PDE Preparing and Using Data for Analysis Practice Test 2 — 15 Questions

Question 1

A data scientist wants to import a pre-trained TensorFlow model into BigQuery ML for batch predictions. The model is stored in a Cloud Storage bucket. Which statement is correct?

Accepted Answer

Use CREATE MODEL with model_type='tensorflow' and model_path='gs://bucket/model'.. BigQuery ML supports importing TensorFlow models via CREATE MODEL with model_type='tensorflow' and model_path pointing to the SavedModel directory in Cloud Storage.

Answer

Use CREATE MODEL with model_type='imported_tensorflow' and model_path='gs://bucket/model'.

Answer

First upload the model to Vertex AI Model Registry, then reference it in BigQuery ML.

Answer

Use the ML.IMPORT_MODEL function to load the model into BigQuery.

Question 2

You need to track the lineage of data in BigQuery, showing how tables are derived from other tables via queries. Which service provides this capability?

Accepted Answer

BigQuery Lineage API. BigQuery lineage API and Dataplex lineage both provide data lineage tracking. BigQuery lineage is built-in, while Dataplex extends it across the data lake.

Answer

Cloud Composer

Answer

Cloud Data Catalog

Answer

Dataflow

Question 3

A data engineer needs to build a feature engineering pipeline using Vertex AI Pipelines. The pipeline should preprocess data, train a model, and deploy it. Which two components are required to define the pipeline? (Choose 2)

Accepted Answer

Kubeflow Pipelines SDK. Vertex AI Pipelines uses the Kubeflow Pipelines SDK (or TFX) to define components and compile them into a pipeline. The pipeline is then run on Vertex AI Pipelines.

Answer

Vertex AI Feature Store

Answer

Dataflow

Answer

Cloud Composer

Question 4

A company uses AutoML Tables to train a classification model. They want to improve model performance by engineering new features from existing timestamp columns. Which three techniques can they apply within AutoML Tables? (Choose 3)

Accepted Answer

Create a new column with the difference between two timestamps.. AutoML Tables automatically extracts features from timestamp columns, such as day of week, month, hour, etc. Users can also manually create new columns via the UI or by preprocessing data before import. However, manual SQL functions cannot be used directly within AutoML Tables.

Answer

Manually add a column with a boolean indicating if the timestamp falls on a weekend.

Answer

Apply a SQL UDF in the AutoML Tables training configuration.

Question 5

A data team wants to use Approximate Aggregation Functions in BigQuery to get faster query results. Which two functions can they use? (Choose 2)

Accepted Answer

APPROX_QUANTILES. BigQuery provides APPROX_COUNT_DISTINCT for approximate distinct counts and APPROX_QUANTILES for approximate quantiles. Other approximate functions include APPROX_TOP_COUNT and APPROX_TOP_SUM.

Answer

APPROX_SUM

Answer

APPROX_AVG

Answer

APPROX_MEDIAN

Question 6

A data engineer needs to create a BigQuery ML model for predicting customer churn using a dataset with 10 million rows and 50 features. The dataset is highly imbalanced (5% churn). Which approach should the engineer use to handle class imbalance during model training?

Accepted Answer

Use the CREATE MODEL statement with CLASS_WEIGHTS = {'0': 0.2, '1': 0.8}. BigQuery ML supports class weights for imbalanced datasets via the CLASS_WEIGHTS option in CREATE MODEL. This assigns higher weight to the minority class without generating synthetic data. SMOTE is not available in BigQuery ML. Undersampling the majority class would lose data, and oversampling with duplication could introduce bias.

Answer

Undersample the majority class before training

Answer

Use SMOTE via TRANSFORM clause in BigQuery ML

Answer

Oversample the minority class by duplicating rows

Question 7

A financial analytics team uses Looker to explore BigQuery data. They need to allow business users to filter by a custom date range that is not tied to an existing dimension. The date range must be user-input at query time. What is the best approach in Looker?

Accepted Answer

Create a parameter in LookML using Liquid templating. Looker uses Liquid templating in LookML to create parameters that render as filter controls at runtime. Users can input values that are then injected into the SQL. Creating a dimension with a yesno filter requires predefined values. The filter parameter on a dimension only allows selecting from existing values, not arbitrary input.

Answer

Create an explore with a custom filter field in the Looker UI

Answer

Use a filter parameter directly on the date dimension

Answer

Add a dimension with a yesno filter that toggles the date range

Question 8

A data scientist wants to train a custom TensorFlow model on Vertex AI using a managed Jupyter notebook. Which Vertex AI service should they use to set up a notebook environment with pre-installed deep learning frameworks?

Accepted Answer

Vertex AI Workbench. Vertex AI Workbench provides managed Jupyter notebooks with pre-installed deep learning frameworks (TensorFlow, PyTorch, etc.) and easy scaling options. Notebooks on Compute Engine would require manual setup. AI Platform Training is for training jobs, not interactive notebooks. Vertex AI Pipelines is for orchestrating ML workflows.

Answer

Compute Engine with Deep Learning VM

Answer

Vertex AI Training via custom job

Answer

Vertex AI Pipelines

Question 9

A retail company uses BigQuery to store sales data and wants to forecast weekly demand for the next 8 weeks using historical data from the past 2 years. They need to account for seasonality and holidays. Which BigQuery ML model type and configuration is most appropriate?

Accepted Answer

ARIMA_PLUS with holiday_region parameter. BigQuery ML's ARIMA_PLUS model is designed for time-series forecasting, automatically detecting seasonality and handling holiday effects via the holiday_region parameter. Linear regression would require manual feature engineering for time components. Time-series DECOMPOSE is not a model type. Boosted trees are not natively time-series aware without feature engineering.

Answer

Boosted tree classifier

Answer

Linear regression with engineered time features

Answer

Time-series DECOMPOSE model

Question 10

A data engineer needs to query data from BigQuery and another cloud provider's storage (AWS S3) using a single SQL query. The data must not be moved or copied to GCP. Which Google Cloud service should they use?

Accepted Answer

BigQuery Omni. BigQuery Omni allows querying data across multiple clouds (AWS S3, Azure Blob Storage) using BigQuery's interface without moving data. BigQuery Omni runs compute in the other cloud's region. BigQuery Transfer Service moves data into BigQuery. Dataplex is for data management, not cross-cloud queries. Cloud Storage Transfer Service is for moving data between clouds.

Answer

Cloud Storage Transfer Service

Answer

Dataplex

Answer

BigQuery Data Transfer Service

Question 11

What is the primary purpose of Vertex AI Feature Store?

Accepted Answer

To store and serve features for machine learning models at scale. Vertex AI Feature Store is a managed service for storing, serving, and sharing ML features. It supports both online (low-latency serving for prediction) and offline (batch serving for training) serving. It is not for model training, data transformation, or experiment tracking.

Answer

To manage and track ML experiments

Answer

To train machine learning models using AutoML

Answer

To transform raw data into features using SQL

Question 12

A company uses Looker Studio to create dashboards from BigQuery data. They notice that dashboard queries take several seconds to load. They want to improve performance without changing the underlying data or creating materialized views. Which option should they use?

Accepted Answer

Enable BigQuery BI Engine for the project. BigQuery BI Engine is an in-memory analysis service that accelerates queries from Looker Studio (and other BI tools) by caching data in memory, significantly reducing latency. Replicating data to Cloud SQL would add complexity and may not handle the volume. Using Looker instead of Looker Studio doesn't inherently speed up queries. Increasing BigQuery slots would help but is more expensive and not as targeted for BI tools.

Answer

Increase the number of BigQuery slots

Answer

Switch to Looker instead of Looker Studio

Answer

Replicate the data to Cloud SQL for faster queries

Question 13

A data engineer is building a production ML pipeline on Vertex AI. The pipeline must preprocess features (e.g., scaling, encoding) and then train a model. The preprocessing logic must be reusable for serving predictions. Which Vertex AI component should they use?

Accepted Answer

Vertex AI Feature Transform. Vertex AI Feature Transform is a managed service that allows you to define transformations using TFX Transform or BigQuery SQL, which are then applied consistently during training and serving. Vertex AI Pipelines can orchestrate but does not itself provide reusable transformations. Dataflow would require custom code. Vertex AI Feature Store serves pre-computed features, not transformations.

Answer

Dataflow with Apache Beam

Answer

Vertex AI Feature Store

Answer

Vertex AI Pipelines

Question 14

A company needs to predict whether a product image contains a specific defect. They have 10,000 labeled images and want to build a model quickly without writing custom code or training from scratch. Which GCP service should they use?

Accepted Answer

AutoML Vision. AutoML Vision is designed for custom image classification tasks with minimal ML expertise. It uses transfer learning and supports up to millions of images. AutoML Tables handles tabular data, not images. Vertex AI custom training would require more effort. AutoML NLP is for text data.

Answer

AutoML Tables

Answer

Vertex AI custom training

Answer

AutoML Natural Language

Question 15

A data engineer needs to split time-series data for training a forecasting model. The data is sorted by timestamp. The engineer wants to avoid leakage where future data influences training. Which data splitting approach should they use?

Accepted Answer

Use a time-series aware split: first 80% of data by timestamp for training, last 20% for testing. For time-series, the only safe split is to use an earlier contiguous block for training and a later block for testing, preserving temporal order. Random splits would cause leakage. K-fold cross-validation on time-series requires special techniques like forward chaining, not standard k-fold. Stratified split is for classification.

Answer

Use k-fold cross-validation with random assignment

Answer

Use stratified splitting on the target variable

Answer

Perform a random 80/20 split on the entire dataset