Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 201:

  A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.4xlarge notebook instance.

  The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget.

  Which solution will result in the MOST cost savings?

  A. Change the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has. Stop the notebook when it is not in use. Run both data preprocessing and feature engineering development on that instance.

  B. Keep the notebook instance type and size the same. Stop the notebook when it is not in use. Run data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.

  C. Change the notebook instance type to a smaller general purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.

  D. Change the notebook instance type to a smaller general purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option.

  Correct Answer: C

• Question 202:

  A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.

  The data scientists are using Amazon Forecast to generate the forecasts.

  Which algorithm in Forecast should the data scientists use to meet these requirements?

  A. Autoregressive Integrated Moving Average (AIRMA)

  B. Exponential Smoothing (ETS)

  C. Convolutional Neural Network - Quantile Regression (CNN-QR)

  D. Prophet

  Correct Answer: C

  Reference: https://jesit.springeropen.com/articles/10.1186/s43067-020-00021-8

• Question 203:

  A company wants to use automatic speech recognition (ASR) to transcribe messages that are less than 60 seconds long from a voicemail-style application. The company requires the correct identification of 200 unique product names, some of which have unique spellings or pronunciations.

  The company has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts it can use to customize the chosen ASR model. The company needs to ensure that everyone can update their customizations multiple times each hour.

  Which approach will maximize transcription accuracy during the development phase?

  A. Use a voice-driven Amazon Lex bot to perform the ASR customization. Create customer slots within the bot that specifically identify each of the required product names. Use the Amazon Lex synonym mechanism to provide additional variations of each product name as mis-transcriptions are identified in development.

  B. Use Amazon Transcribe to perform the ASR customization. Analyze the word confidence scores in the transcript, and automatically create or update a custom vocabulary file with any word that has a confidence score below an acceptable threshold value. Use this updated custom vocabulary file in all future transcription tasks.

  C. Create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. Analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.

  D. Use the audio transcripts to create a training dataset and build an Amazon Transcribe custom language model. Analyze the transcripts and update the training dataset with a manually corrected version of transcripts where product names are not being transcribed correctly. Create an updated custom language model.

  Correct Answer: C

  https://aws.amazon.com/blogs/machine-learning/build-a-custom-vocabulary-to-enhance-speech-to-text-transcription-accuracy-with-amazon-transcribe/

• Question 204:

  A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price.

  Which techniques should the company use for feature selection? (Choose three.)

  A. Data scaling with standardization and normalization

  B. Correlation plot with heat maps

  C. Data binning

  D. Univariate selection

  E. Feature importance with a tree-based classifier

  F. Data augmentation

  Correct Answer: BDE

  Correlation plot with heat maps: This technique can be used to identify the relationship between each feature and the target variable (sales price). By creating a correlation plot with heat maps, the company can quickly visualize the strength and direction of the relationship between each feature and the target variable.

  Univariate selection: This technique can be used to select the features that have the strongest relationship with the target variable. It involves analyzing each feature independently and selecting the ones that have the highest correlation with the target variable.

  Feature importance with a tree-based classifier: This technique can be used to determine the most important features that contribute to the target variable. By using a tree-based classifier such as Random Forest or Gradient Boosting, the company can rank the importance of each feature and select the ones that have the highest importance.

• Question 205:

  A machine learning specialist needs to analyze comments on a news website with users across the globe. The specialist must find the most discussed topics in the comments that are in either English or Spanish.

  What steps could be used to accomplish this task? (Choose two.)

  A. Use an Amazon SageMaker BlazingText algorithm to find the topics independently from language. Proceed with the analysis.

  B. Use an Amazon SageMaker seq2seq algorithm to translate from Spanish to English, if necessary. Use a SageMaker Latent Dirichlet Allocation (LDA) algorithm to find the topics.

  C. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics.

  D. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Lex to extract topics form the content.

  E. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics.

  Correct Answer: BC

  Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/lda.html

• Question 206:

  A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured. Amazon SageMaker Model Monitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset. This dataset is statistically representative of the current production traffic. The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations.

  What should the ML specialist do to resolve the violations?

  A. Manually trigger the monitoring job to re-evaluate the SageMaker endpoint traffic sample.

  B. Run the Model Monitor baseline job again on the new training set. Configure Model Monitor to use the new baseline.

  C. Delete the endpoint and recreate it with the original configuration.

  D. Retrain the model again by using a combination of the original training set and the new training set.

  Correct Answer: B

  https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-create-baseline.html

• Question 207:

  A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.

  Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)

  A. Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.

  B. The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.

  C. Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.

  D. The sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.

  E. Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.

  Correct Answer: AC

  Reference: https://towardsdatascience.com/sales-forecasting-from-time-series-to-deep-learning-5d115514bfac https://arxiv.org/ftp/arxiv/papers/1302/1302.6613.pdf

• Question 208:

  An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.

  Which steps would improve the accuracy of the solution? (Choose three.)

  A. Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.

  B. Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.

  C. Augment the images in the dataset. Use open source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.

  D. Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.

  E. Use Amazon Rekognition Custom Labels to train a new model.

  F. Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.

  Correct Answer: CEF

  Reference: https://docs.aws.amazon.com/rekognition/latest/dg/how-it-works-types.html https://towardsdatascience.com/image-processing-techniques-for-computer-vision-11f92f511e21 https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/training-model.html

• Question 209:

  A data scientist is training a text classification model by using the Amazon SageMaker built-in BlazingText algorithm. There are 5 classes in the dataset, with 300 samples for category A, 292 samples for category B, 240 samples for category C, 258 samples for category D, and 310 samples for category E.

  The data scientist shuffles the data and splits off 10% for testing. After training the model, the data scientist generates confusion matrices for the training and test sets.

  

  

  What could the data scientist conclude form these results?

  A. Classes C and D are too similar.

  B. The dataset is too small for holdout cross-validation.

  C. The data distribution is skewed.

  D. The model is overfitting for classes B and E.

  Correct Answer: A

• Question 210:

  A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.

  Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

  A. Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.

  B. Drop the features with low correlation scores by using a Jupyter notebook.

  C. Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.

  D. Concatenate the features with high correlation scores by using a Jupyter notebook.

  Correct Answer: A

  Dimensions are too high. Use PCA