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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q120-Q125):

NEW QUESTION # 120
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

• A. The validation channel identifying the location of validation data on an Amazon S3 bucket.
• B. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
• C. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
• D. Hyperparameters in a JSON array as documented for the algorithm used.
• E. The training channel identifying the location of training data on an Amazon S3 bucket.
• F. The output path specifying where on an Amazon S3 bucket the trained model will persist.

Answer: B,E,F
NEW QUESTION # 121
A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.
How can the data scientist meet this requirements?

• A. Call the UpdateNotebookInstanceLifecycleConfig API operation
• B. Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance
• C. Call the CreateNotebookInstanceLifecycleConfig API operation
• D. Stop and then restart the SageMaker notebook instance

Answer: D
NEW QUESTION # 122
A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last
24 hours of the data
Which type of data repository is the MOST cost-effective solution?

• A. An Amazon RDS database with hourly table partitions
• B. An Amazon EBS-backed Amazon EC2 instance with hourly directories
• C. An Amazon S3 data lake with hourly object prefixes
• D. An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes

Answer: C
NEW QUESTION # 123
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. Drop all records from the dataset where age has been set to 0.
• B. Use k-means clustering to handle missing 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 the age feature from the dataset and train the model using the rest of the features.

Answer: C
NEW QUESTION # 124
A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population.
Which cross-validation strategy should the Data Scientist adopt?

• A. A stratified k-fold cross-validation strategy with k=5
• B. An 80/20 stratified split between training and validation
• C. A k-fold cross-validation strategy with k=5 and 3 repeats
• D. A k-fold cross-validation strategy with k=5

Answer: A
NEW QUESTION # 125
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