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NEW QUESTION 25
A Machine Learning Engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML Engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%.
What should the ML Engineer do to minimize bias due to missing values?
- A. Delete observations that contain missing values because these represent less than 5% of the data.
- B. Replace each missing value by the mean or median across non-missing values in the same column.
- C. Replace each missing value by the mean or median across non-missing values in same row.
- D. For each feature, approximate the missing values using supervised learning based on other features.
Answer: D
Explanation:
Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C.
Supervised learning applied to the imputation of missing values is an active field of research.
NEW QUESTION 26
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?
- A. Create word embedding vectors that store edit distance with every other word.
- B. Create one-hot word encoding vectors.
- C. Download word embeddings pre-trained on a large corpus.
- D. Produce a set of synonyms for every word using Amazon Mechanical Turk.
Answer: B
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-object2vec-adds-new- features-that-support-automatic-negative-sampling-and-speed-up-training/
NEW QUESTION 27
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. 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.
- 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. 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.
- 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.
Answer: B,D
Explanation:
Reference:
https://arxiv.org/ftp/arxiv/papers/1302/1302.6613.pdf
NEW QUESTION 28
A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.
Which services are integrated with Amazon SageMaker to track this information? (Choose two.)
- A. AWS CloudTrail
- B. AWS Config
- C. AWS Trusted Advisor
- D. AWS Health
- E. Amazon CloudWatch
Answer: A,E
Explanation:
https://aws.amazon.com/sagemaker/faqs/
NEW QUESTION 29
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?
- A. Clustering
- B. Linear regression
- C. Reinforcement learning
- D. Classification
Answer: B
NEW QUESTION 30
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