The Secret Of Amazon AWS-Certified-Machine-Learning-Specialty Free Practice Exam

NEW QUESTION 1
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: C

NEW QUESTION 2
A company offers an online shopping service to its customers. The company wants to enhance the site’s security by requesting additional information when customers access the site from locations that are different from their normal location. The company wants to update the process to call a machine learning (ML) model to determine when additional information should be requested.
The company has several terabytes of data from its existing ecommerce web servers containing the source IP addresses for each request made to the web server. For authenticated requests, the records also contain the login name of the requesting user.
Which approach should an ML specialist take to implement the new security feature in the web application?

• A. Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attemp
• B. Use Amazon SageMaker to train a binary classification model using the factorization machines (FM) algorithm.
• C. Use Amazon SageMaker to train a model using the IP Insights algorith
• D. Schedule updates and retraining of the model using new log data nightly.
• E. Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attemp
• F. Use Amazon SageMaker to train a binary classification model using the IP Insights algorithm.
• G. Use Amazon SageMaker to train a model using the Object2Vec algorith
• H. Schedule updates and retraining of the model using new log data nightly.

Answer: C

NEW QUESTION 3
A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.

How should the data scientist transform the data?

• A. Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata datase
• B. Upload both datasets as .csv files to Amazon S3.
• C. Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata datase
• D. Upload both datasets as tables in Amazon Aurora.
• E. Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata datase
• F. Upload them directly to Forecast from a local machine.
• G. Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO forma
• H. Upload the dataset in this format to Amazon S3.

Answer: A

Explanation:
https://docs.aws.amazon.com/forecast/latest/dg/dataset-import-guidelines-troubleshooting.html

NEW QUESTION 4
A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model’s accuracy. The learning rate parameter is specified in the following HPO configuration:

During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.
Which solution provides the MOST accurate result?

• A. Modify the HPO configuration as follows: C:\Users\Admin\Desktop\Data\Odt data\Untitled.jpgSelect the most accurate hyperparameter configuration form this HPO job.
• B. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job:[0.01, 0.1][0.001, 0.01][0.0001, 0.001]Select the most accurate hyperparameter configuration form these three HPO jobs.
• C. Modify the HPO configuration as follows: C:\Users\Admin\Desktop\Data\Odt data\Untitled.jpgSelect the most accurate hyperparameter configuration form this training job.
• D. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValu
• E. Divide the number of training jobs for each HPO job by three:[0.01, 0.1][0.001, 0.01][0.0001, 0.001]Select the most accurate hyperparameter configuration form these three HPO jobs.

Answer: C

NEW QUESTION 5
An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored for offline analysis.
What approach would be the MOST effective to perform near-real time defect detection?

• A. Use AWS IoT Analytics for ingestion, storage, and further analysi
• B. Use Jupyter notebooks from within AWS IoT Analytics to carry out analysis for anomalies.
• C. Use Amazon S3 for ingestion, storage, and further analysi
• D. Use an Amazon EMR cluster to carry out Apache Spark ML k-means clustering to determine anomalies.
• E. Use Amazon S3 for ingestion, storage, and further analysi
• F. Use the Amazon SageMaker Random Cut Forest (RCF) algorithm to determine anomalies.
• G. Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detectio
• H. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.

Answer: B

NEW QUESTION 6
A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term. The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion.
How will the data scientist MOST effectively model the problem?

• A. The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem.
• B. The data scientist should obtain the optimal equilibrium policy by formulating this problem as a single-agent reinforcement learning problem.
• C. Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using historical data through a supervised learning approach.
• D. Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using unlabeled simulated data representing the new traffic patterns in the city and applying an unsupervised learning approach.

Answer: D

NEW QUESTION 7
A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3
The source systems send data in CSV format in real lime The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3
Which solution takes the LEAST effort to implement?

• A. Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 toserialize data as Parquet
• B. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
• C. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.
• D. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.

Answer: B

Explanation:
https://medium.com/searce/convert-csv-json-files-to-apache-parquet-using-aws-glue-a760d177b45f https://github.com/ecloudvalley/Building-a-Data-Lake-with-AWS-Glue-and-Amazon-S3

NEW QUESTION 8
A machine learning specialist needs to analyze comments on a news website with users across the globe. The specialist must find the most discussed topics in the comments that are in either English or Spanish.
What steps could be used to accomplish this task? (Choose two.)

• A. Use an Amazon SageMaker BlazingText algorithm to find the topics independently from language.Proceed with the analysis.
• B. Use an Amazon SageMaker seq2seq algorithm to translate from Spanish to English, if necessar
• C. Use aSageMaker Latent Dirichlet Allocation (LDA) algorithm to find the topics.
• D. Use Amazon Translate to translate from Spanish to English, if necessar
• E. Use Amazon Comprehend topic modeling to find the topics.
• F. Use Amazon Translate to translate from Spanish to English, if necessar
• G. Use Amazon Lex to extract topics form the content.
• H. Use Amazon Translate to translate from Spanish to English, if necessar
• I. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics.

Answer: B

NEW QUESTION 9
A Machine Learning Specialist is attempting to build a linear regression model.
Given the displayed residual plot only, what is the MOST likely problem with the model?

• A. Linear regression is inappropriat
• B. The residuals do not have constant variance.
• C. Linear regression is inappropriat
• D. The underlying data has outliers.
• E. Linear regression is appropriat
• F. The residuals have a zero mean.
• G. Linear regression is appropriat
• H. The residuals have constant variance.

Answer: D

NEW QUESTION 10
A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment.
Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

• A. Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
• B. Use SCPs to restrict access to SageMaker.
• C. Disable root access on the SageMaker notebook instances.
• D. Enable network isolation for training jobs and models.
• E. Restrict notebook presigned URLs to specific IPs used by the company.
• F. Protect data with encryption at rest and in transi
• G. Use AWS Key Management Service (AWS KMS) to manage encryption keys.

Answer: BDE

Explanation:
https://aws.amazon.com/blogs/machine-learning/millennium-management-secure-machine-learning-using-amaz

NEW QUESTION 11
A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
Which of the following will accomplish this? (Select TWO.)

• A. Customize the built-in image classification algorithm to use Inception and use this for model training.
• B. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
• C. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.
• D. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and use this for model training.
• E. Download and apt-get install the inception network code into an Amazon EC2 instance and use thisinstance as a Jupyter notebook in Amazon SageMaker.

Answer: AD

NEW QUESTION 12
A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.
Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)

• A. Emails exchanged by customers and the company’s customer service agents
• B. Social media posts containing the name of the company or its products
• C. A publicly available collection of news articles
• D. A publicly available collection of customer reviews
• E. Product sales revenue figures for the company
• F. Instruction manuals for the company’s products

Answer: BDF

NEW QUESTION 13
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora
• Profiles for all past and existing customers
• Profiles for all past and existing insured pets
• Policy-level information
• Premiums received
• Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

• A. Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
• B. Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
• C. Use a recommendation engine on customer profile data to understand key characteristics of consumer segment
• D. Find similar profiles on social media
• E. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segment
• F. Find similar profiles on social media

Answer: C

NEW QUESTION 14
A Data Scientist is building a linear regression model and will use resulting p-values to evaluate the statistical significance of each coefficient. Upon inspection of the dataset, the Data Scientist discovers that most of the features are normally distributed. The plot of one feature in the dataset is shown in the graphic.

What transformation should the Data Scientist apply to satisfy the statistical assumptions of the linear regression model?

• A. Exponential transformation
• B. Logarithmic transformation
• C. Polynomial transformation
• D. Sinusoidal transformation

Answer: A

NEW QUESTION 15
Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

• A. The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20 85%
• B. The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 1312%
• C. The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 - 0.32).
• D. The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ' 0.32

Answer: B

Explanation:
https://docs.aws.amazon.com/machine-learning/latest/dg/multiclass-model-insights.html

NEW QUESTION 16
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines.
* Provide a quick and easy way to understand metadata. Which approach meets trfese requirements?

• A. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
• B. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
• C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
• D. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

Answer: A

NEW QUESTION 17
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