Question 1

For the given confusion matrix, what is the recall and precision of the model?

Options :

A : Recall = 0.92 Precision = 0.84

B : Recall = 0.84 Precision = 0.8

C : Recall = 0.92 Precision = 0.8

D : Recall = 0.8 Precision = 0.92

Answer: C

Question 2

A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.

Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

Options :

A : Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.

B : Drop the features with low correlation scores by using a Jupyter notebook.

C : Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.

D : Concatenate the features with high correlation scores by using a Jupyter notebook.

Answer: A

Question 3

A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing

rapidly and approximately 100 GB of new data is generated every day. The company wants to explore

machine learning uses cases while ensuring the data is only accessible to specific IAM users.

Which storage option provides the most processing flexibility and will allow access control with IAM?

Options :

A :

Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict

access to only the desired IAM users.

B :

Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket

policies.

C :

Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access

to the EMR instances using IAM policies.

D :

Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by

the IAM users.

Answer: B

Question 4

A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial dat

a. The company is worried about data egress and wants an ML engineer to secure the environment.

Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

Options :

A : Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.

B : Use SCPs to restrict access to SageMaker.

C : Disable root access on the SageMaker notebook instances.

D : Enable network isolation for training jobs and models.

E : Restrict notebook presigned URLs to specific IPs used by the company.

F : Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys.

Answer: A,D,F

Question 5

A manufacturing company stores production volume data in a PostgreSQL database.

The company needs an end-to-end solution that will give business analysts the ability to prepare data for

processing and to predict future production volume based the previous year's production volume. The solution

must not require the company to have coding knowledge.

Which solution will meet these requirements with the LEAST effort?

Options :

A :

Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Create an Amazon EMR cluster to read the S3 bucket and perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.

B :

Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.

C :

Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Use AWS Glue to read the data in the S3 bucket and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.

D :

Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.

Answer: B