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Google Certified Professional Data Engineer Exam Sample Questions (Q153-Q158):

NEW QUESTION # 153
Which of the following are examples of hyperparameters? (Select 2 answers.)

A. Number of nodes in each hidden layer
B. Number of hidden layers
C. Biases
D. Weights

Answer: A,B

Explanation:
Explanation
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.
Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview

NEW QUESTION # 154
You need to create a SQL pipeline. The pipeline runs an aggregate SOL transformation on a BigQuery table every two hours and appends the result to another existing BigQuery table. You need to configure the pipeline to retry if errors occur. You want the pipeline to send an email notification after three consecutive failures. What should you do?

A. Use the BigQueryUpsertTableOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to true.
B. Create a BigQuery scheduled query to run the SQL transformation with schedule options that repeats every two hours, and enable notification to Pub/Sub topic. Use Pub/Sub and Cloud Functions to send an email after three tailed executions.
C. Create a BigQuery scheduled query to run the SOL transformation with schedule options that repeats every two hours, and enable email notifications.
D. Use the BigQuerylnsertJobOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to true.

Answer: B

Explanation:
To create a robust and resilient SQL pipeline in BigQuery that handles retries and failure notifications, consider the following:
BigQuery Scheduled Queries: This feature allows you to schedule recurring queries in BigQuery. It is a straightforward way to run SQL transformations on a regular basis without requiring extensive setup.
Error Handling and Retries: While BigQuery Scheduled Queries can run at specified intervals, they don't natively support complex retry logic or failure notifications directly. This is where additional Google Cloud services like Pub/Sub and Cloud Functions come into play.
Pub/Sub for Notifications: By configuring a BigQuery scheduled query to publish messages to a Pub/Sub topic upon failure, you can create a decoupled and scalable notification system.
Cloud Functions: Cloud Functions can subscribe to the Pub/Sub topic and implement logic to count consecutive failures. After detecting three consecutive failures, the Cloud Function can then send an email notification using a service like SendGrid or Gmail API.
Implementation Steps:
Set up a BigQuery Scheduled Query:
Create a scheduled query in BigQuery to run your SQL transformation every two hours.
Configure the scheduled query to publish a notification to a Pub/Sub topic in case of a failure.
Create a Pub/Sub Topic:
Create a Pub/Sub topic that will receive messages from the scheduled query.
Develop a Cloud Function:
Write a Cloud Function that subscribes to the Pub/Sub topic.
Implement logic in the Cloud Function to track failure messages. If three consecutive failure messages are detected, the function sends an email notification.
Reference:
BigQuery Scheduled Queries
Pub/Sub Documentation
Cloud Functions Documentation
SendGrid Email API
Gmail API

NEW QUESTION # 155
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low. You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

A. Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
B. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
C. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
D. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
E. Introduce data compression for each file to increase the rate file of file transfer.

Answer: B,D

NEW QUESTION # 156
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
B. Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset
C. Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
D. Create groups for your users and give those groups access to the dataset

Answer: B

NEW QUESTION # 157
You migrated your on-premises Apache Hadoop Distributed File System (HDFS) data lake to Cloud Storage.
The data scientist team needs to process the data by using Apache Spark and SQL. Security policies need to be enforced at the column level. You need a cost-effective solution that can scale into a data mesh. What should you do?

A. 1. Deploy a long-living Dataproc cluster with Apache Hive and Ranger enabled.
2. Configure Ranger for column level security.
3. Process with Dataproc Spark or Hive SQL.
B. 1. Define a BigLake table.
2. Create a taxonomy of policy tags in Data Catalog.
3. Add policy tags to columns.
4. Process with the Spark-BigQuery connector or BigQuery SQL.
C. 1. Apply an Identity and Access Management (IAM) policy at the file level in Cloud Storage.
2. Define a BigQuery external table for SQL processing.
3. Use Dataproc Spark to process the Cloud Storage files.
D. 1. Load the data to BigQuery tables.
2. Create a taxonomy of policy tags in Data Catalog.
3. Add policy tags to columns.
4. Process with the Spark-BigQuery connector or BigQuery SQL.

Answer: B

Explanation:
The key requirements are:
Data on Cloud Storage (migrated from HDFS).
Processing with Spark and SQL.
Column-level security.
Cost-effective and scalable for a data mesh.
Let's analyze the options:
Option A (Load to BigQuery tables, policy tags, Spark-BQ connector/BQ SQL):
Pros: BigQuery native tables offer excellent performance. Policy tags provide robust column-level security managed centrally in Data Catalog. The Spark-BigQuery connector allows Spark to read from/write to BigQuery. BigQuery SQL is powerful. Scales well.
Cons: "Loading" the data into BigQuery means moving it from Cloud Storage into BigQuery's managed storage. This incurs storage costs in BigQuery and an ETL step. While effective, it might not be the most
"cost-effective" if the goal is to query data in place on Cloud Storage, especially for very large datasets.
Option B (Long-living Dataproc, Hive, Ranger):
Pros: Provides a Hadoop-like environment with Spark, Hive, and Ranger for column-level security.
Cons: "Long-living Dataproc cluster" is generally not the most cost-effective, as you pay for the cluster even when idle. Managing Hive and Ranger adds operational overhead. While scalable, it requires more infrastructure management than serverless options.
Option C (IAM at file level, BQ external table, Dataproc Spark):
Pros: Using Cloud Storage is cost-effective for storage. BigQuery external tables allow SQL access.
Cons: IAM at the file level in Cloud Storage does not provide column-level security. This option fails to meet a critical requirement.
Option D (Define a BigLake table, policy tags, Spark-BQ connector/BQ SQL):
Pros:BigLake Tables: These tables allow you to query data in open formats (like Parquet, ORC) on Cloud Storage as if it were a native BigQuery table, but without ingesting the data into BigQuery's managed storage.
This is highly cost-effective for storage.
Column-Level Security with Policy Tags: BigLake tables integrate with Data Catalog policy tags to enforce fine-grained column-level security on the data residing in Cloud Storage. This is a centralized and robust security model.
Spark and SQL Access: Data scientists can use BigQuery SQL directly on BigLake tables. The Spark- BigQuery connector can also be used to access BigLake tables, enabling Spark processing.
Cost-Effective & Scalable Data Mesh: This approach leverages the cost-effectiveness of Cloud Storage, the serverless querying power and security features of BigQuery/Data Catalog, and provides a clear path to building a data mesh by allowing different domains to manage their data in Cloud Storage while exposing it securely through BigLake.
Cons: Performance for BigLake tables might be slightly different than BigQuery native storage for some workloads, but it's designed for high performance on open formats.
Why D is superior for this scenario:
BigLake tables (Option D) directly address the need to keep data in Cloud Storage (cost-effective for a data lake) while providing strong, centrally managed column-level security via policy tags and enabling both SQL (BigQuery) and Spark (via Spark-BigQuery connector) access. This is more aligned with modern data lakehouse and data mesh architectures than loading everything into native BigQuery storage (Option A) if the data is already in open formats on Cloud Storage, or managing a full Hadoop stack on Dataproc (Option B).
Reference:
Google Cloud Documentation: BigLake > Overview. "BigLake lets you unify your data warehouses and data lakes. BigLake tables provide fine-grained access control for tables based on data in Cloud Storage, while preserving access through other Google Cloud services like BigQuery, GoogleSQL, Spark, Trino, and TensorFlow." Google Cloud Documentation: BigLake > Introduction to BigLake tables. "BigLake tables bring BigQuery features to your data in Cloud Storage. You can query external data with fine-grained security (including row- level and column-level security) without needing to move or duplicate data." Google Cloud Documentation: Data Catalog > Overview of policy tags. "You can use policy tags to enforce column-level access control for BigQuery tables, including BigLake tables." Google Cloud Blog: "Announcing BigLake - Unifying data lakes and warehouses" (and similar articles) highlight how BigLake enables querying data in place on Cloud Storage with BigQuery's governance features.

NEW QUESTION # 158
......

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