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A retail company uses a machine learning (ML) model for daily sales forecasting. The company’s brand manager reports that 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 is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies.

What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?

A.

Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.

B.

Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.

C.

Create a line chart with the weekly mean absolute error (MAE) of the model.

D.

Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.

A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.

Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.

How should the Data Scientist correct this issue?

A.

Drop all records from the dataset where age has been set to 0.

B.

Replace the age field value for records with a value of 0 with the mean or median value from the dataset.

C.

Drop the age feature from the dataset and train the model using the rest of the features.

D.

Use k-means clustering to handle missing features.

A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.

Which architecture changes would ensure that provisioned resources are being utilized effectively?

A.

Redeploy the model as a batch transform job on an M5 instance.

B.

Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.

C.

Redeploy the model on a P3dn instance.

D.

Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

A finance company has collected stock return data for 5.000 publicly traded companies. A financial analyst has a dataset that contains 2.000 attributes for each company. The financial analyst wants to use Amazon SageMaker to identify the top 15 attributes that are most valuable to predict future stock returns.

Which solution will meet these requirements with the LEAST operational overhead?

A.

Use the linear learner algorithm in SageMaker to train a linear regression model to predict the stock returns. Identify the most predictive features by ranking absolute coefficient values.

B.

Use random forest regression in SageMaker to train a model to predict the stock returns. Identify the most predictive features based on Gini importance scores.

C.

Use an Amazon SageMaker Data Wrangler quick model visualization to predict the stock returns. Identify the most predictive features based on the quick model's feature importance scores.

D.

Use Amazon SageMaker Autopilot to build a regression model to predict the stock returns. Identify the most predictive features based on an Amazon SageMaker Clarify report.

A Machine Learning Specialist needs to move and transform data in preparation for training Some of the data needs to be processed in near-real time and other data can be moved hourly There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data

Which of the following services can feed data to the MapReduce jobs? (Select TWO )

A.

AWSDMS

B.

Amazon Kinesis

C.

AWS Data Pipeline

D.

Amazon Athena

E.

Amazon ES

A manufacturing company needs to identify returned smartphones that have been damaged by moisture. The company has an automated process that produces 2.000 diagnostic values for each phone. The database contains more than five million phone evaluations. The evaluation process is consistent, and there are no missing values in the data. A machine learning (ML) specialist has trained an Amazon SageMaker linear learner ML model to classify phones as moisture damaged or not moisture damaged by using all available features. The model's F1 score is 0.6.

What changes in model training would MOST likely improve the model's F1 score? (Select TWO.)

A.

Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the SageMaker principal component analysis (PCA) algorithm.

B.

Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the scikit-iearn multi-dimensional scaling (MDS) algorithm.

C.

Continue to use the SageMaker linear learner algorithm. Set the predictor type to regressor.

D.

Use the SageMaker k-means algorithm with k of less than 1.000 to train the model

E.

Use the SageMaker k-nearest neighbors (k-NN) algorithm. Set a dimension reduction target of less than 1,000 to train the model.

A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy

sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as

either a potential risk or no risk. The model is not performing well, even though the Data Scientist has

experimented with many different network structures and tuned the corresponding hyperparameters.

Which approach will provide the MAXIMUM performance boost?

A.

Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a largecollection of news articles related to the energy sector.

B.

Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation lossstops decreasing.

C.

Reduce the learning rate and run the training process until the training loss stops decreasing.

D.

Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to theenergy sector.

A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company's stores. The company provided the ML specialist with sales data for this store from the past 10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately 10% of the days in the historical dataset are missing sales data.

The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires.

Which action will MOST likely improve the performance for the forecasting model?

A.

Aggregate sales from stores in the same geographic area.

B.

Apply smoothing to correct for seasonal variation.

C.

Change the forecast frequency from daily to weekly.

D.

Replace missing values in the dataset by using linear interpolation.

A machine learning (ML) specialist needs to extract embedding vectors from a text series. The goal is to provide a ready-to-ingest feature space for a data scientist to develop downstream ML predictive models. The text consists of curated sentences in English. Many sentences use similar words but in different contexts. There are questions and answers among the sentences, and the embedding space must differentiate between them.

Which options can produce the required embedding vectors that capture word context and sequential QA information? (Choose two.)

A.

Amazon SageMaker seq2seq algorithm

B.

Amazon SageMaker BlazingText algorithm in Skip-gram mode

C.

Amazon SageMaker Object2Vec algorithm

D.

Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode

E.

Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN)

A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may be fraudulent

How should the Specialist frame this business problem'?

A.

Streaming classification

B.

Binary classification

C.

Multi-category classification

D.

Regression classification