MLA-C01 practice questions 191–200

1. Question 191 · 226 in bank

   A retail company is creating an AI-powered assistant for customers. The company has a large body of documentation that the assistant needs to use for general inquiries. The company wants any responses about prices to use only documentation that is less than 1 month old. Which solution will meet these requirements?

   • AUse Amazon Q Business to develop the responses. Configure a document attribute filter so that responses about prices use only the documents from the past month.
   • BUse Amazon Q Business to develop the responses. Configure the source attribution citation so that responses about prices use only the documents from the past month.
   • CSegment the documents into folders based on the month of document creation. Configure Amazon Q Developer to use only the documents from the past month to develop responses about prices.
   • DSegment the documents into folders based on the month of document creation. Grant the assistant access to only the documents from the past month for responses about prices. Use Amazon Q Developer to develop the responses.

   Show answer

   Answer: A

   Next question

2. Question 192 · 226 in bank

   A travel company wants to create an ML model to recommend the next airport destination for its users. The company has collected millions of data records about user location, recent search history on the company’s website, and 2,000 available airports. The data has several categorical features with a target column that is expected to have a high-dimensional sparse matrix. The company needs to use Amazon SageMaker AI built-in algorithms for the model. An ML engineer converts the categorical features by using one-hot encoding. Which algorithm should the ML engineer implement to meet these requirements?

   • AUse the CatBoost algorithm to recommend the next airport destination.
   • BUse the DeepAR forecasting algorithm to recommend the next airport destination.
   • CUse the Factorization Machines algorithm to recommend the next airport destination.
   • DUse the k-means algorithm to cluster users into groups. Map each group to the next airport destination based on user search history.

   Show answer

   Answer: C

   Next question

3. Question 193 · 226 in bank

   An ML engineer is configuring auto scaling for an inference component of a model that runs behind an Amazon SageMaker AI endpoint. The ML engineer configures SageMaker AI auto scaling with a target tracking scaling policy set to 100 invocations per model per minute. The SageMaker AI endpoint scales appropriately during normal business hours. However, the ML engineer notices that at the start of each business day, there are zero instances available to handle requests, which causes delays in processing. The ML engineer must ensure that the SageMaker AI endpoint can handle incoming requests at the start of each business day. Which solution will meet this requirement?

   • AReduce the SageMaker AI auto scaling cooldown period to the minimum supported value. Add an auto scaling lifecycle hook to scale the SageMaker AI instances.
   • BChange the target metric to CPU utilization.
   • CModify the scaling policy target value to one.
   • DApply a step scaling policy that scales based on an Amazon CloudWatch alarm. Apply a second CloudWatch alarm and scaling policy to scale the minimum number of instances from zero to one at the start of each business day.

   Show answer

   Answer: D

   Next question

4. Question 194 · 226 in bank

   An ML engineer is using an Amazon SageMaker Studio notebook to train a neural network by creating an estimator. The estimator runs a Python training script that uses Distributed Data Parallel (DDP) on a single instance that has more than one GPU. The ML engineer discovers that the training script is underutilizing GPU resources. The ML engineer must identify the point in the training script where resource utilization can be optimized. Which solution will meet this requirement?

   • AUse Amazon CloudWatch metrics to create a report that describes GPU utilization over time.
   • BAdd SageMaker Profiler annotations to the training script. Run the script and generate a report from the results.
   • CUse AWS CloudTrail to create a report that describes GPU utilization and GPU memory utilization over time.
   • DCreate a default monitor in Amazon SageMaker Model Monitor and suggest a baseline. Generate a report based on the constraints and statistics the monitor generates.

   Show answer

   Answer: B

   Next question

5. Question 195 · 226 in bank

   An ML engineer at an insurance company trains a regression model to predict the number of insurance policy sales each month. After training the model, the ML engineer uses Amazon SageMaker AI to deploy the model for inference. The ML engineer wants to monitor the model predictions to detect whether the production data distribution differs from the training data distribution when there are changes in customer behaviors. Which solution will meet these requirements?

   • ADetermine whether there is drift in the data quality.
   • BDetermine whether there is drift in the model quality.
   • CDetermine whether there is drift in the model bias.
   • DDetermine whether there is drift in the feature attribution.

   Show answer

   Answer: A

   Next question

6. Question 196 · 226 in bank

   A company is developing an internal cost-estimation tool that uses an ML model in Amazon SageMaker AI. Users upload high-resolution images to the tool. The model must process each image and predict the cost of the object in the image. The model also must notify the user when processing is complete. Which solution will meet these requirements?

   • AStore the images in an Amazon S3 bucket. Deploy the model on SageMaker AI. Use batch transform jobs for model inference. Use an Amazon Simple Queue Service (Amazon SQS) queue to notify users.
   • BStore the images in an Amazon S3 bucket. Deploy the model on SageMaker AI. Use an asynchronous inference strategy for model inference. Use an Amazon Simple Notification Service (Amazon SNS) topic to notify users.
   • CStore the images in an Amazon Elastic File System (Amazon EFS) file system. Deploy the model on SageMaker AI. Use batch transform jobs for model inference. Use an Amazon Simple Queue Service (Amazon SQS) queue to notify users.
   • DStore the images in an Amazon Elastic File System (Amazon EFS) file system. Deploy the model on SageMaker AI. Use an asynchronous inference strategy for model inference. Use an Amazon Simple Notification Service (Amazon SNS) topic to notify users.

   Show answer

   Answer: B

   Next question

7. Question 197 · 226 in bank

   A healthcare company uses an Amazon SageMaker AI endpoint to host a model that predicts patient readmission risk to hospitals. The company wants to predict patient readmissions with high accuracy and is willing to tolerate false positives. The current model performance has degraded over the previous year. The company trains and deploys a new model as a shadow variant for testing on live traffic from hospitals. The company monitors the performance of the new model for a month. During the month of testing, the shadow variant has a higher recall than the existing model but has a lower precision. What should the company do next?

   • APromote the shadow variant to full production.
   • BExtend the shadow testing period to capture more data. Monitor the new model to determine whether precision improves.
   • CUse a blue/green deployment strategy to allocate a small percentage of traffic to the shadow variant to reduce model errors.
   • DDisable the shadow variant and roll back to the main variant.

   Show answer

   Answer: A

   Next question

8. Question 198 · 226 in bank

   An airline company uses an ML model to adjust ticket prices based on demand. The model runs on Amazon SageMaker real-time endpoints. During previous deployments, the model failed to scale quickly enough when website traffic increased, which caused delays in price adjustments. An ML engineer needs to configure auto scaling for the SageMaker endpoints to respond rapidly to traffic changes. The solution must use target tracking scaling policies. Which configuration will be MOST responsive to sudden changes in traffic?

   • AConfigure auto scaling based on the SageMaker AI InvocationsPerInstance standard metric. Configure 10-second interval resolution, and set the default 300-second scale-in cooldown period.
   • BConfigure auto scaling based on the SageMaker AI InvocationsPerInstance metric. Configure high-resolution 10-second intervals, and set a 600-second scale-in cooldown period.
   • CConfigure auto scaling based on the SageMaker InvocationsPerInstance standard metric. Configure 10-second intervals resolution, and set a 600-second scale-in cooldown period.
   • DConfigure auto scaling based on the SageMaker InvocationsPerInstance metric. Configure high-resolution 10-second intervals, and set the default 300-second scale-in cooldown period.

   Show answer

   Answer: D

   Next question

9. Question 199 · 226 in bank

   An ML engineer is collecting data to train a classification ML model by using Amazon SageMaker AI. The target column can have two possible values: Class A or Class B. The ML engineer wants to ensure that the number of samples for both Class A and Class B are balanced, without losing any existing training data. The ML engineer must test the balance of the training data. Which solution will meet this requirement?

   • AUse SageMaker Clarify to check for class imbalance (CI). If the value is equal to 0, then use random undersampling in SageMaker Data Wrangler to balance the classes.
   • BUse SageMaker Clarify to check for class imbalance (CI). If the value is greater than 0, then use synthetic minority oversampling technique (SMOTE) in SageMaker Data Wrangler to balance the classes.
   • CUse SageMaker JumpStart to generate a class imbalance (CI) report. If the value is greater than 0, then use random undersampling in SageMaker Studio to balance the classes.
   • DUse SageMaker JumpStart to generate a class imbalance (CI) report. If the value is equal to 0, then use synthetic minority oversampling technique (SMOTE) in SageMaker Studio to balance the classes.

   Show answer

   Answer: B

   Next question

10. Question 200 · 226 in bank

    An ML engineer is building a logistic regression model to predict customer churn for subscription services. The ML engineer is using a dataset that contains two string variables: location and job_seniority_level. The location variable has 3 distinct values, and the job_seniority_level variable has over 10 distinct values. The ML engineer must perform preprocessing on the variables. Which solution will meet this requirement?

    • AApply tokenization to location. Apply ordinal encoding to job_seniority_level.
    • BApply one-hot encoding to location. Apply ordinal encoding to job_seniority_level
    • CApply binning to location. Apply standard scaling to job_seniority_level.
    • DApply one-hot encoding to location. Apply standard scaling to job_seniority_level.

    Show answer

    Answer: B

    Next question

Keyboard:J Kmove between questionsA–Dselect an answerRshow or hide the answer← →change page

MLA-C01 practice, question 191. 226 questions available.