Google ML Engineer

Question 1

What kinds of problem benefit from ML

Answer 1

• Identification (Classification)
• Prediction
• Grouping (Clustering)

Question 2

What problem does ML Solve

Answer 2

• Identification (Classification)
• Prediction
• Grouping (Clustering)

Question 3

What are technical success metrics

Answer 3

• Accuracy (Positive match/total)
• Precision (True Positive match / (False positive + True positive)
• Recall (True Positive match / (True positive + False negative)

Question 4

What are different types of model

Answer 4

• Decision Tree
• Deep Learning
• Regression

Question 5

What are the steps in ML ops pipeline?

Answer 5

• Data extraction
• Data validation
• Data preparation
• Model training
• Model evaluation
• Model validation

Question 6

Which of the following comes first in a machine-learning pipeline?
- Model evalution
- Data extraction
- Data preparation
- Model training

Answer 6

Data extraction

Question 7

Which of the following is not a kind of data preparation task?
- Addressing missing data
- Removing unwanted data
- Integrating data
- Evaluating model performance

Answer 7

Evaluating model performance

Question 8

Which one isn’t the target feature of the label dataset
- Attributes of instances
- Structured tables
- Images
- Random data

Answer 8

Random data

Question 9

What are target values of label dataset

Answer 9

• Class or category
• Value to predict

Question 10

What are the classification algorithms

Answer 10

• Logistic Regression
• Decision Trees (End of decision trees are classifications), e.g. Random trees
• Naive Bayes (It uses statistic and probability, it use to collate and coappear for classification)
• Neural Networks (Deep learning network)
• Nearest Neighbor (How near or far points are in space for classification)
• Ensemble methods (These are different methods)

Question 11

Is logistic regression a classification algorithm or regression algorithm

Answer 11

Classification algorithm

Question 12

Give a example of decision tree algorithm

Answer 12

Random forest

Question 13

Which algorithm is based on How near or far points are in space and what type of algorithm it is?

Answer 13

• Nearest Neighbor
• Classification algorithm

Question 14

Which are the three regression algorithm

Answer 14

• Linear Regression (Learn formula from series of values which represent the strait line and predict based on formula)
• Decision Tree Regression (Using the structure of decision trees)
• Polynomial Regression (Like to learn a function, but instead of the straight line, it could be a curve)

Question 15

You have data on the price of cars sold over the past two years. You have data on the sale price, age of the car, mileage, interior features, gas mileage, and several other features. You want to use this data to predict the sale prices of other cars. What kind of ML problem is this?
- Classification
- Regression
- Reinforcement learning
- Unsupervised learning

Answer 15

This is a regression problem because we are trying to predict a continuous value.

Question 16

Which of the following is a process risk to successfully deploying a machine learning model? (One choice)
- Insufficiently agreed upon objectives
- Insufficient data
- Biased data
- High F1 score

Answer 16

Insufficiently agreed upon objectives

Question 17

Which are the three Unsupervised Learning algorithm

Answer 17

• Do not use labeled data sets
• Clustering
  
  • K means Clustering
• Association rules
  
  • A priori algorithm
• Dimensionality reduction
  
  • Principle component analysis
  • Autoencoders (compression of data)

Question 18

Which of the following isn’t the use case for Unsupervised learning? (One choice)
- Grouping and segmentation
- Data compression
- Work in game like scenario
- Anomaly detection

Answer 18

Work in game like scenario. This is part of Reinforcement Learning instead of Unsupervised learning

Question 19

Where are the characteristic of Reinforcement Learning? (multiple choice)
- Agent makes a series of choices in an environment
- Environment provides positive or negative feedback
- Trial and error, learn from feedback
- Use in Dimensionality reduction

Answer 19

• Agent makes a series of choices in an environment
• Environment provides positive or negative feedback
• Trial and error, learn from feedback

Use in Dimensionality reduction is unsupervise learning problem

Question 20

Which one is security risk in ML Model Development? (One choice)
- Insufficient data
- Data quality issues (Data exploration, mis categorizing data, missing data)
- Biased data
- Data poisioning

Answer 20

Data poisoning (security risk) where the actor deliberately feeds data which is biased and has a missing data set to tweak the output of ML algorithm

Question 21

What Google Cloud service would you use to store 3 TB of raw data files in Parquet format that will be processed and then used for machine learning training?
- Cloud Storage
- Cloud SQL
- Bigtable
- Cloud Dataproc

Answer 21

Cloud Storage (storing objects such as raw data files.)

Question 22

What Vertex AI feature supports managed and user managed Jupyter Notebooks?
- Vertex AI Training
- Vertex AI FeatureStore
- Vertex AI Workbench
- Vertex AI Labeling

Answer 22

Vertex AI Workbench (support for Jupyter Notebooks.)

Question 23

You are building a deep learning network and need to preform large volumes of low precision calculations. What accelerator would you choose?
- TPU
- GPU
- Edge devices
- Kubernetes Pod

Answer 23

TPU

Question 24

Which of the following is not an example of sensitive data?
- Faces in an image
- Government issued ID number
- Notes in electronic patient records
- Tracking number for an e-commerce shipment

Answer 24

Tracking number for an e-commerce shipment

Question 25

What is a statistical object that describes central tendency and spread of values called?
- Mean
- Distribution
- Variance
- Mode

Answer 25

Distribution

Question 26

What are valid sources of data for use with Vertex AI Datasets?
- CSV files and BigQuery tables/views only
- CSV files and Parquet files only
- BigQuery tables/views only
- CSV files, BigQuery, and Bigtable

Answer 26

CSV files and BigQuery tables/views only

Question 27

When is deleting rows with missing data is not a reasonable option for handling missing data?
- When there are many rows with no missing data
- When a significant portion of datasets has missing values for some feature
- When the data is stored in Parquet format
- When the data is not in a relational database

Answer 27

When a significant portion of datasets has missing values for some feature

Question 28

What is the role of feature attributions in explaining predictions?
- Attribution prevents overfitting
- Attributions are a way of handling missing data
- Attributions are a measure of how much a feature contributes to a prediction
- Attributions are a type of data augmentation

Answer 28

Attributions are a type of data augmentation

Question 29

Which of the following are types of data augmentation used with images?
- Crop and flip
- Crop and synonym substitution
- Feature attribution
- Imputing data and Crop

Answer 29

Crop and flip

Question 30

What type of hyperparameter tuning algorithms use sequential processing and use the results from prior evaluations to inform evaluations of new hyperparameter values?
- Bayesin Search
- Random Search
- Grid Search
- Data Augmentation

Answer 30

Bayesian search updates priors or information known as the start of the evaluation

Question 31

When using distributed training in Vertex AI, what kind of node is responsible for communicating gradients between nodes?
- Worker Nodes
- Primary Replicas
- Reduction Server
- Backup Nodes

Answer 31

The purpose of the reduction server is to increase throughput by communicating gradients among worker nodes.

Question 32

You want to run TensorFlow models most efficiently in Google Cloud. What serving option would you choose?
- KubeFlow
- TensorFlow open source
- Optimized TensorFlow Runtime
- XGBoost

Answer 32

Optimized TensorFlow Runtime contains optimizations used internally at Google Cloud.

Question 33

You want to use Vertex AI monitoring to detect when newly arriving data is significantly different than recent production data. What metric would you use?
- Skew
- Drift
- Precision
- FI Score

Answer 33

Drift measures difference in distribution with recent production data.