Certification Training for AWS-Certified-Machine-Learning-Specialty Exam Dumps Test Engine [2024]
Jan 01, 2024 Step by Step Guide to Prepare for AWS-Certified-Machine-Learning-Specialty Exam
To be eligible for the AWS Certified Machine Learning - Specialty exam, candidates should have a minimum of one to two years of experience in machine learning and a solid understanding of AWS services and architecture. Additionally, candidates should be familiar with programming languages such as Python, R, and Java, and have experience working with data processing and analysis tools such as Apache Spark and TensorFlow. Passing AWS-Certified-Machine-Learning-Specialty exam can help professionals showcase their skills and expertise in the field of machine learning and open up new career opportunities.
The AWS Certified Machine Learning - Specialty exam covers a wide range of topics such as data engineering, data analysis, machine learning algorithms, and deployment. AWS-Certified-Machine-Learning-Specialty exam also tests the candidate's ability to design and implement machine learning solutions that are cost-effective, scalable, and reliable. It is a challenging exam that requires a deep understanding of machine learning concepts and their practical applications in real-world scenarios.
NEW QUESTION # 52
A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago Which method should the Specialist try to improve model performance?
- A. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
- B. The model needs to be completely re-engineered because it is unable to handle product inventory changes
- C. The model's hyperparameters should be periodically updated to prevent drift
- D. The model should be periodically retrained using the original training data plus new data as product inventory changes
Answer: D
NEW QUESTION # 53
A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day, the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.
Which storage scheme is MOST adapted to this scenario?
- A. Store datasets as global tables in Amazon DynamoDB.
- B. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
- C. Store datasets as tables in a multi-node Amazon Redshift cluster.
- D. Store datasets as files in Amazon S3.
Answer: D
NEW QUESTION # 54
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?
- A. The precision of the model is 86%, which is less than the accuracy of the model.
- B. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
- C. The precision of the model is 86%, which is greater than the accuracy of the model.
- D. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
Answer: A
NEW QUESTION # 55
A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing.
The Data Scientist has been given the following requirements to the cloud solution:
* Combine multiple data sources.
* Reuse existing PySpark logic.
* Run the solution on the existing schedule.
* Minimize the number of servers that will need to be managed.
Which architecture should the Data Scientist use to build this solution?
- A. Write the raw data to Amazon S3. Create an AWS Glue ETL job to perform the ETL processing against the input data. Write the ETL job in PySpark to leverage the existing logic. Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule. Configure the output target of the ETL job to write to a
"processed" location in Amazon S3 that is accessible for downstream use. - B. Write the raw data to Amazon S3. Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3. Write the Lambda logic in Python and implement the existing PySpark logic to perform the ETL process. Have the Lambda function output the results to a "processed" location in Amazon S3 that is accessible for downstream use.
- C. Use Amazon Kinesis Data Analytics to stream the input data and perform real-time SQL queries against the stream to carry out the required transformations within the stream. Deliver the output results to a
"processed" location in Amazon S3 that is accessible for downstream use. - D. Write the raw data to Amazon S3. Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule. Use the existing PySpark logic to run the ETL job on the EMR cluster. Output the results to a "processed" location in Amazon S3 that is accessible for downstream use.
Answer: C
NEW QUESTION # 56
A company is building a new version of a recommendation engine. Machine learning (ML) specialists need to keep adding new data from users to improve personalized recommendations. The ML specialists gather data from the users' interactions on the platform and from sources such as external websites and social media.
The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this data is stored in Amazon S3. A set of Python scripts was coded to do the job and is stored in a large Amazon EC2 instance. The whole process takes more than 20 hours to finish, with each script taking at least an hour. The company wants to move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers.
Which approach will address all of these requirements with the LEAST development effort?
- A. Create an AWS Glue job. Convert the scripts to PySpark. Execute the pipeline. Store the results in Amazon S3.
- B. Load the data into Amazon DynamoDB. Convert the scripts to an AWS Lambda function. Execute the pipeline by triggering Lambda executions. Store the results in Amazon S3.
- C. Create a set of individual AWS Lambda functions to execute each of the scripts. Build a step function by using the AWS Step Functions Data Science SDK. Store the results in Amazon S3.
- D. Load the data into an Amazon Redshift cluster. Execute the pipeline by using SQL. Store the results in Amazon S3.
Answer: B
NEW QUESTION # 57
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?
- A. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
- B. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
- C. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
- D. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
Answer: A
NEW QUESTION # 58
A Machine Learning Engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML Engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%.
What should the ML Engineer do to minimize bias due to missing values?
- A. For each feature, approximate the missing values using supervised learning based on other features.
- B. Replace each missing value by the mean or median across non-missing values in the same column.
- C. Delete observations that contain missing values because these represent less than 5% of the data.
- D. Replace each missing value by the mean or median across non-missing values in same row.
Answer: A
Explanation:
Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C.
Supervised learning applied to the imputation of missing values is an active field of research.
NEW QUESTION # 59
A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.
Which feature engineering strategy should the ML specialist use with Amazon SageMaker?
- A. Concatenate the features with high correlation scores by using a Jupyter notebook.
- B. Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.
- C. Drop the features with low correlation scores by using a Jupyter notebook.
- D. Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.
Answer: B
NEW QUESTION # 60
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. High-degree polynomial transformation
- B. Numerical value binning
- C. Cross-validation
- D. One hot encoding
- E. Logarithmic transformation
Answer: B,C
NEW QUESTION # 61
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. Softmax
- B. Dropout
- C. Smooth L1 loss
- D. Rectified linear units (ReLU)
Answer: A
Explanation:
https://medium.com/data-science-bootcamp/understand-the-softmax-function-in-minutes- f3a59641e86d
NEW QUESTION # 62
A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.
The ingestion process must buffer and convert incoming records from JSON to a query- optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.
Which solution should the Data Scientist build to satisfy the requirements?
- A. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
- B. Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
- C. Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
- D. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL database. Have the Analysts query and run dashboards from the RDS database.
Answer: C
NEW QUESTION # 63
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
Which model will meet the business requirement?
- A. Principal component analysis (PCA)
- B. K-means
- C. Logistic regression
- D. Linear regression
Answer: D
NEW QUESTION # 64
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 bucket. Create an ECS cluster that is based on an AWS Deep Learning Containers image. Write the code to perform the feature engineering. Train a logistic regression model for predicting the price, pointing to the bucket with the dataset. Wait for the training job to complete. Perform the inferences.
- B. Create an Amazon SageMaker notebook with a new IAM role that is associated with the notebook. Pull the dataset from the S3 bucket. Explore different combinations of feature engineering transformations, regression algorithms, and hyperparameters. Compare all the results in the notebook, and deploy the most accurate configuration in an endpoint for predictions.
- C. Create an IAM role for Amazon SageMaker with access to the S3 bucket. Create a SageMaker AutoML job with SageMaker Autopilot pointing to the bucket with the dataset. Specify the price as the target attribute. Wait for the job to complete. Deploy the best model for predictions.
- D. Create an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda. Create a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset. Specify the price as the target feature. Wait for the job to complete. Load the model artifact to a Lambda function for inference on prices of new houses.
Answer: A
NEW QUESTION # 65
A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.
How should the Data Science team configure the notebook instance placement to meet these requirements?
- A. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
- B. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker.
- C. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.
- D. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
Answer: D
Explanation:
We must use the VPC endpoint (either Gateway Endpoint or Interface Endpoint)to comply with this requirement "Data communication traffic must stay within the AWS network".
https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-interface-endpoint.html
NEW QUESTION # 66
A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test dat a. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.
Which parameter tuning guidelines should the Specialist follow to avoid overfitting?
- A. Increase the max_depth parameter value.
- B. Lower the min_child_weight parameter value.
- C. Lower the max_depth parameter value.
- D. Update the objective to binary:logistic.
Answer: C
NEW QUESTION # 67
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