Question 1

A manufacturing company needs to identify returned smartphones that have been damaged by moisture. The company has an automated process that produces 2,000 diagnostic values for each phone. The database contains more than five million phone evaluations. The evaluation process is consistent, and there are no missing values in the data. A machine learning (ML) specialist has trained an Amazon SageMaker linear learner ML model to classify phones as moisture damaged or not moisture damaged by using all available features. The model's F1 score is 0.6. Which changes in model training would MOST likely improve the model's F1 score? (Choose two.)

Options :

A :

Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the SageMaker principal component analysis (PCA) algorithm

B :

Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the scikit-learn multi-dimensional scaling (MDS) algorithm

C : Continue to use the SageMaker linear learner algorithm. Set the predictor type to regressor.

D : Use the SageMaker k-means algorithm with k of less than 1,000 to train the model

E :

Use the SageMaker k-nearest neighbors (k-NN) algorithm. Set a dimension reduction target of less than 1,000 to train the model.

Answer: A,E

Question 2

A data engineer is using AWS Glue to create optimized, secure datasets in Amazon S3. The data science team wants the ability to access the ETL scripts directly from Amazon SageMaker notebooks within a VPC. After this setup is complete, the data science team wants the ability to run the AWS Glue job and invoke the SageMaker training job. Which combination of steps should the data engineer take to meet these requirements? (Choose three.)

Options :

A : Create a SageMaker development endpoint in the data science team-s VPC.

B : Create an AWS Glue development endpoint in the data science team-s VPC

C : Create SageMaker notebooks by using the AWS Glue development endpoint.

D : Create SageMaker notebooks by using the SageMaker console

E : Attach a decryption policy to the SageMaker notebooks.

F : Create an IAM policy and an IAM role for the SageMaker notebooks.

Answer: A,D,F

Question 3

A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs

at regular time intervals and uses PySpark to combine and format multiple large data sources into a single

consolidated output for downstream processing.

The Data Scientist has been given the following requirements to the cloud solution:

Combine multiple data sources.

Reuse existing PySpark logic.

Run the solution on the existing schedule.

Minimize the number of servers that will need to be managed.

Which architecture should the Data Scientist use to build this solution?

Options :

A : Write the raw data to Amazon S3. Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule. Use the existing PySpark logic to run the ETL job on the EMR cluster. Output the results to a “processed” location in Amazon S3 that is accessible for downstream use

B : Write the raw data to Amazon S3. Create an AWS Glue ETL job to perform the ETL processing against the input data. Write the ETL job in PySpark to leverage the existing logic. Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule. Configure the output target of the ETL job to write to a “processed” location in Amazon S3 that is accessible for downstream use.

C : Write the raw data to Amazon S3. Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3. Write the Lambda logic in Python and implement the existing PySpark logic to perform the ETL process. Have the Lambda function output the results to a “processed” location in Amazon S3 that is accessible for downstream use.

D : Use Amazon Kinesis Data Analytics to stream the input data and perform real-time SQL queries against the stream to carry out the required transformations within the stream. Deliver the output results to a “processed” location in Amazon S3 that is accessible for downstream use.

Answer: D

Question 4

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?

Options :

A : A k-fold cross-validation strategy with k=5

B : A stratified k-fold cross-validation strategy with k=5

C : A k-fold cross-validation strategy with k=5 and 3 repeats

D : An 80/20 stratified split between training and validation

Answer: B

Question 5

A retail company collects customer comments about its products from social media, the company website, and customer call logs. A team of data scientists and engineers wants to find common topics and determine which products the customers are referring to in their comments. The team is using natural language processing (NLP) to build a model to help with this classification. Each product can be classified into multiple categories that the company defines. These categories are related but are not mutually exclusive. For example, if there is mention of "Sample Yogurt" in the document of customer comments, then "Sample Yogurt" should be classified as "yogurt," "snack," and "dairy product." The team is using Amazon Comprehend to train the model and must complete the project as soon as possible. Which functionality of Amazon Comprehend should the team use to meet these requirements?

Options :

A : Custom classification with multi-class mode

B : Custom classification with multi-label mode

C : Custom entity recognition

D : Built-in models

Answer: B

Higher Test Marks with Free Online MLS-C01 Exam Practice

Buying Options: