MLA-C01 Question Bank · Latest 50 Questions Free Preview (Page 4/5)
Questions 31–40 of the latest 50 MLA-C01 questions (5 pages total), with answers and explanations, no login required. Practice all 233 questions online.
Question 31 · No. 214 · Single choice
A company wants to launch a new website feature that predicts home prices based on user-supplied home attributes. The attributes include location, square footage, and number of bedrooms and bathrooms. An ML engineer has trained a regression model by using the Amazon SageMaker AI XGBoost algorithm. The model performs well with training data. However, the model significantly underperforms when it is validated against real-world data. Which solution will improve the model’s validation score with the LEAST implementation effort?
- A. Create a larger training dataset that includes more real-world data. Retrain the model.
- B. Increase the value of the num_round hyperparameter.
- C. Change the eval_metric hyperparameter from Root Mean Square Error (RMSE) to Error.
- D. Increase the value of the lambda hyperparameter.
Answer: A
Question 32 · No. 215 · Multiple choice
An ML engineer is designing an AI-powered traffic management system to adjust traffic lights during predicted congestion. The system must use near real-time inference to generate predictions to help prevent traffic collisions. The system must use a batch processing pipeline to perform historical analysis of the predictions to continuously refine and improve the model. The historical analysis will take several hours to evaluate how well the predictions correlate with actual outcomes. The system must be able to scale inference endpoints appropriately to meet demand. Which combination of solutions will meet these requirements? (Choose two.)
- A. Use Amazon SageMaker real-time inference endpoints. Configure the endpoints to scale automatically based on a target tracking scaling policy that uses the metric ConcurrentInvocationsPerinstance.
- B. Configure reserved concurrency for AWS Lambda functions to process streaming data. Use Lambda SnapStart to connect the Lambda functions to Amazon SageMaker real-time endpoints to support near real-time traffic predictions.
- C. Configure an Amazon SageMaker Processing job for batch analysis of historical prediction data. Use Amazon EventBridge to schedule the job to run daily. Allow several hours for in-depth analysis to refine and improve the traffic management model.
- D. Use an Amazon EC2 Auto Scaling group to host containers to support the batch analysis of historical prediction data. Configure scaling based on Amazon CloudWatch metrics to analyze historical traffic patterns and model performance over multiple hours.
- E. Use an AWS Lambda function to perform the historical analysis. Use Amazon EventBridge to invoke the Lambda function.
Answer: A, C
Explanation: Option A is correct because Amazon SageMaker real-time inference endpoints are designed for near real-time inference, and using a target tracking scaling policy with the ConcurrentInvocationsPerInstance metric allows the endpoints to scale automatically based on traffic demand, meeting the inference and scalability requirements. Option C is correct because Amazon SageMaker Processing jobs are purpose-built for batch processing workloads and can run for extended periods (no time limit like AWS Lambda's 15-minute timeout), making them ideal for the historical analysis that takes several hours. Using Amazon EventBridge to schedule the job daily automates the batch pipeline. Option B is incorrect because AWS Lambda is not appropriate for near real-time inference requiring low latency, and reserved concurrency limits rather than enables scaling. Option D is incorrect because EC2 Auto Scaling groups are not the AWS-native solution for ML batch processing—SageMaker Processing is more suitable. Option E is incorrect because AWS Lambda functions have a maximum execution time of 15 minutes, which cannot accommodate historical analysis that takes several hours.
Question 33 · No. 216 · Single choice
A company is developing an ML model to forecast future values based on time series data. The dataset includes historical measurements collected at regular intervals and categorical features. The model needs to predict future values based on past patterns and trends. Which algorithm and hyperparameters should the company use to develop the model?
- A. Use the Amazon SageMaker AI XGBoost algorithm. Set the scale_pos_weight hyperparameter to adjust for class imbalance.
- B. Use k-means clustering with k to specify the number of clusters.
- C. Use the Amazon SageMaker AI DeepAR algorithm with matching context_length and prediction_length hyperparameters.
- D. Use the Amazon SageMaker AI Random Cut Forest (RCF) algorithm with contamination to set the expected proportion of anomalies.
Answer: C
Explanation: Option C is the correct answer because the Amazon SageMaker AI DeepAR algorithm is specifically designed for time series forecasting. It uses recurrent neural networks (RNNs) to predict future values based on past patterns and trends. The context_length hyperparameter defines how many past time steps the model should consider, while prediction_length defines how many future steps to predict. Matching these hyperparameters is a best practice for DeepAR. The other options are incorrect: A) XGBoost with scale_pos_weight is for classification with class imbalance, not time series forecasting; B) k-means is for clustering, not prediction; D) Random Cut Forest is for anomaly detection, not forecasting future values.
Question 34 · No. 217 · Single choice
A company develops a recommendation model and hosts the model on an Amazon SageMaker AI endpoint. The model uses the SageMaker AI endpoint to perform near real-time inference to deliver personalized product recommendations to customers based on browsing history, purchase records, and in-app user interactions. After a major marketing campaign, the company observes a sharp drop in the model’s performance. The company needs a solution to proactively monitor, detect, and validate model performance before future marketing campaigns. Which solution will meet these requirements?
- A. Use SageMaker Clarify to analyze changes in feature distribution. Configure SageMaker Model Monitor for near real-time input validation.
- B. Use Amazon CloudWatch dashboards to monitor endpoint metrics. Use SageMaker Model Monitor to track feature attribution.
- C. Use SageMaker Clarify for bias detection. Set up Amazon CloudWatch alarms to monitor model latency.
- D. Use SageMaker Model Monitor to monitor constraints. Use Amazon CloudWatch Logs Insights to analyze error patterns.
Answer: A
Explanation: Option A is the correct answer because it directly addresses the requirements of proactively monitoring, detecting, and validating model performance. SageMaker Clarify analyzes changes in feature distribution (data drift detection), which is crucial for identifying when input patterns shift due to marketing campaigns. SageMaker Model Monitor with near real-time input validation continuously monitors the inference data and can detect anomalies or drift in model inputs before they significantly impact performance. Option B focuses on feature attribution (explainability) rather than proactive validation. Option C deals with bias detection and latency, which don't address the performance drop issue. Option D uses Logs Insights for error pattern analysis, which is more reactive than proactive. Only Option A provides the combined approach of feature distribution analysis and near real-time input validation needed to meet the requirements.
Question 35 · No. 218 · Single choice
A company is developing ML models by using PyTorch and TensorFlow estimators with Amazon SageMaker AI. An ML engineer configures the SageMaker AI estimator and now needs to initiate a training job that uses a training dataset. Which SageMaker AI SDK method can initiate the training job?
- A. fit method
- B. create_model method
- C. deploy method
- D. predict method
Answer: A
Explanation: The fit method is the standard SageMaker AI SDK method used to initiate a training job. When working with PyTorch and TensorFlow estimators in SageMaker, calling estimator.fit() starts the training process using the specified training dataset. The other methods serve different purposes: create_model creates a SageMaker model artifact, deploy creates an endpoint for inference, and predict performs inference against a deployed endpoint.
Question 36 · No. 219 · Single choice
A company needs to perform feature engineering, aggregation, and data preparation. After the features are produced, the company must implement a solution on AWS to process and store the features. Which solution will meet these requirements?
- A. Use Amazon SageMaker Feature Processing to process and ingest the data. Use SageMaker Feature Store to manage and store the features.
- B. Use Amazon SageMaker Model Monitor to automatically ingest and transform the data. Create an Amazon S3 bucket to store the features in JSON format.
- C. Use Amazon Managed Service for Apache Flink to transform the data and to ingest the data directly into Amazon SageMaker Feature Store. Use Feature Store to manage and store the features.
- D. Use an Amazon SageMaker batch transform job to analyze, transform, and ingest the data. Create an Amazon DynamoDB table to store the features.
Answer: A
Explanation: Amazon SageMaker Processing is the appropriate service for feature engineering, data preprocessing, and aggregation tasks. Combined with Amazon SageMaker Feature Store, which is purpose-built to manage, store, and share machine learning features, this solution meets all the requirements. Option B is incorrect because SageMaker Model Monitor is for monitoring deployed models, not for feature engineering. Option C is not ideal because Amazon Managed Service for Apache Flink is primarily for real-time stream processing, not for batch feature engineering. Option D is incorrect because SageMaker batch transform jobs are for generating predictions, not for feature engineering, and DynamoDB is not designed as a feature store.
Question 37 · No. 220 · Single choice
An ML engineer wants to use Amazon SageMaker AI to prepare data for training. During exploratory data analysis, the ML engineer notices that several categorical features are missing values. How can the ML engineer use SageMaker AI to solve this problem?
- A. Use SageMaker Clarify to impute categorical features with the mean value.
- B. Use SageMaker Clarity to impute categorical features with the mode value.
- C. Use SageMaker Data Wrangler to impute categorical features with the mean value.
- D. Use SageMaker Data Wrangler to impute categorical features with the mode value.
Answer: D
Explanation: For categorical features with missing values, the appropriate imputation method is the mode (most frequent value), not the mean which is used for numerical data. SageMaker Data Wrangler is the correct tool for data preparation tasks including imputing missing values, while SageMaker Clarify is designed for bias detection and model explainability rather than data preparation.
Question 38 · No. 221 · Single choice
An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a re-training job if any data drift is detected. How should the ML engineer set up the pipeline to meet this requirement?
- A. Use an AWS Glue crawler and an AWS Glue extract, transform and load (ETL) job to detect data drift. Use AWS Glue triggers to automate the re-training job.
- B. Use Amazon Managed Service for Apache Flink to detect data drift. Use an AWS Lambda function to automate the re-training job.
- C. Use SageMaker Model Monitor to detect data drift. Use an AWS Lambda function to automate the re-training job.
- D. Use Amazon QuickSight anomaly detection to detect data drift. Use an AWS Step Functions workflow to automate the re-training job.
Answer: C
Explanation: SageMaker Model Monitor is the AWS service specifically designed to detect data drift, model quality drift, bias drift, and feature attribution drift in deployed ML models. It continuously monitors the model and can capture data statistics. When data drift is detected, it can trigger alarms and invoke AWS Lambda functions to automate downstream workflows such as re-training jobs. Options A (AWS Glue), B (Managed Apache Flink), and D (QuickSight) are not purpose-built for ML data drift detection—Glue is for ETL, Flink is for stream processing, and QuickSight is a BI visualization tool.
Question 39 · No. 222 · Single choice
A company is developing a new ML model to rank customers in order of their potential to pay back loans. The company needs to use an Amazon SageMaker AI built-in algorithm. Which algorithm should the company use to meet these requirements?
- A. XGBoost
- B. K-means clustering
- C. Principal component analysis (PCA)
- D. Neural Topic Model (NTM)
Answer: A
Explanation: XGBoost is a supervised gradient boosting algorithm that excels at ranking, classification, and regression tasks on tabular data. It is widely used for credit scoring and loan default prediction because it can handle feature importance, missing values, and non-linear relationships effectively. Amazon SageMaker provides XGBoost as a built-in algorithm specifically suited for this kind of ranking problem. K-means is unsupervised clustering, PCA is for dimensionality reduction, and NTM is for topic modeling on text — none of which fit the requirement of ranking customers by their potential to pay back loans.
Question 40 · No. 223 · Single choice
An ML engineer needs to build a processing pipeline to identify and remove personally identifiable information (PII) from petabytes of unstructured data. The ML engineer will use the processed data to train ML models in Amazon SageMaker AI. Which solution will meet these requirements?
- A. Use the Apache Spark-based serverless engine from AWS Glue interactive sessions. Use the Detect PII transform feature to identify and remove the PII data.
- B. Use AWS Glue Data Wrangler within Amazon SageMaker Canvas to detect and remove the PII.
- C. Use the Amazon SageMaker Clarify API to detect and mask the PII data.
- D. Use the DetectEntities API action in Amazon Comprehend to identify and remove the PII data.
Answer: A
Explanation: Option A is correct because AWS Glue interactive sessions provide a serverless Apache Spark-based engine that can scale to handle petabytes of data. The Detect PII transform feature in AWS Glue is specifically designed to identify and mask/remove PII data within Spark processing pipelines, which aligns perfectly with the requirement to build a processing pipeline for large-scale data. The processed data can then be easily used to train ML models in Amazon SageMaker. Option B is not ideal for petabyte-scale data as Glue Data Wrangler is more suited for smaller-scale data preparation. Option C is incorrect because SageMaker Clarify is designed for bias detection and model explainability, not PII removal. Option D is incorrect because it mentions the DetectEntities API (which is for named entity recognition like people, places, organizations) rather than the DetectPIIEntities API, and processing petabytes directly through Comprehend API calls would not be as scalable as a Spark-based solution.