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You are building a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

  • A. Create a new view with BigQuery that does not include a column with city information
  • B. Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
  • C. Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.
  • D. Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.

Answer: C

Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?

  • A. Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
  • B. Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.
  • C. Use Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
  • D. Use Al Platform Notebooks to execute the experiment
  • E. Collect the results in a shared Google Sheetsfile, and query the results using the Google Sheets API

Answer: A

You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?

  • A. AutoML Vision model
  • B. AutoML Vision Edge mobile-versatile-1 model
  • C. AutoML Vision Edge mobile-low-latency-1 model
  • D. AutoML Vision Edge mobile-high-accuracy-1 model

Answer: A

You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

  • A. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery
  • B. Convert your PySpark into SparkSQL queries to transform the data and then run your pipeline on Dataproc to write the data into BigQuery.
  • C. Ingest your data into Cloud SQL convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning
  • D. Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table

Answer: B

You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?

  • A. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
  • B. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
  • C. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucke
  • D. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
  • E. Use Cloud Scheduler to schedule jobs at a regular interva
  • F. For the first step of the jo
  • G. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.

Answer: A

You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?

  • A. Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.
  • B. Write a Python script that uses the BigQuery API to execute queries against BigQuery Execute this script as the first step in your Kubeflow pipeline
  • C. Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries
  • D. Locate the Kubeflow Pipelines repository on GitHub Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipelin
  • E. Use the component to execute queries against BigQuery

Answer: A

You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook. What should you do?

  • A. Use Al Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe
  • B. Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance
  • C. Download your table from BigQuery as a local CSV file, and upload it to your Al Platform notebook instance Use panda
  • D. read_csv to ingest the file as a pandas dataframe
  • E. From a bash cell in your Al Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutii cp to copy the data into the notebook Use panda
  • F. read_csv to ingest the file as a pandas dataframe

Answer: B

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 models 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)

Answer: B

During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?

  • A. Increase the size of the training batch
  • B. Decrease the size of the training batch
  • C. Increase the learning rate hyperparameter
  • D. Decrease the learning rate hyperparameter

Answer: C

You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

  • A. Export the model to BigQuery ML.
  • B. Deploy and version the model on Al Platform.
  • C. Use Dataflow with the SavedModel to read the data from BigQuery
  • D. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.

Answer: A

You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?

  • A. Redaction, reproducibility, and explainability
  • B. Traceability, reproducibility, and explainability
  • C. Federated learning, reproducibility, and explainability
  • D. Differential privacy federated learning, and explainability

Answer: B

You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers

  • A. Decrease the number of parallel trials
  • B. Decrease the range of floating-point values
  • C. Set the early stopping parameter to TRUE
  • D. Change the search algorithm from Bayesian search to random search.
  • E. Decrease the maximum number of trials during subsequent training phases.

Answer: DE

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?

  • A. Set up restrictive I AM permissions on the Al 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 categorie
  • D. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources
  • E. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about Al Platform resource usage In BigQuery create a SQL view that maps users to the resources they are using.

Answer: B

You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

  • A. Ensure that training is reproducible
  • B. Ensure that all hyperparameters are tuned
  • C. Ensure that model performance is monitored
  • D. Ensure that feature expectations are captured in the schema

Answer: B

You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set. What should you do?

  • A. Keep the original test dataset unchanged even if newer products are incorporated into retraining
  • B. Extend your test dataset with images of the newer products when they are introduced to retraining
  • C. Replace your test dataset with images of the newer products when they are introduced to retraining.
  • D. Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.

Answer: C

You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano. Scikit-team, and custom libraries. What should you do?

  • A. Use the Al Platform custom containers feature to receive training jobs using any framework
  • B. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob
  • C. Create a library of VM images on Compute Engine; and publish these images on a centralized repository
  • D. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

Answer: D


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