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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Topic 2: Ensuring solution quality and reliability | 17% | - Troubleshooting and optimization
|
| Topic 3: Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|
| Topic 4: Maintaining and automating data workloads | 18% | - Automation and repeatability
|
| Topic 5: Designing data processing systems | 20% | - Designing for business requirements
|
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NEW QUESTION # 151
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file.
What is the most likely cause of this problem?
Answer: C
NEW QUESTION # 152
You are on the data governance team and are implementing security requirements. You need to encrypt all your data in BigQuery by using an encryption key managed by your team. You must implement a mechanism to generate and store encryption material only on your on-premises hardware security module (HSM). You want to rely on Google managed solutions. What should you do?
Answer: A
NEW QUESTION # 153
You are administering a BigQuery on-demand environment. Your business intelligence tool is submitting hundreds of queries each day that aggregate a large (50 TB) sales history fact table at the day and month levels. These queries have a slow response time and are exceeding cost expectations. You need to decrease response time, lower query costs, and minimize maintenance. What should you do?
Answer: C
Explanation:
To improve response times and reduce costs for frequent queries aggregating a large sales history fact table, materialized views are a highly effective solution. Here's why option A is the best choice:
Materialized Views:
Materialized views store the results of a query physically and update them periodically, offering faster query responses for frequently accessed data.
They are designed to improve performance for repetitive and expensive aggregation queries by precomputing the results.
Efficiency and Cost Reduction:
By building materialized views at the day and month level, you significantly reduce the computation required for each query, leading to faster response times and lower query costs.
Materialized views also reduce the need for on-demand query execution, which can be costly when dealing with large datasets.
Minimized Maintenance:
Materialized views in BigQuery are managed automatically, with updates handled by the system, reducing the maintenance burden on your team.
Steps to Implement:
Identify Aggregation Queries:
Analyze the existing queries to identify common aggregation patterns at the day and month levels.
Create Materialized Views:
Create materialized views in BigQuery for the identified aggregation patterns. For example CREATE MATERIALIZED VIEW project.dataset.sales_daily_summary AS SELECT DATE(transaction_time) AS day, SUM(amount) AS total_sales FROM project.dataset.sales GROUP BY day; CREATE MATERIALIZED VIEW project.dataset.sales_monthly_summary AS SELECT EXTRACT(YEAR FROM transaction_time) AS year, EXTRACT(MONTH FROM transaction_time) AS month, SUM(amount) AS total_sales FROM project.dataset.sales GROUP BY year, month; Query Using Materialized Views:
Update existing queries to use the materialized views instead of directly querying the base table.
Reference Links:
BigQuery Materialized Views
Optimizing Query Performance
NEW QUESTION # 154
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform.
Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?
Answer: A
NEW QUESTION # 155
You are developing an Apache Beam pipeline to extract data from a Cloud SQL instance by using JdbclO. You have two projects running in Google Cloud. The pipeline will be deployed and executed on Dataflow in Project A.
The Cloud SQL instance is running jn Project B and does not have a public IP address. After deploying the pipeline, you noticed that the pipeline failed to extract data from the Cloud SQL instance due to connection failure. You verified that VPC Service Controls and shared VPC are not in use in these projects. You want to resolve this error while ensuring that the data does not go through the public internet. What should you do?
Answer: A
Explanation:
Option A is incorrect because VPC Network Peering alone does not enable connectivity to Cloud SQL instances with private IP addresses. You also need to configure private services access and allocate an IP address range for the service producer network1.
Option B is incorrect because Cloud NAT does not support Cloud SQL instances with private IP addresses. Cloud NAT only provides outbound connectivity for resources that do not have public IP addresses, such as VMs, GKE clusters, and serverless instances2.
Option C is correct because it allows you to use a Compute Engine instance as a proxy server to connect to the Cloud SQL database over the peered network. The proxy server does not need an external IP address because it can communicate with the Dataflow workers and the Cloud SQL instance using internal IP addresses. You need to install the Cloud SQL Auth proxy on the proxy server and configure it to use a service account that has the Cloud SQL Client role.
Option D is incorrect because it requires you to assign public IP addresses to the Dataflow workers, which exposes the data to the public internet. This violates the requirement of ensuring that the data does not go through the public internet. Moreover, adding authorized networks does not work for Cloud SQL instances with private IP addresses.
NEW QUESTION # 156
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