MLS-C01 Practice Exam 56 — 20 Questions

Question 1

A company uses SageMaker Ground Truth to label images for object detection. After labeling, they notice that the bounding boxes are often misaligned with the objects. Which action should they take to improve label quality?

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

Accepted Answer

Use a pre-built annotation tool that enforces bounding box alignment. SageMaker Ground Truth offers pre-built annotation tools, such as the bounding box tool, which includes features like snap-to-grid or edge alignment that enforce precise bounding box placement. Using this tool directly improves label quality by reducing human error in manual drawing, ensuring boxes tightly fit objects without manual guesswork.

Answer

Use automated labeling with a pre-trained model

Answer

Increase the number of workers per task

Answer

Adjust the confidence threshold for the model

Question 2

Refer to the exhibit. A SageMaker endpoint logs this error. What is the most likely cause?

Accepted Answer

The input data has fewer features than the model expects. The error log indicates a mismatch between the number of features in the input data and the number of features the model was trained on. SageMaker's inference endpoint validates the input shape against the model's expected feature dimensions; when the input has fewer features, the model cannot perform the matrix operations required for prediction, resulting in this error. Option D correctly identifies this feature count mismatch as the root cause.

Answer

The model is corrupted

Answer

There is a network connectivity issue

Answer

The input data type is incorrect

Question 3

A company uses AWS Glue to run ETL jobs. The data engineer wants to monitor job performance and troubleshoot failures. Which THREE AWS services or features should they use together? (Choose three.)

Accepted Answer

AWS Glue job bookmarks. Correct options: A, D, E. AWS Glue job bookmarks track processed data to avoid reprocessing, Amazon CloudWatch Logs stores Glue job logs for troubleshooting failures, and Amazon CloudWatch metrics provide performance monitoring metrics. Option B (Amazon S3 Event Notifications) can trigger jobs but not monitor performance or troubleshoot. Option C (Amazon Athena) is used for querying data, not for monitoring Glue jobs.

Answer

Amazon S3 Event Notifications

Answer

Amazon Athena

Question 4

A data engineer needs to transfer 50 TB of data from an on-premises HDFS cluster to Amazon S3. The data must be encrypted in transit and at rest. The on-premises network has a 1 Gbps connection to AWS. The transfer must complete within 5 days. Which solution is MOST cost-effective and meets the requirements?

Accepted Answer

Use AWS DataSync with a DataSync agent installed on-premises to transfer the data to S3.. (AWS DataSync). With a 1 Gbps connection, the maximum theoretical transfer in 5 days is about 54 TB (1 Gbps = 0.125 GB/s, 0.125 * 86400 * 5 = 54000 GB = 54 TB), so network transfer is feasible within the time limit. AWS DataSync uses a DataSync agent installed on-premises to transfer data from HDFS to S3, encrypting data in transit (TLS) and at rest (S3 server-side encryption). This is the most cost-effective solution because it avoids the hardware and shipping costs of Snowball Edge (option C). Option A (S3 Transfer Acceleration) does not directly integrate with HDFS and is designed for speeding up uploads over public internet, not for encrypting data from HDFS. Option D (AWS Glue) is an ETL service, not a data transfer solution, and would require additional infrastructure and complexity.

Answer

Use S3 Transfer Acceleration to upload the data directly from HDFS to S3.

Answer

Order an AWS Snowball Edge device and copy the data to it, then ship it back.

Answer

Use AWS Glue to read from HDFS and write to S3 in a continuous ETL job.

Question 5

Refer to the exhibit. A data engineer has attached this IAM policy to an IAM role used by an AWS Glue ETL job. The job reads from an S3 bucket (data-bucket) that is encrypted with SSE-KMS using the key arn:aws:kms:us-east-1:123456789012:key/abc123, transforms the data, and writes the result to a different S3 bucket (output-bucket) encrypted with a different KMS key (arn:aws:kms:us-east-1:123456789012:key/xyz789). When the job runs, it fails with an access denied error. What is the cause?

Accepted Answer

The policy does not grant kms:Encrypt permission for the output bucket's KMS key.. The job fails because the IAM policy grants kms:Decrypt and kms:GenerateDataKey for the input bucket's KMS key (abc123) but does not grant kms:Encrypt or kms:GenerateDataKey for the output bucket's KMS key (xyz789). To write encrypted data to the output bucket, the AWS Glue job must have permission to encrypt using the output KMS key. Option D is correct because the missing kms:Encrypt permission causes the access denied error. Option A is incorrect because the policy includes s3:GetObject for the input bucket. Option B is incorrect because Glue catalog permissions are not relevant to the encryption error. Option C is incorrect because the error is due to missing KMS permissions for the output bucket, not because the policy does not include s3:PutObject.

Answer

The policy does not include s3:GetObject permission for the output bucket.

Answer

The policy does not include glue:CreateTable permission.

Answer

The policy does not include s3:PutObject permission for the output bucket.

Question 6

A deployed SageMaker endpoint is returning high latency. The model is a scikit-learn Random Forest. Which action is most likely to reduce latency?

Accepted Answer

Reduce the number of trees in the ensemble. Reducing the number of trees in a Random Forest ensemble directly decreases the total number of decision paths that must be evaluated per inference request. Since each tree contributes additively to the prediction time, fewer trees means fewer sequential or parallel evaluations, which lowers the per-request latency at the cost of some model accuracy.

Answer

Prune decision trees in the model

Answer

Increase the number of instances behind the endpoint

Answer

Switch to a GPU instance type

Question 7

A company is using Amazon SageMaker to train a deep learning model for image segmentation. The training job uses a single ml.p3.2xlarge instance and takes 48 hours to complete. The team needs to reduce training time to under 12 hours to meet a deadline. The dataset is 50 GB of images stored in S3. The team currently uses File mode to download the data to the training instance. The model architecture is a convolutional neural network (CNN) with 50 layers. The team has access to multiple instances of the same type. Which approach will most effectively reduce training time?

Accepted Answer

Use SageMaker's distributed data parallelism with multiple instances.. SageMaker's distributed data parallelism splits the 50 GB dataset across multiple ml.p3.2xlarge instances, allowing each instance to process a subset of the data in parallel. This can reduce training time from 48 hours to under 12 hours, assuming near-linear scaling with the number of instances (e.g., 4 instances for a 4x speedup). The approach directly addresses the need to reduce wall-clock time without altering the model architecture or data loading method.

Answer

Reduce the number of layers in the CNN to speed up training.

Answer

Increase the batch size on the single instance to process more data per iteration.

Answer

Switch to Pipe mode to stream data from S3, reducing data loading time.

Question 8

A data scientist is building a regression model to predict house prices. The dataset includes features such as square footage, number of bedrooms, year built, and location. After training a linear regression model, the data scientist notices that the residuals have a clear pattern when plotted against predicted values: they increase with predicted values. The model also has high RMSE. Which action should the data scientist take to improve the model?

Accepted Answer

Apply a log transformation to the target variable.. A pattern in residuals indicates non-linearity, and transforming the target variable (e.g., log transformation) can stabilize variance and linearize relationships. Option A is wrong because removing outliers does not address the underlying non-linearity or heteroscedasticity; it may even discard useful data. Option B is wrong because L1 regularization helps reduce overfitting by penalizing large coefficients, but it does not fix non-constant variance or non-linearity. Option D is wrong because while interaction terms can model relationships between features, they do not directly address the pattern of increasing residuals (heteroscedasticity) and may not resolve the non-linearity in the target.

Answer

Remove outliers from the dataset.

Answer

Use L1 regularization (Lasso) to reduce overfitting.

Answer

Add interaction terms between features.

Question 9

A company is using Amazon Athena to query a data lake in S3. Queries are slow and expensive. The data is stored as JSON. Which action will improve query performance and reduce cost?

Accepted Answer

Convert the data to Parquet format. Converting JSON data to Parquet format significantly improves Athena query performance and reduces cost. Parquet is a columnar storage format that allows Athena to scan only the columns needed for a query, drastically reducing the amount of data read from S3. This minimizes I/O and compute costs, as Athena charges based on the amount of data scanned. In contrast, JSON is row-based and requires scanning entire files even for queries that only touch a few columns.

Answer

Compress the JSON files using gzip

Answer

Partition the data by date

Answer

Increase the number of Athena workers

Question 10

A company uses Amazon Redshift to run analytics on sales data. The data is loaded daily from S3 using COPY commands. The team notices that the COPY command performance degrades over time due to table bloat. The team needs to maintain query performance and reduce storage costs. Which combination of maintenance operations should the team perform regularly? (Choose THREE.)

Accepted Answer

Run the VACUUM command to reclaim space and re-sort rows.. The correct maintenance operations are VACUUM, DEEP COPY, and ANALYZE. VACUUM reclaims space from deleted or updated rows and re-sorts data if sort keys are defined, reducing bloat. DEEP COPY recreates the table to eliminate bloat completely by copying data to a new table and renaming. ANALYZE updates table statistics, which helps the query planner optimize query performance. Option A (UNLOAD) is wrong because it exports data to S3, not a maintenance operation. Option B (changing distribution style) is a schema change that affects data distribution, not a regular maintenance task for bloat removal.

Answer

Run the UNLOAD command to export data to S3 and then reload.

Answer

Change the distribution style of the table to KEY.

Question 11

A research team is training a deep learning model for object detection using SageMaker's built-in SSD algorithm. The dataset contains 50,000 images with bounding box annotations. The team uses a single ml.p3.2xlarge instance. After 24 hours of training, the model's loss has plateaued, but the mean average precision (mAP) on validation is only 0.45. The team wants to improve mAP without increasing training time. Which action should they take?

Accepted Answer

Use a pre-trained model as the backbone (e.g., ResNet-50 pre-trained on ImageNet). (use a pre-trained backbone) transfers learned features, often improving accuracy. Option A (increase learning rate) may destabilize training. Option C (increase batch size) may not improve mAP and could slow convergence. Option D (add more layers) increases training time.

Answer

Increase the learning rate by a factor of 2

Answer

Increase the batch size to 64

Answer

Add more convolutional layers to the backbone

Question 12

A company is using SageMaker to train a model with a large dataset that is stored in S3. The training job is taking a long time due to high I/O latency. The team has already converted the data to RecordIO format. What should they do next to reduce I/O latency?

Accepted Answer

Use Pipe mode to stream the RecordIO data. Pipe mode streams data directly from S3 to the training algorithm in a sequential manner, eliminating the need to download files to the local disk. Since the data is already in RecordIO format, Pipe mode can efficiently read the serialized records, significantly reducing I/O latency compared to File mode.

Answer

Use SageMaker fast file mode

Answer

Use multiple training instances

Answer

Use Amazon FSx for Lustre as the training data source

Answer

Shuffle the data before training

Question 13

A data scientist is exploring a dataset with 500 features and 100,000 observations for a regression problem. The scientist notices that many features are highly correlated with each other. Which technique should the scientist use to reduce multicollinearity and improve model interpretability during exploratory data analysis?

Accepted Answer

Calculate Variance Inflation Factor (VIF) for each feature and remove those with VIF > 10.. Variance Inflation Factor (VIF) is a measure of multicollinearity among features. Removing features with high VIF (e.g., > 10) reduces multicollinearity and retains interpretability. Option A is incorrect because mutual information measures dependency between feature and target, not multicollinearity. Option B is incorrect because PCA creates new features that are linear combinations, reducing interpretability. Option C is incorrect because Lasso regression is a modeling technique, not typically used during exploratory data analysis, and it may not remove all correlated features.

Answer

Compute mutual information between each feature and the target, and keep only the top 50 features.

Answer

Apply Principal Component Analysis (PCA) to reduce the feature space.

Answer

Use Lasso regression to select features with non-zero coefficients.

Question 14

A data science team needs to process streaming data from thousands of IoT devices and perform real-time anomaly detection. The data must be persisted in Amazon S3 for batch processing later. Which combination of AWS services should be used to meet these requirements?

Accepted Answer

Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for anomaly detection, and Amazon Kinesis Data Firehose to deliver data to Amazon S3.. Amazon Kinesis Data Streams provides durable, real-time ingestion for high-throughput IoT data. Kinesis Data Analytics can perform SQL-based anomaly detection on the stream, and Kinesis Data Firehose reliably delivers the processed or raw data to Amazon S3 for batch processing. This combination meets all requirements for streaming ingestion, real-time analytics, and persistent storage.

Answer

Amazon Kinesis Data Streams for ingestion, AWS Glue for anomaly detection, and Amazon S3 for storage.

Answer

AWS Lambda for both ingestion and anomaly detection, and Amazon S3 for storage.

Answer

Amazon Simple Queue Service (SQS) for ingestion, AWS Lambda for anomaly detection, and Amazon S3 for storage.

Question 15

A machine learning team is deploying a real-time inference endpoint for a fraud detection model using Amazon SageMaker. The model is a LightGBM classifier trained on 1 GB of tabular data. The endpoint must respond within 100 ms for 99% of requests, with a throughput of 10 requests per second. During load testing, the team observes that the 99th percentile latency is 250 ms and the endpoint CPU utilization is consistently above 90%. The team has already selected an ml.c5.xlarge instance with auto scaling enabled. Which combination of actions should the team take to meet the latency requirement? (Choose 3.)

Accepted Answer

Upgrade the instance type to ml.c5.2xlarge to increase CPU resources per instance.. (upgrading to ml.c5.2xlarge) provides more CPU resources per instance, reducing CPU utilization and thus latency. Option B (reducing the number of trees in the LightGBM model) decreases the computational complexity of inference, directly lowering inference time. Option C (enabling SageMaker's data compression for endpoint input payloads) reduces the data transfer size, which can lower I/O overhead and network latency. Option D (switching to SageMaker Batch Transform) is unsuitable because it is not designed for real-time inference and would not meet the low-latency requirement. Together, options A, B, and C address the latency issue by improving compute capacity, reducing model complexity, and minimizing data transfer time.

Answer

Switch to using SageMaker Batch Transform instead of a real-time endpoint.

Question 16

A machine learning team is deploying a model for real-time fraud detection. The model must make predictions with less than 100ms latency. The team uses SageMaker and the model is a large ensemble of decision trees. Which SageMaker hosting option is MOST suitable?

Accepted Answer

SageMaker Serverless Inference. SageMaker Serverless Inference is the most suitable option because it automatically scales to handle variable traffic and does not require managing underlying infrastructure. Although it may incur cold starts, for a constantly invoked fraud detection model the endpoint remains warm, achieving sub-100ms latency. The large ensemble of decision trees can be deployed as a single model on a Serverless endpoint, which is optimized for real-time inference with low latency and automatic scaling.

Answer

SageMaker Multi-model endpoint

Answer

SageMaker Elastic Inference

Answer

SageMaker Batch Transform

Question 17

A company stores customer transaction data in Amazon S3. A data scientist needs to perform exploratory data analysis using Amazon SageMaker. The dataset is 500 GB in CSV format. Which approach is most cost-effective and time-efficient for initial data profiling?

Accepted Answer

Use Amazon S3 Select to sample rows directly from S3. Amazon S3 Select can query a subset of rows directly from S3 without loading the entire dataset, enabling quick and cost-effective profiling. Option B is incorrect because loading the full 500 GB into a SageMaker notebook instance is expensive and time-consuming. Option C is incorrect because converting to Parquet format adds overhead that is unnecessary for initial profiling. Option D is incorrect because using AWS Glue ETL to transform the entire dataset before analysis is not cost-effective for initial data exploration.

Answer

Load the entire dataset into a SageMaker notebook instance and use pandas

Answer

Convert the data to Parquet format and then use Athena to query

Answer

Use AWS Glue ETL to transform the data and then analyze in Athena

Question 18

A machine learning engineer is analyzing a dataset with high cardinality categorical features. They want to reduce the number of categories by grouping rare categories into an 'Other' category. Which Amazon SageMaker processing job capability is best suited for this task?

Accepted Answer

Amazon SageMaker Processing. Amazon SageMaker Processing allows you to run custom data processing scripts (e.g., using pandas) that can handle grouping rare categories into 'Other' based on frequency thresholds. Option B (Data Wrangler) is a visual tool that may not offer the same level of customization for complex grouping logic. Option C (AWS Glue Studio) is a visual ETL tool but lacks tight integration with SageMaker and may be less efficient for this specific task. Option D (Autopilot) is designed for automated model building, not custom data processing.

Answer

Amazon SageMaker Data Wrangler

Answer

AWS Glue Studio

Answer

Amazon SageMaker Autopilot

Question 19

A data scientist receives the above error during model training. What is the most likely cause?

Accepted Answer

The training data contains missing or infinite values.. The error message indicates that the training data contains missing (NaN) or infinite values, which causes the loss function to become NaN. This is a common issue when data has not been properly cleaned. Option B is wrong because a high learning rate typically leads to divergence or instability, not NaN values due to data issues. Option C is wrong because an incorrect data format would result in a parsing error, not a NaN loss. Option D is wrong because insufficient memory leads to an out-of-memory error, not NaN values.

Answer

The learning rate is too high.

Answer

The data format is incorrect; expected CSV but received JSON.

Answer

The instance type lacks sufficient memory.

Question 20

A data scientist is analyzing a dataset with 500 features and 10,000 rows. The target variable is binary. After training a logistic regression model, the coefficients show many non-zero values but the model has low accuracy on the test set. Which EDA step should the data scientist perform next to improve model performance?

Accepted Answer

Use correlation analysis or mutual information to select the most relevant features.. With 500 features and low accuracy, the model likely suffers from overfitting due to irrelevant or redundant features. Correlation analysis or mutual information helps select the most relevant features, reducing noise and improving generalization. Option A (PCA) reduces dimensionality but creates uninterpretable components and may lose feature relationships, not directly addressing irrelevant features. Option B (collect more data) may help but does not solve the core issue of irrelevant features. Option C (normalization) only scales features, not reduce them, and logistic regression is not sensitive to scale if coefficients are interpreted carefully; overfitting is more likely due to too many features.

Answer

Apply Principal Component Analysis (PCA) to reduce dimensionality.

Answer

Collect more training data to improve generalization.

Answer

Normalize the features using StandardScaler.