AWS-Certified-Machine-Learning-Specialty Free Practice Test

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?

1. A. Create one-hot word encoding vectors.
2. B. Produce a set of synonyms for every word using Amazon Mechanical Turk.
3. C. Create word embedding factors that store edit distance with every other word.
4. D. Download word embedding’s pre-trained on a large corpus.

Correct Answer: D

A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.4xlarge notebook instance.
The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget. Which solution will result in the MOST cost savings?

1. A. Change the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance ha
2. B. Stop the notebook when it is not in us
3. C. Run both data preprocessing and feature engineering development on that instance.
4. D. Keep the notebook instance type and size the sam
5. E. Stop the notebook when it is not in us
6. F. Run data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
7. G. Change the notebook instance type to a smaller general purpose instanc
8. H. Stop the notebook when it is not in us
9. I. Run data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
10. J. Change the notebook instance type to a smaller general purpose instanc
11. K. Stop the notebook when it is not in us
12. L. Run data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option.

Correct Answer: B

A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model
What should the Specialist do to address the performance issues with the current solution?

1. A. Use the SageMaker batch transform feature
2. B. Compress the training data into Apache Parquet format.
3. C. Ensure that the input mode for the training job is set to Pipe.
4. D. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.

Correct Answer: B

An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored for offline analysis.
What approach would be the MOST effective to perform near-real time defect detection?

1. A. Use AWS IoT Analytics for ingestion, storage, and further analysi
2. B. Use Jupyter notebooks from within AWS IoT Analytics to carry out analysis for anomalies.
3. C. Use Amazon S3 for ingestion, storage, and further analysi
4. D. Use an Amazon EMR cluster to carry out Apache Spark ML k-means clustering to determine anomalies.
5. E. Use Amazon S3 for ingestion, storage, and further analysi
6. F. Use the Amazon SageMaker Random Cut Forest (RCF) algorithm to determine anomalies.
7. G. Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detectio
8. H. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.

Correct Answer: B

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?

1. A. Drop all records from the dataset where age has been set to 0.
2. B. Replace the age field value for records with a value of 0 with the mean or median value from the dataset.
3. C. Drop the age feature from the dataset and train the model using the rest of the features.
4. D. Use k-means clustering to handle missing features.

Correct Answer: A