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New Question A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes. The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes. Which function will produce the desired output? A. Dropout B. Smooth L1 loss C. Softmax D. Rectified linear units (ReLU) Answer: D New Question A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target. What option can the Specialist use to determine whether it is overestimating or underestimating the target value? A. Root Mean Square Error (RMSE) B. Residual plots C. Area under the curve D. Confusion matrix Answer: C New Question A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided. Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class? A. Decision tree B. Linear support vector machine (SVM) C. Naive Bayesian classifier D. Single Perceptron with sigmoidal activation function Answer: C New Question A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric. This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours. With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s). Which visualization will accomplish this? A. A histogram showing whether the most important input feature is Gaussian. B. A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension. C. A scatter plot showing the performance of the objective metric over each training iteration. D. A scatter plot showing the correlation between maximum tree depth and the objective metric. Answer: B New Question A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences. The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions. Here is an example from the dataset: "The quck BROWN FOX jumps over the lazy dog." Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Choose three.) A. Perform part-of-speech tagging and keep the action verb and the nouns only. B. Normalize all words by making the sentence lowercase. C. Remove stop words using an English stopword dictionary. D. Correct the typography on "quck" to "quick." E. 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Answer: A New Question A Data Engineer needs to build a model using a dataset containing customer credit card information How can the Data Engineer ensure the data remains encrypted and the credit card information is secure? A. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers. B. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers. C. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers. D. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue. 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Answer: C New Question A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant. Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test? A. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced. B. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker. C. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker. D. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data Answer: B New Question A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the LEAST effort to be able to query this data? A. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries. B. Use AWS Glue to catalogue the data and Amazon Athena to run queries. C. Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries. D. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries. Answer: B New Question A Machine Learning Specialist is developing a custom video recommendation model for an application. The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket. The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance. Which approach allows the Specialist to use all the data to train the model? A. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode. B. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset C. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode. D. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset. Answer: A New Question A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a catalog of the metadata concerning the datasets. The solution should require the least amount of setup and maintenance. Which solution will allow the company to achieve its goals? A. Create an Amazon EMR cluster with Apache Hive installed. Then, create a Hive metastore and a script to run transformation jobs on a schedule. B. Create an AWS Glue crawler to populate the AWS Glue Data Catalog. Then, author an AWS Glue ETL job, and set up a schedule for data transformation jobs. C. Create an Amazon EMR cluster with Apache Spark installed. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule. D. Create an AWS Data Pipeline that transforms the data. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule. Answer: B Explanation: AWS Glue is the correct answer because this option requires the least amount of setup and maintenance since it is serverless, and it does not require management of the infrastructure. A, C, and D are all solutions that can solve the problem, but require more steps for configuration, and require higher operational overhead to run and maintain. New Question A Data Scientist is working on optimizing a model during the training process by varying multiple parameters. 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๋ณต์„ ๋ถ€๋ฅด๋Š” 7๊ฐ€์ง€ ๊ธ์ •์ ์ธ ์ƒ๊ฐ๋ฒ•
1. ๋จผ์ € ์ˆจ์„ ์™„์ „ํžˆ ๋‚ด์‰ฐ ๋‹ค์Œ ์ฒœ์ฒœํžˆ ํฌ๊ฒŒ ๋“ค์ด์‰ฐ๋‹ค. ์ด๋•Œ ์„ธ์ƒ์˜ ์—ฌ๋Ÿฌ๊ฐ€์ง€ ์ข‹์€ ๊ฒƒ๋“ค์„ ๋“ค์ดํ‚จ๋‹ค๋Š” ๊ธฐ๋ถ„์œผ๋กœ '์•„,์ข‹๋‹ค' ๋ผ๊ณ  ๋งˆ์Œ์œผ๋กœ๋ถ€ํ„ฐ ์ƒ๊ฐํ•˜๋ฉด์„œ ์ˆจ์„ ๋“ค์ด์‰ฐ๋‹ค. 2. ๊ทธ๋ฆฌ๊ณ  ์ฒœ์ฒœํžˆ ์ˆจ์„ ๋‚ด์‰ฌ๋ฉด์„œ ์ข‹์€ ๊ฒƒ๋“ค์„ ๋“ค์ด์ผฐ์œผ๋ฏ€๋กœ ๋‹ต๋ก€ํ•˜๋Š” ๊ฐ์‚ฌ์˜ ๊ธฐ๋ถ„์œผ๋กœ '๊ณ ๋ง™๋‹ค'๊ณ  ์ƒ๊ฐํ•œ๋‹ค. 3. ๋งˆ์Œ์†์œผ๋กœ ๋‹ค์‹œ '์ข‹๋‹ค' '๊ณ ๋ง™๋‹ค'๋Š” ์ƒ๊ฐ์„ ํ•œ๋‹ค. ๋ˆ„๊ตฌ์—๊ฒŒ ๊ณ ๋งˆ์šด์ง€, ๋ฌด์—‡์ด ์ข‹์€์ง€๋Š” ์ƒ๊ฐํ•˜์ง€ ์•Š์•„๋„ ๋œ๋‹ค. 4. ๊ทธ๋Ÿฌ๋ฉด ์˜จ๋ชฌ์˜ ์„ธํฌ๊ฐ€ ์ฆ๊ฑฐ์›Œํ•˜๋ฉฐ ๊ทธ ๋ฐ˜์‘์œผ๋กœ ์†์ด ๋”ฐ๋œปํ•ด์ง€๋Š” ๊ฒƒ์„ ๋Š๋‚„ ๊ฒƒ์ด๋‹ค. ๋˜ ๊ณ ๋ง™๋‹ค๊ณ  ์ƒ๊ฐํ•˜๋ฉด ์˜จ๋ชธ์ด ๋”ฐ๋œปํ•ด์งˆ ๊ฒƒ์ด๋‹ค. 5. ๊ทธ๋Ÿฐ ์ž๊ธฐ ๋ชธ์˜ ๋ณ€ํ™”๋ฅผ ์˜์‹ํ•˜๋ฉด์„œ '์ข‹๋‹ค' '๊ณ ๋ง™๋‹ค' ๋ฅผ ๋ฐ˜๋ณตํ•œ๋‹ค. ์žฌ๋ฏธ์žˆ๋Š” ๊ฒƒ์€ '์ข‹๋‹ค'๋ผ๊ณ  ์ƒ๊ฐํ•˜๋ฉด ์ •๋ง ์•ž์œผ๋กœ ์ข‹์€ ์ผ๋“ค์ด ๋งŽ์ด ์ƒ๊ธด๋‹ค๋Š” ์‚ฌ์‹ค์ด๋‹ค. ์‹ ์ด ์•„๋‹ˆ๋ผ ์ž์‹ ์˜ ํž˜์œผ๋กœ ์ข‹์€ ์ผ๋“ค์„ ๋งŒ๋“ค์–ด๋‚ด๊ณ  ๋ฐœ๊ฒฌ ํ•˜๋Š” ๋Šฅ๋ ฅ์— ๋ˆˆ๋œจ๊ฒŒ ๋œ๋‹ค. 6. '์ข‹๋‹ค' '๊ณ ๋ง™๋‹ค'๋Š” ๊ฐ„๋‹จํ•œ ๋น„๋ฒ•์ด์ง€๋งŒ, ์ •๋ง ํ–‰๋ณตํ•ด์ง„๋‹ค. ํ˜น์‹œ ์‹ค์ˆ˜๋กœ ๋„˜์–ด์ ธ ๋ฌด๋ฆŽ์ด ๋ฒ—๊ฒจ์ง€๊ณ  ํ”ผ๊ฐ€ ๋‚˜๋ฉด ๋ณดํ†ต์€ '๋ˆ„๊ฐ€ ์—ฌ๊ธฐ์— ์ด๋Ÿฐ ๊ฒƒ์„ ๋†“์•„๋‘” ๊ฑฐ์•ผ!' ํ•˜๊ณ  ํ™”๋ฅผ ๋‚ด๊ฒ ์ง€๋งŒ, ์ด ์—ฐ์Šต์„ ํ•˜๊ณ  ๋‚˜๋ฉด ๋‹ฌ๋ผ์ง„๋‹ค. ๋„˜์–ด์ ธ์„œ ์•„ํ”„๊ธฐ๋Š” ํ•˜์ง€๋งŒ '์•„, ๋‹คํ–‰์ด๋‹ค. ๊ณจ์ ˆ๋˜์ง€๋Š” ์•Š์•˜์œผ๋‹ˆ๊นŒ.' ํ•˜๊ณ  ์ƒ๊ฐํ•˜๊ฒŒ ๋  ๊ฒƒ์ด๋‹ค. 7. ์ข‹๋‹ค๊ณ  ์ƒ๊ฐํ•˜๋ฉด ์น˜์œ ๋ ฅ, ๊ณง ๋‚ซ๊ฒŒ ํ•˜๋Š” ํž˜์ด ๊ฐ•ํ•ด์ง€๊ธฐ ๋•Œ๋ฌธ์— ๋นจ๋ฆฌ ๋‚ซ๋Š”๋‹ค. ๊ฐ€๋Šฅํ•œํ•œ ์ž ์ž๊ธฐ ์ „์— ํ•œ๋‹ค. ์•„๋‹ˆ๋ฉด ์•„์นจ์— ๋ˆˆ์„ ๋– ์„œ '์ž˜ ์žค๋‹ค. ๊ธฐ๋ถ„์ข‹๋‹ค.' ํ•˜๊ณ  ๋งํ•ด๋ณธ๋‹ค.