Google PROFESSIONAL-MACHINE-LEARNING-ENGINEER Online Practice Questions and Exam Preparation

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

• Question 161:

  You are developing a model to help your company create more targeted online advertising campaigns. You need to create a dataset that you will use to train the model. You want to avoid creating or reinforcing unfair bias in the model. What should you do? (Choose two.)

  A. Include a comprehensive set of demographic features
  B. Include only the demographic groups that most frequently interact with advertisements
  C. Collect a random sample of production traffic to build the training dataset
  D. Collect a stratified sample of production traffic to build the training dataset
  E. Conduct fairness tests across sensitive categories and demographics on the trained model
  E. Conduct fairness tests across sensitive categories and demographics on the trained model

• Question 162:

  You have been given a dataset with sales predictions based on your company's marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the data. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks. You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?

  A. Use BigQuery ML to run several regression models, and analyze their performance.
  B. Read the data from BigQuery using Dataproc, and run several models using SparkML.
  C. Use Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics.
  D. Train a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.
  A. Use BigQuery ML to run several regression models, and analyze their performance.

• Question 163:

  You work for a biotech startup that is experimenting with deep learning ML models based on properties of biological organisms. Your team frequently works on early-stage experiments with new architectures of ML models, and writes custom TensorFlow ops in C++. You train your models on large datasets and large batch sizes. Your typical batch size has 1024 examples, and each example is about 1 MB in size. The average size of a network with all weights and embeddings is 20 GB. What hardware should you choose for your models?

  A. A cluster with 2 n1-highcpu-64 machines, each with 8 NVIDIA Tesla V100 GPUs (128 GB GPU memory in total), and a n1-highcpu-64 machine with 64 vCPUs and 58 GB RAM
  B. A cluster with 2 a2-megagpu-16g machines, each with 16 NVIDIA Tesla A100 GPUs (640 GB GPU memory in total), 96 vCPUs, and 1.4 TB RAM
  C. A cluster with an n1-highcpu-64 machine with a v2-8 TPU and 64 GB RAM
  D. A cluster with 4 n1-highcpu-96 machines, each with 96 vCPUs and 86 GB RAM
  D. A cluster with 4 n1-highcpu-96 machines, each with 96 vCPUs and 86 GB RAM

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  Explanation/Reference:

  https://cloud.google.com/tpu/docs/intro-to-tpu#cpus

• Question 164:

  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.
  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.

• Question 165:

  You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

  A. Use AI Platform for distributed training.
  B. Create a cluster on Dataproc for training.
  C. Create a Managed Instance Group with autoscaling.
  D. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
  A. Use AI Platform for distributed training.

• Question 166:

  You are implementing a batch inference ML pipeline in Google Cloud. The model was developed using TensorFlow and is stored in SavedModel format in Cloud Storage. You need to apply the model to a historical dataset containing 10 TB of data that is stored in a BigQuery table. How should you perform the inference?

  A. Export the historical data to Cloud Storage in Avro format. Configure a Vertex AI batch prediction job to generate predictions for the exported data
  B. Import the TensorFlow model by using the CREATE MODEL statement in BigQuery ML. Apply the historical data to the TensorFlow model
  C. Export the historical data to Cloud Storage in CSV format. Configure a Vertex AI batch prediction job to generate predictions for the exported data
  D. Configure a Vertex AI batch prediction job to apply the model to the historical data in BigQuery
  B. Import the TensorFlow model by using the CREATE MODEL statement in BigQuery ML. Apply the historical data to the TensorFlow model

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  Explanation/Reference:

• Question 167:

  You work for a startup that has multiple data science workloads. Your compute infrastructure is currently on-premises, and the data science workloads are native to PySpark. Your team plans to migrate their data science workloads to Google Cloud. You need to build a proof of concept to migrate one data science job to Google Cloud. You want to propose a migration process that requires minimal cost and effort. What should you do first?

  A. Create a n2-standard-4 VM instance and install Java, Scala, and Apache Spark dependencies on it.
  B. Create a Google Kubernetes Engine cluster with a basic node pool configuration, install Java, Scala, and Apache Spark dependencies on it.
  C. Create a Standard (1 master, 3 workers) Dataproc cluster, and run a Vertex AI Workbench notebook instance on it.
  D. Create a Vertex AI Workbench notebook with instance type n2-standard-4.
  C. Create a Standard (1 master, 3 workers) Dataproc cluster, and run a Vertex AI Workbench notebook instance on it.

• Question 168:

  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)
  C. Recurrent Neural Networks (RNN)

• Question 169:

  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. Write your data in TFRecords.
  B. Z-normalize all the numeric features.
  C. Oversample the fraudulent transaction 10 times.
  D. Use one-hot encoding on all categorical features.
  C. Oversample the fraudulent transaction 10 times.

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  Explanation/Reference:

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

• Question 170:

  You work for a company that is developing an application to help users with meal planning. You want to use machine learning to scan a corpus of recipes and extract each ingredient (e.g., carrot, rice, pasta) and each kitchen cookware (e.g., bowl, pot, spoon) mentioned. Each recipe is saved in an unstructured text file. What should you do?

  A. Create a text dataset on Vertex AI for entity extraction Create two entities called “ingredient” and “cookware”, and label at least 200 examples of each entity. Train an AutoML entity extraction model to extract occurrences of these entity types. Evaluate performance on a holdout dataset.
  B. Create a multi-label text classification dataset on Vertex AI. Create a test dataset, and label each recipe that corresponds to its ingredients and cookware. Train a multi-class classification model. Evaluate the model's performance on a holdout dataset.
  C. Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe. Evaluate the model's performance on a prelabeled dataset.
  D. Create a text dataset on Vertex AI for entity extraction. Create as many entities as there are different ingredients and cookware. Train an AutoML entity extraction model to extract those entities. Evaluate the model's performance on a holdout dataset.
  A. Create a text dataset on Vertex AI for entity extraction Create two entities called “ingredient” and “cookware”, and label at least 200 examples of each entity. Train an AutoML entity extraction model to extract occurrences of these entity types. Evaluate performance on a holdout dataset.

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  Explanation/Reference: