A manufacturing company has a large set of labeled historical sales data The manufacturer would like to predict how many units of a particular part should be produced each quarter Which machine learning approach should be used to solve this problem?

• A.Random Cut Forest (RCF)
• B.Principal component analysis (PCA)
• C.Linear regression
• D.Logistic regression

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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 line chart with the weekly mean absolute error (MAE) of the model.
• C.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.
• 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.

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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)

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When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

• A.The training channel identifying the location of training data on an Amazon S3 bucket.
• B.The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
• C.Hyperparameters in a JSON array as documented for the algorithm used.
• D.The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
• E.The output path specifying where on an Amazon S3 bucket the trained model will persist.
• F.The validation channel identifying the location of validation data on an Amazon S3 bucket.

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A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city As this is a prototype, only daily data from the last year is available Which model is MOST likely to provide the best results in Amazon SageMaker?

• A.Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
• B.Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
• C.Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.
• D.Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.

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