a realistic assessment of model performance on the minority class.

Answer

F1 score

Answer

Area under the ROC curve

Answer

Cohen's kappa

Question 8

A data scientist is using Amazon SageMaker to train a model, but the training job fails with an 'Out of memory' error. The instance type is ml.p3.2xlarge. Which action should the data scientist take to resolve the issue?

Accepted Answer

Reduce the mini-batch size in the training script.. The 'Out of memory' error on a single ml.p3.2xlarge instance indicates that the GPU memory is insufficient for the current workload. Reducing the mini-batch size directly decreases the memory footprint per training step, allowing the model to fit within the available GPU memory without changing the instance type or incurring additional costs.

Answer

Use Pipe input mode.

Answer

Increase the number of instances.

Answer

Use a Spot instance.

Question 9

A company's machine learning model is overfitting to the training data. The data scientist has already tried reducing the model complexity and adding regularization, but the model still overfits. Which technique could the data scientist use to further reduce overfitting?

Accepted Answer

Use data augmentation to increase the training dataset size. Data augmentation artificially increases the size and diversity of the training dataset by applying transformations (e.g., rotations, flips, noise injection) to existing samples. This exposes the model to more varied examples, reducing its tendency to memorize noise and improving generalization — directly countering overfitting when other methods have failed.

Answer

Decrease the batch size

Answer

Increase the number of training epochs

Answer

Increase the learning rate

Question 10

A company uses Amazon SageMaker to deploy a model for real-time inference. The endpoint uses an ml.m5.large instance with automatic scaling based on CPU utilization. The team notices that during traffic spikes, the endpoint returns 5xx errors. What should the team do to improve the endpoint's availability?

Accepted Answer

Increase the instance type to ml.c5.2xlarge.. The correct answer is A because upgrading the instance type from ml.m5.large to ml.c5.2xlarge provides more CPU and memory resources, which directly addresses the root cause of 5xx errors during traffic spikes — insufficient compute capacity to handle the request load. Automatic scaling based on CPU utilization may not react quickly enough to sudden spikes, leading to request queuing and timeouts that manifest as 5xx errors. A larger instance type increases the baseline throughput, reducing the likelihood of resource exhaustion before scaling can take effect.

Answer

Reduce the scaling cooldown period.

Answer

Place an Application Load Balancer in front of the endpoint.

Answer

Use Amazon API Gateway to throttle requests.

Question 11

A data scientist wants to understand the relationship between a categorical feature with 3 levels and a continuous target variable. Which visualization is most appropriate?

Accepted Answer

Box plot grouped by category. A box plot grouped by category (Option C) is the most appropriate visualization because it directly compares the distribution of a continuous target variable across the three levels of a categorical feature. It displays median, quartiles, and potential outliers for each group, making it ideal for understanding central tendency, spread, and skewness in a side-by-side comparison.

Answer

Correlation matrix

Answer

Line chart

Answer

Scatter plot

Question 12

Match each AWS service to its primary purpose in a machine learning pipeline.

Accepted Answer

Amazon SageMaker: Fully managed service for building, training, and deploying machine learning models.. The correct matches associate each service with its primary ML pipeline role. SageMaker is for end-to-end ML, Lambda for serverless inference, S3 for data/model storage, Glue for data preparation. Distractors confuse these roles.

Answer

Amazon SageMaker: Serverless compute service for inference.

Answer

AWS Lambda: Scalable object storage for models.

Question 13

Match each data format to its typical use in AWS ML.

Accepted Answer

CSV: Tabular data for built-in algorithms. Common data formats in AWS ML: CSV for tabular data, JSON for semi-structured inference I/O, Parquet for columnar analytics, and TFRecord for TensorFlow training. Distractors confuse CSV with Parquet and JSON with binary formats.

Answer

CSV: Columnar storage for analytics

Answer

JSON: Binary format for efficient I/O

Question 14

A data scientist is analyzing a dataset with a target variable that is heavily imbalanced (e.g., 99% negative class, 1% positive class). Which exploratory data analysis technique is most appropriate to understand the relationship between features and the target before modeling?

Accepted Answer

Use stratified sampling to create a balanced subset, then compute correlation matrices and box plots.. Option C is correct because stratified sampling preserves the class distribution in the sample, allowing you to create a balanced subset for exploratory analysis. Computing correlation matrices and box plots on this balanced subset reveals feature-target relationships without being overwhelmed by the majority class, which is critical for imbalanced datasets like 99% negative vs. 1% positive.

Answer

Randomly sample 10% of the data and plot feature distributions by class.

Answer

Apply PCA to reduce dimensionality, then visualize the first two components.

Answer

Focus only on the majority class features to avoid bias.

Question 15

Which TWO of the following are benefits of feature scaling for machine learning algorithms?

Accepted Answer

Faster convergence of gradient descent. Option D is correct because feature scaling, typically via standardization (z-score) or min-max normalization, ensures that gradient descent converges faster. Without scaling, features with larger numerical ranges dominate the gradient updates, causing the algorithm to oscillate and require more iterations to reach the optimum. Scaling produces a more spherical contour of the loss function, allowing gradient descent to take more direct steps toward the minimum.

Answer

Eliminates the effect of outliers

Answer

Reduces the need for feature selection

Answer

Improves performance of decision tree algorithms

Question 16

A company is using SageMaker to host a model that performs real-time fraud detection. The model receives high request volumes with occasional spikes. The company wants to ensure that the endpoint can handle spikes without throttling while minimizing cost. Which scaling strategy should be used?

Accepted Answer

Use a target tracking scaling policy with a target value of 70% for the SageMakerVariantInvocationsPerInstance metric.. A target tracking scaling policy with the SageMakerVariantInvocationsPerInstance metric is the correct choice because it automatically adjusts the instance count to maintain a target utilization (e.g., 70%), handling spikes without manual intervention while minimizing cost by scaling down during low traffic. This is the recommended approach for real-time endpoints with variable traffic, as it aligns with AWS best practices for dynamic scaling.

Answer

Use a simple scaling policy with a step adjustment based on the InvocationsPerInstance metric.

Answer

Manually adjust the instance count based on monitoring dashboards.

Answer

Use a scheduled scaling action to add instances during peak hours.

Question 17

In exploratory data analysis, a data scientist notices that the distribution of a feature 'income' is heavily right-skewed. Which transformation is most appropriate to reduce skewness?

Accepted Answer

Log transformation.. Log transformation is the most appropriate technique to reduce right skewness in a feature like 'income' because it compresses the long tail of high values while expanding the lower end, making the distribution more symmetric. This is particularly effective for income data, which often follows a log-normal distribution, and is a standard preprocessing step in machine learning to improve model performance.

Answer

Standardization (z-score).

Answer

Square transformation.

Answer

Min-max scaling.

Question 18

A data scientist is using SageMaker to train a model and wants to track experiments, including hyperparameters and metrics. Which TWO actions should the scientist take to set up experiment tracking? (Choose TWO.)

Accepted Answer

Use the SageMaker Experiments Python SDK to create an experiment and log runs.. Option A is correct because the SageMaker Experiments Python SDK provides the primary interface for creating and managing experiments, allowing the data scientist to log runs, hyperparameters, and metrics in a structured way. This SDK directly integrates with SageMaker training jobs and notebook executions to capture experiment metadata.

Answer

Enable SageMaker Model Monitor to track training metrics.

Answer

Configure CloudWatch Logs to store experiment data.

Answer

Enable SageMaker Studio to automatically capture experiments.

Question 19

A data scientist is training a binary classification model on a highly imbalanced dataset where the positive class represents only 1% of the data. The model achieves 99% accuracy but only identifies 5% of the actual positives. Which metric should the data scientist use to evaluate model performance?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positives correctly identified by the model. With only 5% of positives detected, recall is 0.05, which directly reveals the model's failure to capture the minority class despite high accuracy. In imbalanced datasets, accuracy is misleading because the model can achieve 99% accuracy by simply predicting the majority class (negative) for all instances.

Answer

Mean squared error

Answer

Accuracy

Answer

Precision

Question 20

A team is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires GPU for inference. The endpoint must handle variable traffic patterns with minimal latency. Which deployment strategy should the team use?

Accepted Answer

Use a SageMaker multi-model endpoint with GPU instance type.. B is correct because SageMaker multi-model endpoints (MMEs) allow multiple models to be hosted on a single GPU-backed endpoint, dynamically loading and unloading models from disk to GPU memory as needed. This reduces cost and cold-start latency compared to single-model endpoints, while still providing GPU acceleration for deep learning inference. MMEs are ideal for variable traffic patterns because they can scale horizontally and share GPU resources efficiently.

Answer

Deploy a single model endpoint with an auto-scaling policy.

Answer

Deploy a serverless endpoint using SageMaker Serverless Inference.

Answer

Use SageMaker Batch Transform to process requests in batches.