Google Google Certifications PROFESSIONAL-MACHINE-LEARNING-ENGINEER Questions & Answers

• Question 131:

  You are responsible for building a unified analytics environment across a variety of on-premises data marts. Your company is experiencing data quality and security challenges when integrating data across the servers, caused by the use of a wide range of disconnected tools and temporary solutions. You need a fully managed, cloud-native data integration service that will lower the total cost of work and reduce repetitive work. Some members on your team prefer a codeless interface for building Extract, Transform, Load (ETL) process. Which service should you use?

  A. Dataflow

  B. Dataprep

  C. Apache Flink

  D. Cloud Data Fusion

  Correct Answer: D

  https://cloud.google.com/data-fusion

• Question 132:

  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?

  

  A. 1= Dataflow, 2= BigQuery

  B. 1 = Pub/Sub, 2= Datastore

  C. 1 = Dataflow, 2 = Cloud SQL

  D. 1 = Cloud Function, 2= Cloud SQL

  Correct Answer: A

  https://github.com/GoogleCloudPlatform/dataflow-contact-center-speech-analysis

• Question 133:

  You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

  A. Build a classification model

  B. Build a knowledge-based filtering model

  C. Build a collaborative-based filtering model

  D. Build a regression model using the features as predictors

  Correct Answer: C

  Reference: https://cloud.google.com/solutions/recommendations-using-machine-learning-on-compute-engine

• Question 134:

  You recently developed a deep learning model. To test your new model, you trained it for a few epochs on a large dataset. You observe that the training and validation losses barely changed during the training run. You want to quickly debug your model. What should you do first?

  A. Verify that your model can obtain a low loss on a small subset of the dataset

  B. Add handcrafted features to inject your domain knowledge into the model

  C. Use the Vertex AI hyperparameter tuning service to identify a better learning rate

  D. Use hardware accelerators and train your model for more epochs

  Correct Answer: A

  Verifying that your model can obtain a low loss on a small subset of the dataset is a good first step for debugging because it helps you determine if your model is capable of fitting the data and learning from it. If your model cannot fit a small subset of the data, it may indicate issues with the model architecture, initialization, or optimization algorithm. By starting with a small subset, you can identify and fix these issues more quickly, before moving on to larger-scale training and more complex debugging tasks.

• Question 135:

  You have deployed multiple versions of an image classification model on AI Platform. You want to monitor the performance of the model versions over time. How should you perform this comparison?

  A. Compare the loss performance for each model on a held-out dataset.

  B. Compare the loss performance for each model on the validation data.

  C. Compare the receiver operating characteristic (ROC) curve for each model using the What-If Tool.

  D. Compare the mean average precision across the models using the Continuous Evaluation feature.

  Correct Answer: D

  Compare the mean average precision across the models using the Continuous Evaluation feature

  https://cloud.google.com/vertex-ai/docs/evaluation/introduction

  Vertex AI provides model evaluation metrics, such as precision and recall, to help you determine the performance of your models...

  Vertex AI supports evaluation of the following model types:

  AuPRC: The area under the precision-recall (PR) curve, also referred to as average precision. This value ranges from zero to one, where a higher value indicates a higher-quality model.

• Question 136:

  You trained a text classification model. You have the following SignatureDefs:

  

  You started a TensorFlow-serving component server and tried to send an HTTP request to get a prediction using:

  headers = {"content-type": "application/json"}

  json_response = requests.post('http: //localhost:8501/v1/models/text_model:predict', data=data, headers=headers)

  What is the correct way to write the predict request?

  A. data = json.dumps({"signature_name": "seving_default", "instances" [[`ab', `bc', `cd']]})

  B. data = json.dumps({"signature_name": "serving_default", "instances" [[`a', `b', `c', `d', `e', `f']]})

  C. data = json.dumps({"signature_name": "serving_default", "instances" [[`a', `b', `c'], [`d', `e', `f']]})

  D. data = json.dumps({"signature_name": "serving_default", "instances" [[`a', `b'], [`c', `d'], [`e', `f']]})

  Correct Answer: D

  A negative number in the shape enables auto expand (https://stackoverflow.com/questions/37956197/what-is-the-negative-index-in-shape-arrays-used-for-tensorflow).

  Then the first number -1 out of the shape (-1, 2) speaks the number of 1 dimensional arrays within the tensor (and it can autoexpand) while the second numer (2) sets the number of elements in the inner array at 2. Hence D.

• Question 137:

  You are training an LSTM-based model on AI Platform to summarize text using the following job submission script:

  gcloud ai-platform jobs submit training $JOB_NAME \

  --package-path $TRAINER_PACKAGE_PATH \

  --module-name $MAIN_TRAINER_MODULE \

  --job-dir $JOB_DIR \

  --region $REGION \

  --scale-tier basic \

  -- \

  --epochs 20 \

  --batch_size=32 \

  --learning_rate=0.001 \

  You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?

  A. Modify the `epochs' parameter.

  B. Modify the `scale-tier' parameter.

  C. Modify the `batch size' parameter.

  D. Modify the `learning rate' parameter.

  Correct Answer: B

  Changing the scale tier does not impact performance璷nly speeds up training time. Epochs, Batch size, and learning rate all are hyperparameters that might impact model accuracy.

• Question 138:

  You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your model's features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

  A. Classification

  B. Reinforcement Learning

  C. Recurrent Neural Networks (RNN)

  D. Convolutional Neural Networks (CNN)

  Correct Answer: C

• Question 139:

  You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (PII) to Google Cloud. You want to use the Cloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the PII is not accessible by unauthorized individuals?

  A. Stream all files to Google Cloud, and then write the data to BigQuery. Periodically conduct a bulk scan of the table using the DLP API.

  B. Stream all files to Google Cloud, and write batches of the data to BigQuery. While the data is being written to BigQuery, conduct a bulk scan of the data using the DLP API.

  C. Create two buckets of data: Sensitive and Non-sensitive. Write all data to the Non-sensitive bucket. Periodically conduct a bulk scan of that bucket using the DLP API, and move the sensitive data to the Sensitive bucket.

  D. Create three buckets of data: Quarantine, Sensitive, and Non-sensitive. Write all data to the Quarantine bucket. Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket.

  Correct Answer: D

  https://cloud.google.com/architecture/automating-classification-of-data-uploaded-to-cloud-storage#building_the_quarantine_and_classification_pipeline

• Question 140:

  You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?

  A. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run.

  B. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.

  C. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.

  D. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic.

  Correct Answer: B

  https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#cicd_architecture GCP recommends to use Cloud Build when building KubeFlow Pipelines. It's possible to run unit tests in Cloud Build. And, the others seems overly complex/unnecessary