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

• Question 1:

  You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?

  A. Increase the instance memory to 512 GB, and increase the batch size.

  B. Replace the NVIDIA P100 GPU with a K80 GPU in the training job.

  C. Enable early stopping in your Vertex AI Training job.

  D. Use the tf.distribute.Strategy API and run a distributed training job.

  Correct Answer: D

• Question 2:

  You are developing a classification model to support predictions for your company's various products. The dataset you were given for model development has class imbalance You need to minimize false positives and false negatives What evaluation metric should you use to properly train the model?

  A. F1 score

  B. Recall

  C. Accuracy

  D. Precision

  Correct Answer: A

• Question 3:

  You are developing an ML model using a dataset with categorical input variables. You have randomly split half of the data into training and test sets. After applying one-hot encoding on the categorical variables in the training set, you discover that one categorical variable is missing from the test set. What should you do?

  A. Use sparse representation in the test set.

  B. Randomly redistribute the data, with 70% for the training set and 30% for the test set

  C. Apply one-hot encoding on the categorical variables in the test data

  D. Collect more data representing all categories

  Correct Answer: C

• Question 4:

  You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?

  A. Modify the target variable using the Box-Cox transformation.

  B. Z-normalize all the numeric features.

  C. Oversample the fraudulent transaction 10 times.

  D. Log transform all numeric features.

  Correct Answer: C

  https://towardsdatascience.com/how-to-build-a-machine-learning-model-to-identify-credit-card-fraud-in-5-stepsa-hands-on-modeling-5140b3bd19f1

• Question 5:

  While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?

  A. Include the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.

  B. Move the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.

  C. Migrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.

  D. Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.

  Correct Answer: A

  https://blog.tensorflow.org/2020/03/tensorflow-extended-tfx-using-apache-beam-large-scale-data-processing.html

• Question 6:

  You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company's logo. In the dataset, 96% of examples don't have the logo, so the dataset is very skewed. Which metric would give you the most confidence in your model?

  A. Precision

  B. Recall

  C. RMSE

  D. F1 score

  Correct Answer: D

  https://stephenallwright.com/imbalanced-data-metric/

• Question 7:

  You recently deployed an ML model. Three months after deployment, you notice that your model is underperforming on certain subgroups, thus potentially leading to biased results. You suspect that the inequitable performance is due to class imbalances in the training data, but you cannot collect more data. What should you do? (Choose two.)

  A. Remove training examples of high-performing subgroups, and retrain the model.

  B. Add an additional objective to penalize the model more for errors made on the minority class, and retrain the model

  C. Remove the features that have the highest correlations with the majority class.

  D. Upsample or reweight your existing training data, and retrain the model

  E. Redeploy the model, and provide a label explaining the model's behavior to users.

  Correct Answer: BD

  https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/

• Question 8:

  You are training an ML model using data stored in BigQuery that contains several values that are considered Personally Identifiable Information (PII). You need to reduce the sensitivity of the dataset before training your model. Every column is critical to your model. How should you proceed?

  A. Using Dataflow, ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column.

  B. Use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow with the DLP API to encrypt sensitive values with Format Preserving Encryption.

  C. Use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow to replace all sensitive data by using the encryption algorithm AES-256 with a salt.

  D. Before training, use BigQuery to select only the columns that do not contain sensitive data. Create an authorized view of the data so that sensitive values cannot be accessed by unauthorized individuals.

  Correct Answer: B

  https://cloud.google.com/dlp/docs/transformations-reference#types_of_de-identification_techniques https://cloud.google.com/dlp/docs/transformations-reference#crypto

• Question 9:

  You work on the data science team at a manufacturing company. You are reviewing the company's historical sales data, which has hundreds of millions of records. For your exploratory data analysis, you need to calculate descriptive statistics such as mean, median, and mode; conduct complex statistical tests for hypothesis testing; and plot variations of the features over time. You want to use as much of the sales data as possible in your analyses while minimizing computational resources. What should you do?

  A. Visualize the time plots in Google Data Studio. Import the dataset into Vertex Al Workbench user-managed notebooks. Use this data to calculate the descriptive statistics and run the statistical analyses.

  B. Spin up a Vertex Al Workbench user-managed notebooks instance and import the dataset. Use this data to create statistical and visual analyses.

  C. Use BigQuery to calculate the descriptive statistics. Use Vertex Al Workbench user-managed notebooks to visualize the time plots and run the statistical analyses.

  D. Use BigQuery to calculate the descriptive statistics, and use Google Data Studio to visualize the time plots. Use Vertex Al Workbench user-managed notebooks to run the statistical analyses.

  Correct Answer: C

  https://cloud.google.com/architecture/data-science-with-r-on-gcp-eda#ai_platform_notebooks https://cloud.google.com/vertex-ai-workbench#section-5

• Question 10:

  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 Vertex Al Pipelines to execute the experiments. Query the results stored in MetadataStore using the Vertex Al API.

  B. Use Vertex Al Training to execute the experiments. Write the accuracy metrics to BigQuery, and query the results using the BigQuery API.

  C. Use Vertex Al Training to execute the experiments. Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.

  D. Use Vertex Al Workbench user-managed notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API.

  Correct Answer: A

  The Vertex AI Pipelines provide a powerful tool for automating machine learning workflows, including data preparation, training, and deployment. MetadataStore can be used to track the performance of different models by logging accuracy metrics and other important information. The Vertex AI API can then be used to query the metadata store and retrieve the results of different experiments.