MLS-C01 Practice Test 50 — 15 Questions

Question 1

Which TWO of the following are appropriate techniques for detecting outliers in a univariate continuous feature?

Accepted Answer

Use Z-score and flag values with absolute Z-score > 3.. The Z-score method (Option B) is a standard statistical technique for detecting outliers in a univariate continuous feature. It measures how many standard deviations a data point is from the mean, and flagging values with an absolute Z-score greater than 3 is a common threshold because, under a normal distribution, approximately 99.7% of data falls within three standard deviations, making points beyond this likely outliers.

Answer

Apply a Random Forest classifier to predict outliers.

Answer

Remove any value that is more than one standard deviation from the mean.

Answer

Use DBSCAN clustering with default parameters.

Question 2

A data scientist is training a binary classification model using Amazon SageMaker's XGBoost. The dataset is highly imbalanced (99% negative class, 1% positive class). The data scientist wants to maximize the F1-score. Which parameter adjustment is most appropriate?

Accepted Answer

Set scale_pos_weight to 99. Setting scale_pos_weight to 99 is the most appropriate adjustment because it directly addresses class imbalance by assigning a higher weight to the minority (positive) class during training. In XGBoost, scale_pos_weight controls the balance of positive and negative weights, typically set as sum(negative instances) / sum(positive instances), which here is 99/1 = 99. This forces the model to penalize misclassifications of the positive class more heavily, thereby improving recall and F1-score.

Answer

Set max_depth to 10

Answer

Set eta to 0.01

Answer

Set subsample to 0.5

Question 3

A data scientist is trying to read a CSV file from S3 bucket 'my-bucket' with key 'training/data.csv' using an IAM role with the attached policy shown in the exhibit. The read operation fails with an Access Denied error. What is the most likely cause?

Accepted Answer

The policy does not include the s3:ListBucket permission, which is required to access the object.. The s3:GetObject permission alone is sufficient for direct object retrieval using the object's full key (e.g., via AWS CLI `aws s3api get-object`). However, many AWS services and tools (such as the S3 console, Amazon Athena, or AWS Glue) implicitly invoke a ListObjects API call to resolve the object path or display the bucket contents, which requires the s3:ListBucket permission. Without it, these operations fail with an Access Denied error even though GetObject is granted. In this scenario, the error likely occurs because the tool or service used to read the file performs a ListObjects call first.

Answer

The object is encrypted with SSE-KMS and the role does not have kms:Decrypt permission.

Answer

The resource ARN in the first statement should be 'arn:aws:s3:::my-bucket/training' without the wildcard.

Answer

The policy explicitly denies s3:GetObject because of the second statement with the trailing slash.

Question 4

A machine learning engineer is deploying a sentiment analysis model using Amazon SageMaker. The model is a BERT-based transformer that takes up to 512 tokens. The engineer notices that inference latency is high (over 500 ms per request) on a single ml.c5.xlarge instance. The application requires latency under 100 ms. The model has already been optimized using half-precision (FP16). Which action should the engineer take to reduce latency?

Accepted Answer

Use a GPU instance such as ml.g4dn.xlarge. (Use a GPU instance such as ml.g4dn.xlarge) provides the parallel processing power needed to accelerate transformer inference, significantly reducing latency for BERT-based models even after FP16 optimization. Option B (Reduce max sequence length to 128) would lower computational cost and latency but at the risk of truncating input and degrading accuracy, so it is not the primary recommended action. Option C (Increase batch size) improves throughput for multiple requests but does not reduce the latency of a single request. Option D (SageMaker Neo) optimizes the model for the target instance, but with an already optimized model (FP16) and strict latency requirement under 100 ms, switching to a GPU instance is more effective.

Answer

Reduce the maximum sequence length to 128

Answer

Increase the batch size for inference requests

Answer

Use SageMaker Neo to compile the model for the target instance

Question 5

A data scientist is analyzing a dataset with 1,000 features. They suspect many features are redundant and want to reduce dimensionality before training a model. Which technique is most appropriate for identifying the most important features?

Accepted Answer

Use L1 regularization (Lasso) to shrink coefficients to zero. L1 regularization (Lasso) is the most appropriate technique for identifying the most important features because it performs feature selection by shrinking the coefficients of less important features to zero, effectively selecting a subset of original features. This directly identifies which features are most relevant. PCA, while a dimensionality reduction technique, creates new components that are linear combinations of original features and does not identify the importance of original features. Random forest feature importance and correlation matrix methods can identify redundant features but are less direct for selecting the most important subset.

Answer

Apply principal component analysis (PCA) and select the top components

Answer

Train a random forest and remove features with low importance

Answer

Compute the correlation matrix and remove features with high correlation

Question 6

A financial services company needs to build a data lake on Amazon S3 that meets regulatory requirements for data retention and encryption. Data must be encrypted at rest and in transit, and access must be audited. The data lake will be queried by Amazon Athena and Amazon Redshift Spectrum. Which combination of actions should be taken?

Accepted Answer

Enable S3 default encryption with SSE-KMS and enable AWS CloudTrail for S3 data events.. S3 default encryption with SSE-KMS provides encryption at rest with customer-managed keys, and enabling AWS CloudTrail for S3 data events provides comprehensive auditing of access to the data lake. This combination meets the regulatory requirements for data retention, encryption, and audit. Option B is incorrect because SSL/TLS only ensures encryption in transit, and versioning does not provide encryption at rest or auditing. Option C is incorrect because IAM policies control access but do not provide encryption. Option D is incorrect because SSE-S3 does not allow key management control, which may be required, and S3 access logs are less detailed than CloudTrail for auditing.

Answer

Use IAM policies to control access and enable S3 server access logging.

Answer

Use SSL/TLS for all connections and enable S3 versioning.

Answer

Enable S3 default encryption with SSE-S3 and use S3 access logs.

Question 7

A data science team is deploying a machine learning model to production using SageMaker. The model is a PyTorch model that requires custom inference logic including image preprocessing. The team needs to ensure that the endpoint can handle variable batch sizes and has low latency. Which deployment approach should the team use?

Accepted Answer

Use SageMaker Inference Pipelines with a preprocessing container followed by the PyTorch model container.. SageMaker Inference Pipelines allow chaining of preprocessing and prediction containers, which enables custom inference logic like image preprocessing and supports variable batch sizes with low latency. Option B (AWS Lambda with API Gateway) is not designed for real-time inference with large models and variable batch sizes due to execution time and payload limits. Option C (SageMaker Python SDK's Predictor class) is a client interface to invoke an already deployed endpoint, not a deployment approach. Option D (SageMaker multi-model endpoint) hosts multiple models on a single endpoint but does not directly address custom inference logic or preprocessing. Option E (SageMaker Batch Transform) is for offline batch inference, not real-time low-latency inference.

Answer

Deploy the model as an AWS Lambda function and use API Gateway.

Answer

Use the SageMaker Python SDK's Predictor class with the model artifact.

Answer

Use a SageMaker multi-model endpoint to host the model with a custom container.

Answer

Use SageMaker Batch Transform for real-time inference.

Question 8

A data scientist is training a binary classifier using logistic regression on a dataset that is highly imbalanced (95% negative class, 5% positive class). The model achieves 95% accuracy but only predicts the negative class. Which metric should the scientist use to evaluate the model's performance on the positive class?

Accepted Answer

Recall. The model only predicts the negative class, so recall (true positives / (true positives + false negatives)) is 0, directly revealing the failure to detect positives. Precision is undefined (or 0/0) because there are no positive predictions. Accuracy is misleading (95% but useless). Therefore, recall is the most informative metric for evaluating performance on the positive class.

Answer

Precision

Answer

F1 Score

Answer

Accuracy

Question 9

A company is building a text classification model to categorize customer support tickets. The dataset is highly imbalanced with 95% of tickets belonging to 'General Inquiry' and 5% to 'Complaint'. The data scientist is using a random forest classifier. Which metric is most appropriate for evaluating model performance on the minority class?

Accepted Answer

F1-score for 'Complaint'. In a highly imbalanced dataset (95% General Inquiry, 5% Complaint), accuracy is misleading because a model that predicts 'General Inquiry' for every ticket would achieve 95% accuracy but completely fail on the minority class. The F1-score for 'Complaint' is the harmonic mean of precision and recall, providing a balanced evaluation of the model's ability to correctly identify complaints without being skewed by the majority class. For a random forest classifier, this metric directly addresses the minority class performance, which is the primary concern.

Answer

Accuracy

Answer

ROC AUC

Answer

Precision for 'Complaint'

Question 10

A data analyst is using Amazon Athena to query a partitioned dataset in S3. They notice that queries are scanning more data than expected. Which step should they take during exploratory data analysis to optimize query performance?

Accepted Answer

Check the partition metadata to ensure queries are pruning partitions.. Checking partition metadata (e.g., using SHOW PARTITIONS or querying information_schema) ensures that queries are applying partition pruning, which reduces the amount of data scanned. Option A is incorrect: converting to Parquet improves columnar scan efficiency but does not directly address partition misuse. Option B is incorrect: S3 Select filters data at the object level, but Athena already pushes down filters; this does not fix a lack of partition pruning. Option C is incorrect: increasing workers improves parallelism but does not reduce scanned data if partitions are not pruned.

Answer

Convert the data to Parquet format.

Answer

Use S3 Select to filter data before querying.

Answer

Increase the number of workers in Athena.

Question 11

Refer to the exhibit. A data scientist plans to read this CSV file into memory for exploratory data analysis using pandas. The instance has 8 GB of RAM. What is the MOST likely issue the scientist will encounter?

Accepted Answer

The file is too large to load into memory on this instance. The file size is approximately 1 GB (1073741824 bytes = 1 GB), and pandas typically requires 3-5x the file size in memory for CSV parsing, which would exceed the 8 GB RAM. Option A is wrong because pandas can handle 10 million rows; the issue is memory, not row count. Option C is wrong because the ContentType is text/csv, so it is indeed CSV format. Option D is wrong because there is no indication of permission issues (HTTP 200).

Answer

The file contains too many rows for pandas to handle

Answer

The file is not in CSV format despite the ContentType

Answer

The file is not accessible because of insufficient permissions

Question 12

An e-commerce company uses Amazon DynamoDB as the primary data store for user sessions. They want to run analytics on historical session data using Amazon Athena. What is the recommended approach to export DynamoDB data to S3 in a format optimized for Athena?

Accepted Answer

Use AWS Glue ETL to read from DynamoDB and write to S3 as Parquet. AWS Glue ETL can read from DynamoDB and write to S3 in Parquet format, which is optimized for Athena due to its columnar storage and compression. Option A (AWS Data Pipeline) can copy data to S3 as CSV, but CSV is less efficient for Athena and Data Pipeline is a legacy service. Option B (Amazon Kinesis Data Firehose) is designed for streaming data, not for exporting existing DynamoDB tables. Option C (DynamoDB Streams with Lambda) writes to S3 as JSON, which is less performant than Parquet for Athena queries and adds operational complexity.

Answer

Use AWS Data Pipeline to copy data to S3 as CSV

Answer

Use Amazon Kinesis Data Firehose to stream data from DynamoDB to S3

Answer

Use DynamoDB Streams with AWS Lambda to write to S3 as JSON

Question 13

A company uses Amazon SageMaker to deploy a model that predicts customer churn. The model is retrained weekly. The data scientist notices that the model's accuracy remains high, but the business reports that the model is not capturing new churn patterns. What is the most likely cause?

Accepted Answer

The model is experiencing concept drift. Concept drift occurs when the statistical properties of the target variable change over time, causing the model's predictions to become less relevant even if accuracy metrics remain high. In this scenario, the model is retrained weekly but still fails to capture new churn patterns because the underlying customer behavior has shifted—a classic sign of concept drift rather than a data or overfitting issue. Amazon SageMaker's built-in Model Monitor can detect such drift by comparing inference data distributions against a baseline.

Answer

The model is underfitting the data

Answer

The model has data leakage from future data

Answer

The model is overfitting to the training data

Question 14

A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). Which metric is most appropriate for evaluating model performance?

Accepted Answer

Area Under the ROC Curve (AUC-ROC). AUC-ROC is the most appropriate metric for imbalanced binary classification because it evaluates the model's ability to distinguish between positive and negative classes across all classification thresholds, without being biased by the 95% negative majority. It measures the trade-off between true positive rate and false positive rate, making it robust to class imbalance.

Answer

R-squared

Answer

Mean Squared Error (MSE)

Answer

Accuracy

Question 15

A data scientist needs to select a model training infrastructure that supports distributed training across multiple GPUs and provides automatic model parallelism. Which TWO AWS services should the scientist consider?

Accepted Answer

Amazon EMR. Amazon EMR is correct because it supports distributed training across multiple GPUs using frameworks like TensorFlow, PyTorch, and Apache Spark, and it can automatically handle model parallelism through its integration with Horovod or custom distributed training scripts. Amazon SageMaker is correct because it provides built-in distributed training libraries (e.g., SageMaker Distributed Data Parallel and Model Parallel) that automatically partition model layers across multiple GPUs, enabling efficient training of large models.

Answer

AWS Glue

Answer

AWS Lambda

Answer

Amazon Redshift