AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty Dumps Full Questions with Free PDF Questions to Pass
100% Updated Amazon AWS-Certified-Machine-Learning-Specialty Enterprise PDF Dumps
Amazon MLS-C01 certification exam is a valuable credential for professionals who want to demonstrate their expertise in machine learning and AWS technologies. Candidates who pass the exam are recognized as AWS Certified Machine Learning Specialists, which can help them advance their careers and open up new job opportunities. AWS Certified Machine Learning - Specialty certification also demonstrates a commitment to continuous learning and development, as candidates are required to maintain their certification by completing continuing education activities and renewing their certification every two years.
NEW QUESTION # 23
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 # 24
A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city As this is a prototype, only daily data from the last year is available Which model is MOST likely to provide the best results in Amazon SageMaker?
- A. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
- B. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
- C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.
- D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
Answer: D
NEW QUESTION # 25
A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population How should the Data Scientist correct this issue?
- A. Use k-means clustering to handle missing features
- B. Drop the age feature from the dataset and train the model using the rest of the features.
- C. Replace the age field value for records with a value of 0 with the mean or median value from the dataset
- D. Drop all records from the dataset where age has been set to 0.
Answer: D
Explanation:
Explanation
NEW QUESTION # 26
An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?
- A. r5.2xlarge (memory optimized)
- B. p3.2xlarge (GPU accelerated computing)
- C. p3 8xlarge (GPU accelerated computing)
- D. . m5 4xlarge (general purpose)
Answer: B
NEW QUESTION # 27
A Machine Learning Specialist needs to move and transform data in preparation for training Some of the data needs to be processed in near-real time and other data can be moved hourly There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data Which of the following services can feed data to the MapReduce jobs? (Select TWO )
- A. Amazon Athena
- B. Amazon Kinesis
- C. AWSDMS
- D. AWS Data Pipeline
- E. Amazon ES
Answer: B,D
Explanation:
https://aws.amazon.com/jp/emr/?whats-new-cards.sort-by=item.additionalFields.postDateTime&whats-new-cards.sort-order=desc
NEW QUESTION # 28
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?
- A. Binomial distribution
- B. Uniform distribution
- C. Normal distribution
- D. Poisson distribution ,
Answer: D
NEW QUESTION # 29
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? (Choose three.)
- A. Decrease dropout.
- B. Decrease feature combinations.
- C. Increase regularization.
- D. Increase feature combinations.
- E. Decrease regularization.
- F. Increase dropout.
Answer: B,C,F
Explanation:
Feature selection: consider using fewer feature combinations, decrease n-grams size, and decrease the number of numeric attribute bins.
Increase the amount of regularization used
https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html
NEW QUESTION # 30
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 locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.
- B. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset
- C. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
- D. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
Answer: C
NEW QUESTION # 31
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?
- A. Create VPC peering with Amazon VPC hosting Amazon SageMaker.
- B. Create a NAT gateway within the corporate VPC.
- C. Route Amazon SageMaker traffic through an on-premises network.
- D. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
Answer: B
Explanation:
https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf (46)
NEW QUESTION # 32
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?
- A. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body
- B. Build the Docker container to be NVIDIA-Docker compatible.
- C. Organize the Docker container's file structure to execute on GPU instances.
- D. Bundle the NVIDIA drivers with the Docker image.
Answer: C
NEW QUESTION # 33
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 dropout rate for the second-to-last layer.
- B. Increase the training data by adding variation in rotation for training images.
- C. Increase the number of layers for the neural network.
- D. Increase the number of epochs for model training.
Answer: A
NEW QUESTION # 34
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll). The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?
- A. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.
- B. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
- C. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
- D. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
Answer: C
NEW QUESTION # 35
An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, any sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the dat a. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining.
How should a machine learning specialist meet these requirements?
- A. Use PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.
- B. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site- to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.
- C. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.
- D. Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.
Answer: D
NEW QUESTION # 36
A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.
Which solution requires the LEAST coding effort?
- A. Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.
- B. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3
- C. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team
- D. Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team
Answer: C
NEW QUESTION # 37
A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.
The company requires at least 85% accuracy to make use of the model.
After an exhaustive grid search, the optimal hyperparameters produced the following:
68% accuracy on the training set
67% accuracy on the validation set
What can the machine learning specialist do to improve the system's accuracy?
- A. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.
- B. Add more data to the training set and retrain the model using transfer learning to reduce the bias.
- C. Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.
- D. Train a new model using the current neural network architecture.
Answer: B
NEW QUESTION # 38
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset Which tool should be used to improve the validation accuracy?
- A. Amazon SageMaker BlazingText allow mode
- B. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers
- C. Natural Language Toolkit (NLTK) stemming and stop word removal
- D. Amazon Comprehend syntax analysts and entity detection
Answer: C
NEW QUESTION # 39
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s) Which visualization will accomplish this?
- A. A scatter plot showing (he performance of the objective metric over each training iteration
- B. A histogram showing whether the most important input feature is Gaussian.
- C. A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.
- D. A scatter plot showing the correlation between maximum tree depth and the objective metric.
Answer: C
NEW QUESTION # 40
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric. This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours.
With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s).
Which visualization will accomplish this?
- A. A histogram showing whether the most important input feature is Gaussian.
- B. A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.
- C. A scatter plot showing the performance of the objective metric over each training iteration.
- D. A scatter plot showing the correlation between maximum tree depth and the objective metric.
Answer: B
NEW QUESTION # 41
A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined The model needs lo be retrained daily Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?
- A. Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehouse stream that transforms raw record attributes into simple transformed values using SQL.
- B. Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.
- C. Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3
- D. Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3 then use AWS Glue to do the transformation
Answer: A
NEW QUESTION # 42
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