Question 1

You work on a growing team of more than 50 data scientists who all use Al Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

Options :

A : Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.

B : Separate each data scientist-s work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.

C : Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.

D : Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using

Answer: C

Question 2

Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (PII) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?

Options :

A : 1= Dataflow, 2= BigQuery

B : 1 = Pub/Sub, 2= Datastore

C : 1 = Dataflow, 2 = Cloud SQL

D : 1 = Cloud Function, 2= Cloud SQL

Answer: A

Question 3

You developed an ML model with AI Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine

(GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?

Options :

A : Significantly increase the max_batch_size TensorFlow Serving parameter.

B : Switch to the tensorflow-model-server-universal version of TensorFlow Serving.

C : Significantly increase the max_enqueued_batches TensorFlow Serving parameter.

D : Recompile TensorFlow Serving using the source to support CPU-specific optimizations. Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes

Answer: D

Question 4

You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?

Options :

A : Create an object detection model that can localize the rust spots.

B : Develop an image segmentation ML model to locate the boundaries of the rust spots.

C : Develop a template matching algorithm using traditional computer vision libraries.

D : Develop an image classification ML model to predict the presence of the disease.

Answer: B

Question 5

You work at a leading healthcare firm developing state-of-the-art algorithms for various use cases You have unstructured textual data with custom labels You need to extract and classify various medical phrases with these labels What should you do?

Options :

A : Use the Healthcare Natural Language API to extract medical entities.

B : Use a BERT-based model to fine-tune a medical entity extraction model.

C : Use AutoML Entity Extraction to train a medical entity extraction model.

D : Use TensorFlow to build a custom medical entity extraction model.

Answer: B