Updated Dec-2021 Exam MLS-C01 Dumps - Pass Your Certification Exam
Latest Real Amazon MLS-C01 Exam Dumps Questions
Recommended Experience
Before registering for the AWS Certified Machine Learning – Specialty exam, the applicant should ensure meeting some prerequisites as stated by the vendor. First, working experience of 1-2 years in running ML workloads as well as their development and architecting on AWS cloud is a must for the candidate. Moreover, it is recommended to have practical skills in executing the hyperparameter optimization, deep learning and ML frameworks, and operational and model-training best practices for AWS Machine learning.
NEW QUESTION 18
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.
What should the Specialist do to initialize the model to re-train it with the custom data?
- A. Initialize the model with pre-trained weights in all layers including the last fully connected layer.
- B. Initialize the model with random weights in all layers and replace the last fully connected layer.
- C. Initialize the model with random weights in all layers including the last fully connected layer.
- D. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
Answer: D
NEW QUESTION 19
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. Mean absolute percentage error (MAPE)
- C. Misclassification rate
- D. Area Under the ROC Curve (AUC)
Answer: D
NEW QUESTION 20
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?
- A. Linear regression
- B. Clustering
- C. Classification
- D. Reinforcement learning
Answer: A
NEW QUESTION 21
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. 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.
- B. 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.
- 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. 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
Answer: C
NEW QUESTION 22
A gaming company has launched an online game where people can start playing for free, but they need to pay if they choose to use certain features. The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year. The company has gathered a labeled dataset from 1 million users.
The training dataset consists of 1,000 positive samples (from users who ended up paying within 1 year) and
999,000 negative samples (from users who did not use any paid features). Each data sample consists of 200 features including user age, device, location, and play patterns.
Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set. However, the prediction results on a test dataset were not satisfactory Which of the following approaches should the Data Science team take to mitigate this issue? (Choose two.)
- A. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
- B. Change the cost function so that false negatives have a higher impact on the cost value than false positives.
- C. Change the cost function so that false positives have a higher impact on the cost value than false negatives.
- D. Include a copy of the samples in the test dataset in the training dataset.
- E. Add more deep trees to the random forest to enable the model to learn more features.
Answer: A,B
NEW QUESTION 23
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?
- A. Produce a set of synonyms for every word using Amazon Mechanical Turk.
- B. Create one-hot word encoding vectors.
- C. Download word embeddings pre-trained on a large corpus.
- D. Create word embedding vectors that store edit distance with every other word.
Answer: B
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-object2vec-adds-new- features-that-support-automatic-negative-sampling-and-speed-up-training/
NEW QUESTION 24
A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data Which AWS service should the Data Scientist use?
- A. Amazon Athena
- B. Amazon QuickSight
- C. AWS Glue
- D. Amazon Redshift Spectrum
Answer: A
NEW QUESTION 25
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. K-means
- B. Logistic regression
- C. Linear regression
- D. Principal component analysis (PCA)
Answer: C
NEW QUESTION 26
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. An 1AM role that allows access to the specific S3 bucket
- B. A permissive S3 bucket policy
- C. A SageMaker notebook security group that allows access to Amazon S3
- D. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.
- E. An S3 bucket owner that matches the notebook owner
- F. An AWS KMS key policy that allows access to the customer master key (CMK)
Answer: A,D,F
NEW QUESTION 27
A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches What actions would allow the Specialist to get relevant numerical representations?
- A. Average colors by channel to obtain three-dimensional representations of images.
- B. Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels
- C. Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer
- D. Reduce image resolution and use reduced resolution pixel values as features
Answer: D
NEW QUESTION 28
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. 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.
- B. 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.
- 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. 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
Answer: C
NEW QUESTION 29
An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images Which of the following should be used to resolve this issue? (Select TWO)
- A. Perform data augmentation on the training data
- B. Add vanishing gradient to the model
- C. Use gradient checking in the model
- D. Make the neural network architecture complex.
- E. Add L2 regularization to the model
Answer: A,B
NEW QUESTION 30
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 using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.
- C. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue 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
NEW QUESTION 31
A Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors While exploring the data, the Specialist notices that the magnitude of the input features vary greatly The Specialist does not want variables with a larger magnitude to dominate the model What should the Specialist do to prepare the data for model training'?
- A. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude
- B. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution
- C. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude
- D. Apply the orthogonal sparse Diagram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
Answer: C
NEW QUESTION 32
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's hyperparameters should be periodically updated to prevent drift.
- C. The model should be periodically retrained using the original training data plus new data as product inventory changes.
- D. The model needs to be completely re-engineered because it is unable to handle product inventory changes.
Answer: C
NEW QUESTION 33
A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream.
As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.
Which next step is MOST likely to improve the data ingestion rate into Amazon S3?
- A. Increase the number of shards for the data stream.
- B. Decrease the retention period for the data stream.
- C. Add more consumers using the Kinesis Client Library (KCL).
- D. Increase the number of S3 prefixes for the delivery stream to write to.
Answer: A
Explanation:
Explanation/Reference:
NEW QUESTION 34
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 Amazon CloudWatch alarm to trigger an AWS Batch 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 Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
- C. 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.
- 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: C
NEW QUESTION 35
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Salary for AWS Certified Machine Learning Specialty Exam certified
- England: 82,930 Pound
- India: 712,503 INR
- Europe: 97,902 Euro
- United States: 107,786 USD
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