NEW QUESTION 1
A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been
advised that identifying valid transactions is equally as important as identifying fraudulent transactions What metric is BEST suited to score the model?

• A. Precision
• B. Recall
• C. Area Under the ROC Curve (AUC)
• D. Root Mean Square Error (RMSE)

Answer: A

NEW QUESTION 2
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

Answer: C

NEW QUESTION 3
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

Answer: D

NEW QUESTION 4
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? (Select TWO.)

• A. AWS CloudTrail
• B. AWS Health
• C. AWS Trusted Advisor
• D. Amazon CloudWatch
• E. AWS Config

Answer: AD

NEW QUESTION 5
A Machine Learning Specialist is building a supervised model that will evaluate customers' satisfaction with their mobile phone service based on recent usage The model's output should infer whether or not a customer is likely to switch to a competitor in the next 30 days
Which of the following modeling techniques should the Specialist use1?

• A. Time-series prediction
• B. Anomaly detection
• C. Binary classification
• D. Regression

Answer: D

NEW QUESTION 6
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)

Answer: C

NEW QUESTION 7
During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates What is the MOST likely cause of this issue?

• A. The class distribution in the dataset is imbalanced
• B. Dataset shuffling is disabled
• C. The batch size is too big
• D. The learning rate is very high

Answer: D

NEW QUESTION 8
A Machine Learning Specialist is training a model to identify the make and model of vehicles in images The Specialist wants to use transfer learning and an existing model trained on images of general objects The Specialist collated a large custom dataset of pictures containing different vehicle makes and models

• A. Initialize the model with random weights in all layers including the last fully connected layer
• B. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
• C. Initialize the model with random weights in all layers and replace the last fully connected layer
• D. Initialize the model with pre-trained weights in all layers including the last fully connected layer

Answer: B

NEW QUESTION 9
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 10
For the given confusion matrix, what is the recall and precision of the model?

• A. Recall = 0.92 Precision = 0.84
• B. Recall = 0.84 Precision = 0.8
• C. Recall = 0.92 Precision = 0.8
• D. Recall = 0.8 Precision = 0.92

Answer: A

NEW QUESTION 11
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy js acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes
What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

• A. Do not change the TensorFlow cod
• B. Change the machine to one with a more powerful GPU to speed up the training.
• C. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMake
• D. Parallelize the training to as many machines as needed to achieve the business goals.
• E. Switch to using a built-in AWS SageMaker DeepAR mode
• F. Parallelize the training to as many machines as needed to achieve the business goals.
• G. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

Answer: B

NEW QUESTION 12
A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model
What should the Specialist do to address the performance issues with the current solution?

• A. Use the SageMaker batch transform feature
• B. Compress the training data into Apache Parquet format.
• C. Ensure that the input mode for the training job is set to Pipe.
• D. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.

Answer: B

NEW QUESTION 13
A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested The company also wants be able to save the results in its data lake for later processing and analysis
What is the MOST efficient way to accomplish these tasks'?

• A. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection Then use Kinesis Data Firehose to stream the results to Amazon S3
• B. Ingest the data into Apache Spark Streaming using Amazon EM
• C. and use Spark MLlib with k-means to perform anomaly detection Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake
• D. Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.
• E. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data

Answer: B

NEW QUESTION 14
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

Answer: C

NEW QUESTION 15
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)

Answer: C

NEW QUESTION 16
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?

• A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
• B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
• C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
• D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Answer: B

Explanation:
Many developers want to implement the famous Amazon model that was used to power the “People who bought this also bought these items” feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.

NEW QUESTION 17
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

Answer: D

NEW QUESTION 18
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)

• A. Decrease regularization.
• B. Increase regularization.
• C. Increase dropout.
• D. Decrease dropout.
• E. Increase feature combinations.
• F. Decrease feature combinations.

Answer: BDE

NEW QUESTION 19
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

Answer: B

NEW QUESTION 20
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 locall
• B. Confirm that the training code is executing and the model parameters seem reasonabl
• C. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
• D. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to theinstanc
• E. Train on a small amount of the data to verify the training code and hyperparameter
• F. Go back toAmazon SageMaker and train using the full dataset
• G. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatiblewith Amazon SageMake
• H. Initiate a SageMaker training job using the full dataset from the S3 bucket usingPipe input mode.
• I. Load a smaller subset of the data into the SageMaker notebook and train locall
• J. Confirm that the training code is executing and the model parameters seem reasonabl
• K. 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 21
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen
Which combination of algorithms would provide the appropriate insights? (Select TWO )

• A. The factorization machines (FM) algorithm
• B. The Latent Dirichlet Allocation (LDA) algorithm
• C. The principal component analysis (PCA) algorithm
• D. The k-means algorithm
• E. The Random Cut Forest (RCF) algorithm

Answer: CD

Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.

NEW QUESTION 22
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? (Select 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. One-hot encode all words in the sentence
• F. Tokenize the sentence into words.

Answer: ABD

NEW QUESTION 23
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 24
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