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Question 1:

A web-based company wants to improve its conversion rate on its landing page. Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker. However, there is an overfitting problem: training data shows 90% accuracy in predictions, while test data shows 70% accuracy only.

The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases.

Which action is recommended to provide the HIGHEST accuracy model for the company\’s test and validation data?

A. Increase the randomization of training data in the mini-batches used in training.

B. Allocate a higher proportion of the overall data to the training dataset

C. Apply L1 or L2 regularization and dropouts to the training.

D. Reduce the number of layers and units (or neurons) from the deep learning network.

Correct Answer: D


Question 2:

A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.

The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist has been asked to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Choose two.)

A. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.

B. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

C. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.

D. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.

E. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

Correct Answer: DE


Question 3:

A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1..10]:

Considering the graph, what is a reasonable selection for the optimal choice of k?

A. 1

B. 4

C. 7

D. 10

Correct Answer: C


Question 4:

A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the training accuracy is 90% It is known that human-level performance for this type of image classification is around 90%

What should the Specialist consider to fix this issue1?

A. A longer training time

B. Making the network larger

C. Using a different optimizer

D. Using some form of regularization

Correct Answer: D


Question 5:

An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time.

Which solution should the agency consider?

A. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection of known employees, and alert when non-employees are detected.

B. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detect faces from a collection of known employees and alert when non-employees are detected.

C. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when nonemployees are detected.

D. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected.

Correct Answer: D

Reference: https://aws.amazon.com/blogs/machine-learning/video-analytics-in-the-cloud-and-at-the-edgewith-aws-deeplens-and-kinesis-video-streams/


Question 6:

Example Corp has an annual sale event from October to December. The company has sequential sales data from the past 15 years and wants to use Amazon ML to predict the sales for this year\’s upcoming event. Which method should Example Corp use to split the data into a training dataset and evaluation dataset?

A. Pre-split the data before uploading to Amazon S3

B. Have Amazon ML split the data randomly.

C. Have Amazon ML split the data sequentially.

D. Perform custom cross-validation on the data

Correct Answer: C


Question 7:

A bank\’s Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not

Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

A. Seq2seq

B. XGBoost

C. K-means

D. Random Cut Forest (RCF)

Correct Answer: C


Question 8:

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

A. The training channel identifying the location of training data on an Amazon S3 bucket.

B. The validation channel identifying the location of validation data on an Amazon S3 bucket.

C. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.

D. Hyperparameters in a JSON array as documented for the algorithm used.

E. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.

F. The output path specifying where on an Amazon S3 bucket the trained model will persist.

Correct Answer: AEF


Question 9:

A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat.

The ML Specialist performs some tests and records the following results for a neural network-based image

classifier:

Total number of images available = 1,000 Test set images = 100 (constant test set)

The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by

their owners.

Which techniques can be used by the ML Specialist to improve this specific test error?

A. Increase the training data by adding variation in rotation for training images.

B. Increase the number of epochs for model training.

C. Increase the number of layers for the neural network.

D. Increase the dropout rate for the second-to-last layer.

Correct Answer: B


Question 10:

The Chief Editor for a product catalog wants the Research and Development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company\’s retail brand The team has a set of training data

Which machine learning algorithm should the researchers use that BEST meets their requirements?

A. Latent Dirichlet Allocation (LDA)

B. Recurrent neural network (RNN)

C. K-means

D. Convolutional neural network (CNN)

Correct Answer: C


Question 11:

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 quenes

D. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries

Correct Answer: D


Question 12:

A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training

What should the Specialist do to optimize the data for training on SageMaker\’?

A. Use the SageMaker batch transform feature to transform the training data into a DataFrame

B. Use AWS Glue to compress the data into the Apache Parquet format

C. Transform the dataset into the Recordio protobuf format

D. Use the SageMaker hyperparameter optimization feature to automatically optimize the data

Correct Answer: C


Question 13:

An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data

Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

A. Listwise deletion

B. Last observation carried forward

C. Multiple imputation

D. Mean substitution

Correct Answer: C

Reference: https://worldwidescience.org/topicpages/i/imputing missing values.html


Question 14:

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. Logistic regression

B. Random Cut Forest (RCF)

C. Principal component analysis (PCA)

D. Linear regression

Correct Answer: B


Question 15:

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 accuracy?

A. Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))

B. Logistic regression

C. Support vector machine (SVM) with non-linear kernel

D. Single perceptron with tanh activation function

Correct Answer: B

Reference: https://machinelearningmastery.com/logistic-regression-for-machine-learning/