PMLE practice questions 171–180

1. Question 171 · 364 in bank

   You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?

   • AStore features in Bigtable as key/value data.
   • BStore features in Vertex AI Feature Store.
   • CStore features as a Vertex AI dataset, and use those features to train the models hosted in Vertex AI endpoints.
   • DStore features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.

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   Answer: B

   Explanation

   My Answer: B Vertex AI Feature Store because of these: “must retrieve the features with low latency” ,“retrieve historical data at a specific point in time”, and “ store the features with minimal effort”

   Source: Adapted from community discussion — guilhermebutzke

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2. Question 172 · 364 in bank

   You are working on a prototype of a text classification model in a managed Vertex AI Workbench notebook. You want to quickly experiment with tokenizing text by using a Natural Language Toolkit (NLTK) library. How should you add the library to your Jupyter kernel?

   • AInstall the NLTK library from a terminal by using the pip install nltk command.
   • BWrite a custom Dataflow job that uses NLTK to tokenize your text and saves the output to Cloud Storage.
   • CCreate a new Vertex AI Workbench notebook with a custom image that includes the NLTK library.
   • DInstall the NLTK library from a Jupyter cell by using the !pip install nltk --user command.

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   Answer: D

   Explanation

   Direct Installation: It installs the library directly within the notebook environment, making it immediately available for use. Simplicity: It requires a single command in a Jupyter cell, eliminating the need for external tools or configuration. User-Specific Installation: The --user flag ensures the library is installed in your user space, avoiding conflicts with system-wide packages.

   Source: Adapted from community discussion — pikachu007

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3. Question 173 · 364 in bank

   You have recently used TensorFlow to train a classification model on tabular data. You have created a Dataflow pipeline that can transform several terabytes of data into training or prediction datasets consisting of TFRecords. You now need to productionize the model, and you want the predictions to be automatically uploaded to a BigQuery table on a weekly schedule. What should you do?

   • AImport the model into Vertex AI and deploy it to a Vertex AI endpoint. On Vertex AI Pipelines, create a pipeline that uses the DataflowPythonJobOp and the ModelBacthPredictOp components.
   • BImport the model into Vertex AI and deploy it to a Vertex AI endpoint. Create a Dataflow pipeline that reuses the data processing logic sends requests to the endpoint, and then uploads predictions to a BigQuery table.
   • CImport the model into Vertex AI. On Vertex AI Pipelines, create a pipeline that uses the DataflowPvthonJobOp and the ModelBatchPredictOp components.
   • DImport the model into BigQuery. Implement the data processing logic in a SQL query. On Vertex AI Pipelines create a pipeline that uses the BigquervQueryJobOp and the BigqueryPredictModelJobOp components.

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   Answer: C

   Explanation

   The DataflowPythonJobOp operator lets you create a Vertex AI Pipelines component that prepares data by submitting a Python-based Apache Beam job to Dataflow for execution. https://cloud.google.com/vertex-ai/docs/pipelines/dataflow-component#dataflowpythonjobop Using we can specify an output location for Vertex AI to store predictions results https://cloud.google.com/vertex-ai/docs/pipelines/batchprediction-component A - is incorrect since we dont need an endpoint for batch predictions B - creating a new Dataflow pipeline is redundant

   Source: Adapted from community discussion — BlehMaks

   References

   • Google Cloud documentation
   • Google Cloud documentation

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4. Question 174 · 364 in bank

   You work for an online grocery store. You recently developed a custom ML model that recommends a recipe when a user arrives at the website. You chose the machine type on the Vertex AI endpoint to optimize costs by using the queries per second (QPS) that the model can serve, and you deployed it on a single machine with 8 vCPUs and no accelerators. A holiday season is approaching and you anticipate four times more traffic during this time than the typical daily traffic. You need to ensure that the model can scale efficiently to the increased demand. What should you do?

   • A1. Maintain the same machine type on the endpoint. 2. Set up a monitoring job and an alert for CPU usage. 3. If you receive an alert, add a compute node to the endpoint.
   • B1. Change the machine type on the endpoint to have 32 vCPUs. 2. Set up a monitoring job and an alert for CPU usage. 3. If you receive an alert, scale the vCPUs further as needed.
   • C1. Maintain the same machine type on the endpoint Configure the endpoint to enable autoscaling based on vCPU usage. 2. Set up a monitoring job and an alert for CPU usage. 3. If you receive an alert, investigate the cause.
   • D1. Change the machine type on the endpoint to have a GPU. Configure the endpoint to enable autoscaling based on the GPU usage. 2. Set up a monitoring job and an alert for GPU usage. 3. If you receive an alert, investigate the cause.

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   Answer: C

   Explanation

   Option A: Manually adding compute nodes after an alert might lead to delays and potential outages during peak traffic. Option B: Upgrading to 32 vCPUs upfront might be an overkill if the current machine type with 8 vCPUs can handle the typical daily traffic. Vertical scaling (more vCPUs) might be suitable only if the model can benefit from additional CPU power. Option D: Using a GPU is unlikely to benefit a recipe recommendation model, which likely doesn't involve intensive graphical processing. Additionally, monitoring GPU usage wouldn't be relevant.

   Source: Adapted from community discussion — fitri001

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5. Question 175 · 364 in bank

   You recently trained an XGBoost model on tabular data. You plan to expose the model for internal use as an HTTP microservice. After deployment, you expect a small number of incoming requests. You want to productionize the model with the least amount of effort and latency. What should you do?

   • ADeploy the model to BigQuery ML by using CREATE MODEL with the BOOSTED_TREE_REGRESSOR statement, and invoke the BigQuery API from the microservice.
   • BBuild a Flask-based app. Package the app in a custom container on Vertex AI, and deploy it to Vertex AI Endpoints.
   • CBuild a Flask-based app. Package the app in a Docker image, and deploy it to Google Kubernetes Engine in Autopilot mode.
   • DUse a prebuilt XGBoost Vertex container to create a model, and deploy it to Vertex AI Endpoints.

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   Answer: D

   Explanation

   Prebuilt Container: It eliminates the need to build and manage a custom container, reducing development time and complexity. Vertex AI Endpoints: It provides a managed serving infrastructure with low latency and high availability, optimizing performance for predictions. Minimal Effort: It involves simple steps of creating a Vertex model and deploying it to an endpoint, streamlining the process.

   Source: Adapted from community discussion — pikachu007

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6. Question 176 · 364 in bank

   You work for an international manufacturing organization that ships scientific products all over the world. Instruction manuals for these products need to be translated to 15 different languages. Your organization’s leadership team wants to start using machine learning to reduce the cost of manual human translations and increase translation speed. You need to implement a scalable solution that maximizes accuracy and minimizes operational overhead. You also want to include a process to evaluate and fix incorrect translations. What should you do?

   • ACreate a workflow using Cloud Function triggers. Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket. Configure another Cloud Function that translates the documents using the Cloud Translation API, and saves the translations to an output Cloud Storage bucket. Use human reviewers to evaluate the incorrect translations.
   • BCreate a Vertex AI pipeline that processes the documents launches, an AutoML Translation training job, evaluates the translations and deploys the model to a Vertex AI endpoint with autoscaling and model monitoring. When there is a predetermined skew between training and live data, re-trigger the pipeline with the latest data.
   • CUse AutoML Translation to train a model. Configure a Translation Hub project, and use the trained model to translate the documents. Use human reviewers to evaluate the incorrect translations.
   • DUse Vertex AI custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data. Deploy the model to a Vertex AI endpoint with autoscaling and model monitoring. When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.

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   Answer: C

   Explanation

   The answer is C, to use Translation Hub 1.Accuracy maximization: AutoML Translation uses machine learning to train a translation model on your specific data, which can lead to higher accuracy compared to generic translation models. 2.Minimal operational overhead: AutoML Translation handles the training and deployment of the translation model, reducing the need for manual intervention. 3.Evaluation and correction: The solution includes human reviewers to evaluate and correct any incorrect translations, ensuring high quality.

   Source: Adapted from community discussion — daidai75

   References

   • Google Cloud documentation

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7. Question 177 · 364 in bank

   You have developed an application that uses a chain of multiple scikit-learn models to predict the optimal price for your company’s products. The workflow logic is shown in the diagram. Members of your team use the individual models in other solution workflows. You want to deploy this workflow while ensuring version control for each individual model and the overall workflow. Your application needs to be able to scale down to zero. You want to minimize the compute resource utilization and the manual effort required to manage this solution. What should you do?

   

   • AExpose each individual model as an endpoint in Vertex AI Endpoints. Create a custom container endpoint to orchestrate the workflow.
   • BCreate a custom container endpoint for the workflow that loads each model’s individual files Track the versions of each individual model in BigQuery.
   • CExpose each individual model as an endpoint in Vertex AI Endpoints. Use Cloud Run to orchestrate the workflow.
   • DLoad each model’s individual files into Cloud Run. Use Cloud Run to orchestrate the workflow. Track the versions of each individual model in BigQuery.

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   Answer: C

   Explanation

   Option A: A custom container endpoint for orchestration adds complexity and management overhead. Option B: Loading model files directly into a custom container endpoint can lead to versioning challenges and potential conflicts if models are shared across workflows. Option D: Using BigQuery for model versioning is not its primary function and might introduce complexities in model loading and management.

   Source: Adapted from community discussion — pikachu007

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8. Question 178 · 364 in bank

   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?

   • ADataflow
   • BDataprep
   • CApache Flink
   • DCloud Data Fusion

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   Answer: D

   Explanation

   D is correct Visual point-and-click interface enabling code-free deployment of ETL/ELT data pipelines and Operate high-volumes of data pipelines periodically source: https://cloud.google.com/data-fusion#all-features

   Source: Adapted from community discussion — mousseUwU

   References

   • Google Cloud documentation
   • Google Cloud documentation

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9. Question 179 · 364 in bank

   You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine part failed. You recently started experimenting with a few different preprocessing and modeling approaches in a Vertex AI Workbench notebook. You want to log data and track artifacts from each run. How should you set up your experiments?

   • A1. Use the Vertex AI SDK to create an experiment and set up Vertex ML Metadata. 2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_merrics function to log loss values.
   • B1. Use the Vertex AI SDK to create an experiment and set up Vertex ML Metadata. 2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_metrics function to log loss values.
   • C1. Create a Vertex AI TensorBoard instance and use the Vertex AI SDK to create an experiment and associate the TensorBoard instance. 2. Use the assign_input_artifact method to track the preprocessed data and use the log_time_series_metrics function to log loss values.
   • D1. Create a Vertex AI TensorBoard instance, and use the Vertex AI SDK to create an experiment and associate the TensorBoard instance. 2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_metrics function to log loss values.

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   Answer: C

   Explanation

   C. Tensorboard for experimentation and comparison of different model runs. assign_input_artifacts to track preprocessed data, since it links artifacts as inputs to the execution. https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.Execution#google_cloud_aiplatform_Execution_assign_input_artifacts Using log_time_series_metrics would make sense if what we were doing is logging a metric, which we aren't when we track the preprocessed data not yet ran by the model.

   Source: Adapted from community discussion — b1a8fae

   References

   • Google Cloud documentation
   • Google Cloud documentation

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10. Question 180 · 364 in bank

    You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?

    • ACreate a Vertex AI Workbench user-managed notebook using the default VM instance, and use the %%bigquerv magic commands in Jupyter to query the tables.
    • BCreate a Vertex AI Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.
    • CCreate a Vertex AI Workbench user-managed notebook on a Dataproc Hub, and use the %%bigquery magic commands in Jupyter to query the tables.
    • DCreate a Vertex AI Workbench managed notebook on a Dataproc cluster, and use the spark-bigquery-connector to access the tables.

    Show answer

    Answer: B

    Explanation

    "Managed notebooks are usually a good choice if you want to use a notebook for data exploration, analysis, modeling, or as part of an end-to-end data science workflow. Managed notebooks instances let you perform workflow-oriented tasks without leaving the JupyterLab interface. They also have many integrations and features for implementing your data science workflow." vs. "User-managed notebooks can be a good choice for users who require extensive customization or who need a lot of control over their environment." Seems more like the former -> B

    Source: Adapted from community discussion — b1a8fae

    References

    • Google Cloud documentation
    • Google Cloud documentation

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PMLE practice, question 171. 364 questions available.