The Secret Of Amazon AWS-Certified-Machine-Learning-Specialty Testing Material

NEW QUESTION 1
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

• A. Recall
• B. Misclassification rate
• C. Mean absolute percentage error (MAPE)
• D. Area Under the ROC Curve (AUC)

Answer: D

NEW QUESTION 2
A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price.
Which techniques should the company use for feature selection? (Choose three.)

• A. Data scaling with standardization and normalization
• B. Correlation plot with heat maps
• C. Data binning
• D. Univariate selection
• E. Feature importance with a tree-based classifier
• F. Data augmentation

Answer: CDF

NEW QUESTION 3
A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be used downstream for alerting and incident response. The data scientist has access to unlabeled historic data to use, if needed.
The solution needs to do the following:
Calculate an anomaly score for each web traffic entry.
Adapt unusual event identification to changing web patterns over time. Which approach should the data scientist implement to meet these requirements?

• A. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random Cut Forest (RCF) built-in mode
• B. Use an Amazon Kinesis Data Stream to process the incoming webtrafficdat
• C. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the RCF modelto calculate the anomaly score for each record.
• D. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-inXGBoost mode
• E. Use an Amazon Kinesis Data Stream to process the incoming web traffic dat
• F. Attach apreprocessing AWS Lambda function to perform data enrichment by calling the XGBoost model to calculate the anomaly score for each record.
• G. Collect the streaming data using Amazon Kinesis Data Firehos
• H. Map the delivery stream as an inputsource for Amazon Kinesis Data Analytic
• I. Write a SQL query to run in real time against the streaming datawith the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for each record using a tumbling window.
• J. Collect the streaming data using Amazon Kinesis Data Firehos
• K. Map the delivery stream as an inputsource for Amazon Kinesis Data Analytic
• L. Write a SQL query to run in real time against the streaming datawith the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record using a sliding window.

Answer: D

NEW QUESTION 4
While working on a neural network project, a Machine Learning Specialist discovers thai some features in the data have very high magnitude resulting in this data being weighted more in the cost function What should the Specialist do to ensure better convergence during backpropagation?

• A. Dimensionality reduction
• B. Data normalization
• C. Model regulanzation
• D. Data augmentation for the minority class

Answer: D

NEW QUESTION 5
A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features. The Marketing team has not provided any insight about which features are relevant for churn prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide gap between the training and validation set accuracy.
Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team’s needs? (Choose two.)

• A. Add L1 regularization to the classifier
• B. Add features to the dataset
• C. Perform recursive feature elimination
• D. Perform t-distributed stochastic neighbor embedding (t-SNE)
• E. Perform linear discriminant analysis

Answer: BE

NEW QUESTION 6
A large JSON dataset for a project has been uploaded to a private Amazon S3 bucket The Machine Learning Specialist wants to securely access and explore the data from an Amazon SageMaker notebook instance A new VPC was created and assigned to the Specialist
How can the privacy and integrity of the data stored in Amazon S3 be maintained while granting access to the Specialist for analysis?

• A. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled Use an S3 ACL to open read privileges to the everyone group
• B. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Copy the JSON dataset from Amazon S3 into the ML storage volume on the SageMaker notebook instance and work against the local dataset
• C. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Define a custom S3 bucket policy to only allow requests from your VPC to access the S3 bucket
• D. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enable
• E. Generate an S3 pre-signed URL for access to data in the bucket

Answer: B

NEW QUESTION 7
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 8
A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.
Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

• A. Cross-validation
• B. Numerical value binning
• C. High-degree polynomial transformation
• D. Logarithmic transformation
• E. One hot encoding

Answer: AB

NEW QUESTION 9
A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?

• A. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
• B. AWS Glue with a custom ETL script to transform the data.
• C. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
• D. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

Answer: A

NEW QUESTION 10
A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.
The data scientists are using Amazon Forecast to generate the forecasts.
Which algorithm in Forecast should the data scientists use to meet these requirements?

• A. Autoregressive Integrated Moving Average (AIRMA)
• B. Exponential Smoothing (ETS)
• C. Convolutional Neural Network - Quantile Regression (CNN-QR)
• D. Prophet

Answer: B

NEW QUESTION 11
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: C

NEW QUESTION 12
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values. What technique should be used to convert this column to binary values.

• A. Binarization
• B. One-hot encoding
• C. Tokenization
• D. Normalization transformation

Answer: B

NEW QUESTION 13
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: BCF

NEW QUESTION 14
A company wants to create a data repository in the AWS Cloud for machine learning (ML) projects. The company wants to use AWS to perform complete ML lifecycles and wants to use Amazon S3 for the data storage. All of the company’s data currently resides on premises and is 40 in size.
The company wants a solution that can transfer and automatically update data between the on-premises object storage and Amazon S3. The solution must support encryption, scheduling, monitoring, and data integrity validation.
Which solution meets these requirements?

• A. Use the S3 sync command to compare the source S3 bucket and the destination S3 bucke
• B. Determine which source files do not exist in the destination S3 bucket and which source files were modified.
• C. Use AWS Transfer for FTPS to transfer the files from the on-premises storage to Amazon S3.
• D. Use AWS DataSync to make an initial copy of the entire datase
• E. Schedule subsequent incremental transfers of changing data until the final cutover from on premises to AWS.
• F. Use S3 Batch Operations to pull data periodically from the on-premises storag
• G. Enable S3 Versioning on the S3 bucket to protect against accidental overwrites.

Answer: C

Explanation:
Configure DataSync to make an initial copy of your entire dataset, and schedule subsequent incremental transfers of changing data until the final cut-over from on-premises to AWS.

NEW QUESTION 15
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: D

NEW QUESTION 16
A real-estate company is launching a new product that predicts the prices of new houses. The historical data for the properties and prices is stored in .csv format in an Amazon S3 bucket. The data has a header, some categorical fields, and some missing values. The company’s data scientists have used Python with a common open-source library to fill the missing values with zeros. The data scientists have dropped all of the categorical fields and have trained a model by using the open-source linear regression algorithm with the default parameters.
The accuracy of the predictions with the current model is below 50%. The company wants to improve the model performance and launch the new product as soon as possible.
Which solution will meet these requirements with the LEAST operational overhead?

• A. Create a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucke
• B. Create an ECS cluster that is based on an AWS Deep Learning Containers imag
• C. Write the code to perform the feature engineerin
• D. Train a logistic regression model for predicting the price, pointing to the bucket with the datase
• E. Wait for the training job to complet
• F. Perform the inferences.
• G. Create an Amazon SageMaker notebook with a new IAM role that is associated with the noteboo
• H. Pull the dataset from the S3 bucke
• I. Explore different combinations of feature engineering transformations,regression algorithms, and hyperparameter
• J. Compare all the results in the notebook, and deploy the most accurate configuration in an endpoint for predictions.
• K. Create an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambd
• L. Create a training job with the SageMaker built-in XGBoost model pointing to the bucket with the datase
• M. Specify the price as the target featur
• N. Wait for the job to complet
• O. Load the model artifact to a Lambda function for inference on prices of new houses.
• P. Create an IAM role for Amazon SageMaker with access to the S3 bucke
• Q. Create a SageMaker AutoML job with SageMaker Autopilot pointing to the bucket with the datase
• R. Specify the price as the target attribut
• S. Wait for the job to complet
• T. Deploy the best model for predictions.

Answer: A

NEW QUESTION 17
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