MLS-C01 practice questions 111–120

1. Question 111 · 368 in bank

   A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users. What should the Specialist do to meet this objective?

   • ABuild a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR
   • BBuild a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
   • CBuild a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR
   • DBuild a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR

   Show answer

   Answer: B

   Explanation

   Content-based filtering relies on similarities between features of items, whereas colloborative-based filtering relies on preferences from other users and how they respond to similar items.

   Source: Adapted from community discussion — kalyanvarma

   References

   • AWS documentation

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2. Question 112 · 368 in bank

   An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget. What should the Specialist do to meet these requirements?

   • ACreate one-hot word encoding vectors.
   • BProduce a set of synonyms for every word using Amazon Mechanical Turk.
   • CCreate word embedding vectors that store edit distance with every other word.
   • DDownload word embeddings pre-trained on a large corpus.

   Show answer

   Answer: D

   Explanation

   the solution is word embedding. As it is a interactive online dictionary, we need pre-trained word embedding thus the answer is D. In addition, there is no mention that the online dictonary is unique and does not have a pre-trained word embedding. Thus I strongly feel the answer is D

   Source: Adapted from community discussion — JayK

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3. Question 113 · 368 in bank

   A company is building a machine learning (ML) model to classify images of plants. An ML specialist has trained the model using the Amazon SageMaker built-in Image Classification algorithm. The model is hosted using a SageMaker endpoint on an ml.m5.xlarge instance for real-time inference. When used by researchers in the field, the inference has greater latency than is acceptable. The latency gets worse when multiple researchers perform inference at the same time on their devices. Using Amazon CloudWatch metrics, the ML specialist notices that the ModelLatency metric shows a high value and is responsible for most of the response latency. The ML specialist needs to fix the performance issue so that researchers can experience less latency when performing inference from their devices. Which action should the ML specialist take to meet this requirement?

   • AChange the endpoint instance to an ml.t3 burstable instance with the same vCPU number as the ml.m5.xlarge instance has.
   • BAttach an Amazon Elastic Inference ml.eia2.medium accelerator to the endpoint instance.
   • CEnable Amazon SageMaker Autopilot to automatically tune performance of the model.
   • DChange the endpoint instance to use a memory optimized ML instance.

   Show answer

   Answer: B

   Explanation

   The ModelLatency metric shows that the model inference time is causing the latency issue. Amazon Elastic Inference is designed to speed up the inference process of a machine learning model without needing to deploy the model on a more powerful instance. By attaching an Elastic Inference accelerator to the endpoint instance, the ML specialist can offload the compute-intensive parts of the inference process to the accelerator, resulting in faster inference times and lower latency.

   Source: Adapted from community discussion — AjoseO

   References

   • AWS documentation
   • AWS documentation

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4. Question 114 · 368 in bank

   An automotive company is using computer vision in its autonomous cars. The company has trained its models successfully by using transfer learning from a convolutional neural network (CNN). The models are trained with PyTorch through the use of the Amazon SageMaker SDK. The company wants to reduce the time that is required for performing inferences, given the low latency that is required for self-driving. Which solution should the company use to evaluate and improve the performance of the models?

   • AUse Amazon CloudWatch algorithm metrics for visibility into the SageMaker training weights, gradients, biases, and activation outputs. Compute the filter ranks based on this information. Apply pruning to remove the low-ranking filters. Set the new weights. Run a new training job with the pruned model.
   • BUse SageMaker Debugger for visibility into the training weights, gradients, biases, and activation outputs. Adjust the model hyperparameters, and look for lower inference times. Run a new training job.
   • CUse SageMaker Debugger for visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on this information. Apply pruning to remove the low-ranking filters. Set the new weights. Run a new training job with the pruned model.
   • DUse SageMaker Model Monitor for visibility into the ModelLatency metric and OverheadLatency metric of the model after the model is deployed. Adjust the model hyperparameters, and look for lower inference times. Run a new training job.

   Show answer

   Answer: C

   Explanation

   Using SageMaker Debugger, the company can monitor the training process and evaluate the performance of the model by computing filter ranks based on information like weights, gradients, biases, and activation outputs. After identifying the low-ranking filters, the company can apply pruning to remove them and set new weights. By doing so, the company can reduce the model size and improve the inference time. Finally, a new training job with the pruned model can be run to verify the performance improvements Not D because Model Monitor is a tool for monitoring the performance of deployed models, and it does not provide any direct feedback or insights into the model training process or ways to improve model inference time. Therefore, while Model Monitor can be useful for monitoring the performance of deployed models, it is not the best choice for evaluating and improving the performance of the models during the training phase, which is what the question is asking for.

   Source: Adapted from community discussion — AjoseO

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation
   • AWS documentation

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5. Question 115 · 368 in bank

   A company's machine learning (ML) specialist is designing a scalable data storage solution for Amazon SageMaker. The company has an existing TensorFlow-based model that uses a train.py script. The model relies on static training data that is currently stored in TFRecord format. What should the ML specialist do to provide the training data to SageMaker with the LEAST development overhead?

   • APut the TFRecord data into an Amazon S3 bucket. Use AWS Glue or AWS Lambda to reformat the data to protobuf format and store the data in a second S3 bucket. Point the SageMaker training invocation to the second S3 bucket.
   • BRewrite the train.py script to add a section that converts TFRecord data to protobuf format. Point the SageMaker training invocation to the local path of the data. Ingest the protobuf data instead of the TFRecord data.
   • CUse SageMaker script mode, and use train.py unchanged. Point the SageMaker training invocation to the local path of the data without reformatting the training data.
   • DUse SageMaker script mode, and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the SageMaker training invocation to the S3 bucket without reformatting the training data.

   Show answer

   Answer: D

   Explanation

   SageMaker script mode allows you to use your existing TensorFlow training scripts without any modifications. You can use the same TFRecord data format that your model expects, and point the SageMaker training invocation to the S3 bucket where the data is stored. SageMaker will automatically download the data to the local path of the training instance and pass it as an argument to your train.py script. You don’t need to reformat the data to protobuf format or rewrite your script to convert the data12.

   Source: Adapted from community discussion — Mickey321

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6. Question 116 · 368 in bank

   An ecommerce company wants to train a large image classification model with 10,000 classes. The company runs multiple model training iterations and needs to minimize operational overhead and cost. The company also needs to avoid loss of work and model retraining. Which solution will meet these requirements?

   • ACreate the training jobs as AWS Batch jobs that use Amazon EC2 Spot Instances in a managed compute environment.
   • BUse Amazon EC2 Spot Instances to run the training jobs. Use a Spot Instance interruption notice to save a snapshot of the model to Amazon S3 before an instance is terminated.
   • CUse AWS Lambda to run the training jobs. Save model weights to Amazon S3.
   • DUse managed spot training in Amazon SageMaker. Launch the training jobs with checkpointing enabled.

   Show answer

   Answer: D

   Explanation

   https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html Managed spot training can optimize the cost of training models up to 90% over on-demand instances. SageMaker manages the Spot interruptions on your behalf. "Spot instances can be interrupted, causing jobs to take longer to start or finish. You can configure your managed spot training job to use checkpoints. SageMaker copies checkpoint data from a local path to Amazon S3. When the job is restarted, SageMaker copies the data from Amazon S3 back into the local path. The training job can then resume from the last checkpoint instead of restarting."

   Source: Adapted from community discussion — Peeking

   References

   • AWS documentation

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7. Question 117 · 368 in bank

   A retail company uses a machine learning (ML) model for daily sales forecasting. The model has provided inaccurate results for the past 3 weeks. At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company's ML team determines that the inaccuracies are occurring because of a change in the value distributions of the model features. The ML team must implement a solution that will detect when this type of change occurs in the future. Which solution will meet these requirements with the LEAST amount of operational overhead?

   • AUse Amazon SageMaker Model Monitor to create a data quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.
   • BUse Amazon SageMaker Model Monitor to create a model quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.
   • CUse Amazon SageMaker Debugger to create rules to capture feature values Set up an Amazon CloudWatch alarm for the rules.
   • DUse Amazon CloudWatch to monitor Amazon SageMaker endpoints. Analyze logs in Amazon CloudWatch Logs to check for data drift.

   Show answer

   Answer: A

   Explanation

   A is correct. "If the statistical nature of the data that your model receives while in production drifts away from the nature of the baseline data it was trained on, the model begins to lose accuracy in its predictions. Amazon SageMaker Model Monitor uses rules to detect data drift and alerts you when it happens." https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-quality.html

   Source: Adapted from community discussion — hichemck

   References

   • AWS documentation
   • AWS documentation

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8. Question 118 · 368 in bank

   A machine learning (ML) specialist has prepared and used a custom container image with Amazon SageMaker to train an image classification model. The ML specialist is performing hyperparameter optimization (HPO) with this custom container image to produce a higher quality image classifier. The ML specialist needs to determine whether HPO with the SageMaker built-in image classification algorithm will produce a better model than the model produced by HPO with the custom container image. All ML experiments and HPO jobs must be invoked from scripts inside SageMaker Studio notebooks. How can the ML specialist meet these requirements in the LEAST amount of time?

   • APrepare a custom HPO script that runs multiple training jobs in SageMaker Studio in local mode to tune the model of the custom container image. Use the automatic model tuning capability of SageMaker with early stopping enabled to tune the model of the built-in image classification algorithm. Select the model with the best objective metric value.
   • BUse SageMaker Autopilot to tune the model of the custom container image. Use the automatic model tuning capability of SageMaker with early stopping enabled to tune the model of the built-in image classification algorithm. Compare the objective metric values of the resulting models of the SageMaker AutopilotAutoML job and the automatic model tuning job. Select the model with the best objective metric value.
   • CUse SageMaker Experiments to run and manage multiple training jobs and tune the model of the custom container image. Use the automatic model tuning capability of SageMaker to tune the model of the built-in image classification algorithm. Select the model with the best objective metric value.
   • DUse the automatic model tuning capability of SageMaker to tune the models of the custom container image and the built-in image classification algorithm at the same time. Select the model with the best objective metric value.

   Show answer

   Answer: C

   Explanation

   Option D: What can be easier than calling Hyperparameter Job for both custom container and built-in algorithm using the same command inside SageMaker Studio, probably nothing. So, let's look into proofs why this is possible. Remember, we have to find the possible solution which can be achieved in the LEAST amount of development time. 1. It's possible to Tune Multiple Algorithms with Hyperparameter Optimization to Find the Best Model. We only need to provide job settings that apply to all of the algorithms to be tested with list of training definitions. https://docs.aws.amazon.com/sagemaker/latest/dg/multiple-algorithm-hpo.html 2. You can use SageMaker AMT with built-in algorithms, custom algorithms, or SageMaker pre-built containers for machine learning frameworks. (Ctrl + F). https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html

   Source: Adapted from community discussion — injoho

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation
   • AWS documentation
   • AWS documentation

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9. Question 119 · 368 in bank

   Select 2

   A company wants to deliver digital car management services to its customers. The company plans to analyze data to predict the likelihood of users changing cars. The company has 10 TB of data that is stored in an Amazon Redshift cluster. The company's data engineering team is using Amazon SageMaker Studio for data analysis and model development. Only a subset of the data is relevant for developing the machine learning models. The data engineering team needs a secure and cost-effective way to export the data to a data repository in Amazon S3 for model development. Which solutions will meet these requirements? (Choose two.)

   • ALaunch multiple medium-sized instances in a distributed SageMaker Processing job. Use the prebuilt Docker images for Apache Spark to query and plot the relevant data and to export the relevant data from Amazon Redshift to Amazon S3.
   • BLaunch multiple medium-sized notebook instances with a PySpark kernel in distributed mode. Download the data from Amazon Redshift to the notebook cluster. Query and plot the relevant data. Export the relevant data from the notebook cluster to Amazon S3.
   • CUse AWS Secrets Manager to store the Amazon Redshift credentials. From a SageMaker Studio notebook, use the stored credentials to connect to Amazon Redshift with a Python adapter. Use the Python client to query the relevant data and to export the relevant data from Amazon Redshift to Amazon S3.
   • DUse AWS Secrets Manager to store the Amazon Redshift credentials. Launch a SageMaker extra-large notebook instance with block storage that is slightly larger than 10 TB. Use the stored credentials to connect to Amazon Redshift with a Python adapter. Download, query, and plot the relevant data. Export the relevant data from the local notebook drive to Amazon S3.
   • EUse SageMaker Data Wrangler to query and plot the relevant data and to export the relevant data from Amazon Redshift to Amazon S3.

   Show answer

   Answer: CE

   Explanation

   CE Option A: Launching multiple medium-sized instances in a distributed SageMaker Processing job and using the prebuilt Docker images for Apache Spark to query and plot the relevant data is a possible solution, but it may not be the most cost-effective solution as it requires spinning up multiple instances. Option B: Launching multiple medium-sized notebook instances with a PySpark kernel in distributed mode is another solution, but it may not be the most secure solution as the data would be stored on the instances and not in a centralized data repository. Option D: Using AWS Secrets Manager to store the Amazon Redshift credentials and launching a SageMaker extra-large notebook instance is a solution, but the block storage requirement that is slightly larger than 10 TB could be costly and may not be necessary.

   Source: Adapted from community discussion — solution123

   References

   • AWS documentation
   • AWS documentation

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10. Question 120 · 368 in bank

    A company is building an application that can predict spam email messages based on email text. The company can generate a few thousand human-labeled datasets that contain a list of email messages and a label of "spam" or "not spam" for each email message. A machine learning (ML) specialist wants to use transfer learning with a Bidirectional Encoder Representations from Transformers (BERT) model that is trained on English Wikipedia text data. What should the ML specialist do to initialize the model to fine-tune the model with the custom data?

    • AInitialize the model with pretrained weights in all layers except the last fully connected layer.
    • BInitialize the model with pretrained weights in all layers. Stack a classifier on top of the first output position. Train the classifier with the labeled data.
    • CInitialize the model with random weights in all layers. Replace the last fully connected layer with a classifier. Train the classifier with the labeled data.
    • DInitialize the model with pretrained weights in all layers. Replace the last fully connected layer with a classifier. Train the classifier with the labeled data.

    Show answer

    Answer: D

    Explanation

    D is the right option since initializing the model with pretrained weights, the model can leverage the knowledge learned from a large corpus of text data, such as English Wikipedia text data, to improve its performance on a specific task, such as spam email classification . And Replacing the last fully connected layer with a classifier is necessary because the last layer of BERT is designed for predicting masked words in a sentence, which is different from the task of spam email classification

    Source: Adapted from community discussion — teka112233

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MLS-C01 practice, question 111. 368 questions available.