[Apr-2024] Updated Amazon AWS-Certified-Machine-Learning-Specialty Dumps – PDF & Online Engine
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The AWS-Certified-Machine-Learning-Specialty exam is a challenging certification exam aimed at validating the skills and knowledge of individuals who want to design, implement, and deploy machine learning solutions using AWS services. Candidates who pass AWS-Certified-Machine-Learning-Specialty exam can demonstrate their expertise in machine learning and can enhance their career prospects in the field of data science and machine learning.
Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Certification Exam is a professional certification that validates a candidate's expertise in designing, developing, and deploying machine learning models using Amazon Web Services (AWS). AWS Certified Machine Learning - Specialty certification is intended for individuals who have a strong understanding of machine learning and are looking to demonstrate their skills and knowledge in this field.
NEW QUESTION # 69
A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training What should the Specialist do to optimize the data for training on SageMaker'?
- A. Transform the dataset into the Recordio protobuf format
- B. Use the SageMaker batch transform feature to transform the training data into a DataFrame
- C. Use the SageMaker hyperparameter optimization feature to automatically optimize the data
- D. Use AWS Glue to compress the data into the Apache Parquet format
Answer: A
Explanation:
The Recordio protobuf format is a binary data format that is optimized for training on SageMaker. It allows faster data loading and lower memory usage compared to other formats such as CSV or numpy arrays. The Recordio protobuf format also supports features such as sparse input, variable-length input, and label embedding. To use the Recordio protobuf format, the data needs to be serialized and deserialized using the appropriate libraries. Some of the built-in algorithms in SageMaker support the Recordio protobuf format as a content type for training and inference. References:
Common Data Formats for Training
Using RecordIO Format
Content Types Supported by Built-in Algorithms
NEW QUESTION # 70
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 number of epochs for model training.
- 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 dropout rate for the second-to-last layer.
Answer: B
NEW QUESTION # 71
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
- Real-time analytics
- Interactive analytics of historical data
- Clickstream analytics
- Product recommendations
Which services should the Specialist use?
- A. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
- B. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
- C. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations
- D. Amazon Athena as the data catalog: Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-real-time data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
Answer: B
NEW QUESTION # 72
A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?
- A. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
- B. Use an 1AM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
- C. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
- D. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
Answer: B
NEW QUESTION # 73
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. Dataset shuffling is disabled
- B. The class distribution in the dataset is imbalanced
- C. The batch size is too big
- D. The learning rate is very high
Answer: D
Explanation:
Explanation
Mini-batch gradient descent is a variant of gradient descent that updates the model parameters using a subset of the training data (called a mini-batch) at each iteration. The learning rate is a hyperparameter that controls how much the model parameters change in response to the gradient. If the learning rate is very high, the model parameters may overshoot the optimal values and oscillate around the minimum of the cost function. This can cause the training accuracy to fluctuate and prevent the model from converging to a stable solution. To avoid this issue, the learning rate should be chosen carefully, such as by using a learning rate decay schedule or an adaptive learning rate algorithm1. Alternatively, the batch size can be increased to reduce the variance of the gradient estimates2. However, the batch size should not be too big, as this can slow down the training process and reduce the generalization ability of the model3. Dataset shuffling and class distribution are not likely to cause oscillations in training accuracy, as they do not affect the gradient updates directly. Dataset shuffling can help avoid getting stuck in local minima and improve the convergence speed of mini-batch gradient descent4.
Class distribution can affect the performance and fairness of the model, especially if the dataset is imbalanced, but it does not necessarily cause fluctuations in training accuracy.
NEW QUESTION # 74
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?
- A. Perform one-hot encoding on highly correlated features
- B. Apply the Pearson correlation coefficient
- C. Use matrix multiplication on highly correlated features.
- D. Create a new feature space using principal component analysis (PCA)
Answer: C
NEW QUESTION # 75
A retail company stores 100 GB of daily transactional data in Amazon S3 at periodic intervals. The company wants to identify the schema of the transactional dat a. The company also wants to perform transformations on the transactional data that is in Amazon S3.
The company wants to use a machine learning (ML) approach to detect fraud in the transformed data.
Which combination of solutions will meet these requirements with the LEAST operational overhead? {Select THREE.)
- A. Use Amazon Redshift ML to train a model to detect fraud.
- B. Use Amazon Redshift to store procedures to perform data transformations
- C. Use AWS Glue workflows and AWS Glue jobs to perform data transformations.
- D. Use Amazon Athena to scan the data and identify the schema.
- E. Use AWS Glue crawlers to scan the data and identify the schema.
- F. Use Amazon Fraud Detector to train a model to detect fraud.
Answer: C,E,F
Explanation:
To meet the requirements with the least operational overhead, the company should use AWS Glue crawlers, AWS Glue workflows and jobs, and Amazon Fraud Detector. AWS Glue crawlers can scan the data in Amazon S3 and identify the schema, which is then stored in the AWS Glue Data Catalog. AWS Glue workflows and jobs can perform data transformations on the data in Amazon S3 using serverless Spark or Python scripts. Amazon Fraud Detector can train a model to detect fraud using the transformed data and the company's historical fraud labels, and then generate fraud predictions using a simple API call.
Option A is incorrect because Amazon Athena is a serverless query service that can analyze data in Amazon S3 using standard SQL, but it does not perform data transformations or fraud detection.
Option C is incorrect because Amazon Redshift is a cloud data warehouse that can store and query data using SQL, but it requires provisioning and managing clusters, which adds operational overhead. Moreover, Amazon Redshift does not provide a built-in fraud detection capability.
Option E is incorrect because Amazon Redshift ML is a feature that allows users to create, train, and deploy machine learning models using SQL commands in Amazon Redshift. However, using Amazon Redshift ML would require loading the data from Amazon S3 to Amazon Redshift, which adds complexity and cost. Also, Amazon Redshift ML does not support fraud detection as a use case.
References:
AWS Glue Crawlers
AWS Glue Workflows and Jobs
Amazon Fraud Detector
NEW QUESTION # 76
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?
- A. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
- B. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
- C. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
- D. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
Answer: A
Explanation:
Amazon SageMaker notebook instances are fully managed environments that provide an integrated Jupyter notebook interface for data exploration, analysis, and machine learning. Amazon SageMaker notebook instances are based on EC2 instances that run within AWS service accounts, not within customer accounts. This means that the ML Specialist cannot find the Amazon SageMaker notebook instance's EC2 instance or EBS volume within the VPC, as they are not visible or accessible to the customer. However, the ML Specialist can still take a snapshot of the EBS volume by using the Amazon SageMaker console or API. The ML Specialist can also use VPC interface endpoints to securely connect the Amazon SageMaker notebook instance to the resources within the VPC, such as Amazon S3 buckets, Amazon EFS file systems, or Amazon RDS databases
NEW QUESTION # 77
A Machine Learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar- sized datasets. The team's leaders need to accelerate the training process.
What can a Machine Learning Specialist do to address this concern?
- A. Use AWS Glue to transform the CSV dataset to the JSON format.
- B. Use Amazon Kinesis to stream the data to Amazon SageMaker.
- C. Use Amazon SageMaker Pipe mode.
- D. Use Amazon Machine Learning to train the models.
Answer: C
Explanation:
Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure
NEW QUESTION # 78
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 files in an Amazon EBS volume attached to an Amazon EC2 instance.
- B. Store datasets as global tables in Amazon DynamoDB.
- C. Store datasets as files in Amazon S3.
- D. Store datasets as tables in a multi-node Amazon Redshift cluster.
Answer: C
Explanation:
Explanation
The best storage scheme for this scenario is to store datasets as files in Amazon S3. Amazon S3 is a scalable, cost-effective, and durable object storage service that can store any amount and type of data. Amazon S3 also supports querying data using SQL with Amazon Athena, a serverless interactive query service that can analyze data directly in S3. This way, the Data Science team can easily explore and analyze their datasets without having to load them into a database or a compute instance.
The other options are not as suitable for this scenario because:
Storing datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance would limit the scalability and availability of the data, as EBS volumes are only accessible within a single availability zone and have a maximum size of 16 TiB. Also, EBS volumes are more expensive than S3 buckets and require provisioning and managing EC2 instances.
Storing datasets as tables in a multi-node Amazon Redshift cluster would incur higher costs and complexity than using S3 and Athena. Amazon Redshift is a data warehouse service that is optimized for analytical queries over structured or semi-structured data. However, it requires setting up and maintaining a cluster of nodes, loading data into tables, and choosing the right distribution and sort keys for optimal performance. Moreover, Amazon Redshift charges for both storage and compute, while S3 and Athena only charge for the amount of data stored and scanned, respectively.
Storing datasets as global tables in Amazon DynamoDB would not be feasible for large amounts of data, as DynamoDB is a key-value and document database service that is designed for fast and consistent performance at any scale. However, DynamoDB has a limit of 400 KB per item and 25 GB per partition key value, which may not be enough for storing large datasets. Also, DynamoDB does not support SQL queries natively, and would require using a service like Amazon EMR or AWS Glue to run SQL queries over DynamoDB data.
References:
Amazon S3 - Cloud Object Storage
Amazon Athena - Interactive SQL Queries for Data in Amazon S3
Amazon EBS - Amazon Elastic Block Store (EBS)
Amazon Redshift - Data Warehouse Solution - AWS
Amazon DynamoDB - NoSQL Cloud Database Service
NEW QUESTION # 79
A company uses camera images of the tops of items displayed on store shelves to determine which items were removed and which ones still remain. After several hours of data labeling, the company has a total of
1,000 hand-labeled images covering 10 distinct items. The training results were poor.
Which machine learning approach fulfills the company's long-term needs?
- A. Augment training data for each item using image variants like inversions and translations, build the model, and iterate.
- B. Attach different colored labels to each item, take the images again, and build the model
- C. Convert the images to grayscale and retrain the model
- D. Reduce the number of distinct items from 10 to 2, build the model, and iterate
Answer: A
Explanation:
Explanation
Data augmentation is a technique that can increase the size and diversity of the training data by applying various transformations to the original images, such as inversions, translations, rotations, scaling, cropping, flipping, and color variations. Data augmentation can help improve the performance and generalization of image classification models by reducing overfitting and introducing more variability to the data. Data augmentation is especially useful when the original data is limited or imbalanced, as in the case of the company's problem. By augmenting the training data for each item using image variants, the company can build a more robust and accurate model that can recognize the items on the store shelves from different angles, positions, and lighting conditions. The company can also iterate on the model by adding more data or fine-tuning the hyperparameters to achieve better results.
References:
Build high performing image classification models using Amazon SageMaker JumpStart The Effectiveness of Data Augmentation in Image Classification using Deep Learning Data augmentation for improving deep learning in image classification problem Class-Adaptive Data Augmentation for Image Classification
NEW QUESTION # 80
An Amazon SageMaker notebook instance is launched into Amazon VPC The SageMaker notebook references data contained in an Amazon S3 bucket in another account The bucket is encrypted using SSE-KMS The instance returns an access denied error when trying to access data in Amazon S3.
Which of the following are required to access the bucket and avoid the access denied error? (Select THREE )
- A. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.
- B. An AWS KMS key policy that allows access to the customer master key (CMK)
- C. An 1AM role that allows access to the specific S3 bucket
- D. A permissive S3 bucket policy
- E. A SageMaker notebook security group that allows access to Amazon S3
- F. An S3 bucket owner that matches the notebook owner
Answer: A,B,C
NEW QUESTION # 81
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. Normalize all words by making the sentence lowercase
- B. Remove stop words using an English stopword dictionary.
- C. Correct the typography on "quck" to "quick."
- D. Perform part-of-speech tagging and keep the action verb and the nouns only
- E. One-hot encode all words in the sentence
- F. Tokenize the sentence into words.
Answer: C,D,E
NEW QUESTION # 82
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. Convolutional Neural Network - Quantile Regression (CNN-QR)
- B. Exponential Smoothing (ETS)
- C. Autoregressive Integrated Moving Average (AIRMA)
- D. Prophet
Answer: B
NEW QUESTION # 83
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 to serialize 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: D
NEW QUESTION # 84
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?
- A. Perform one-hot encoding on highly correlated features
- B. Apply the Pearson correlation coefficient
- C. Create a new feature space using principal component analysis (PCA)
- D. Use matrix multiplication on highly correlated features.
Answer: C
Explanation:
Principal component analysis (PCA) is an unsupervised machine learning algorithm that attempts to reduce the dimensionality (number of features) within a dataset while still retaining as much information as possible. This is done by finding a new set of features called components, which are composites of the original features that are uncorrelated with one another. They are also constrained so that the first component accounts for the largest possible variability in the data, the second component the second most variability, and so on. By using PCA, the impact of having a large number of features that are highly correlated with each other can be reduced, as the new feature space will have fewer dimensions and less redundancy. This can make the linear models more stable and less prone to overfitting. References:
Principal Component Analysis (PCA) Algorithm - Amazon SageMaker
Perform a large-scale principal component analysis faster using Amazon SageMaker | AWS Machine Learning Blog Machine Learning- Prinicipal Component Analysis | i2tutorials
NEW QUESTION # 85
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. Delete observations that contain missing values because these represent less than 5% of the data.
- B. For each feature, approximate the missing values using supervised learning based on other features.
- C. Replace each missing value by the mean or median across non-missing values in same row.
- D. Replace each missing value by the mean or median across non-missing values in the same column.
Answer: B
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 # 86
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non- fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist has been asked to reduce the number of false negatives.
Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Choose two.)
- A. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
- B. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
- C. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
- D. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
- E. Change the XGBoost eval_metric parameter to optimize based on AUC instead of error.
Answer: D,E
NEW QUESTION # 87
A company is interested in building a fraud detection model. Currently, the Data Scientist does not have a sufficient amount of information due to the low number of fraud cases.
Which method is MOST likely to detect the GREATEST number of valid fraud cases?
- A. Oversampling using SMOTE
- B. Class weight adjustment
- C. Oversampling using bootstrapping
- D. Undersampling
Answer: A
Explanation:
With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE) adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario. Refer to Section 4.2 at this link for supporting informatio
NEW QUESTION # 88
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