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| Section | Weight | Objectives |
|---|---|---|
| Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Ensuring solution quality and reliability | 17% | - Troubleshooting and optimization
|
| Maintaining and automating data workloads | 18% | - Automation and repeatability
|
| Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|
| Designing data processing systems | 20% | - Designing for regulatory and security requirements
|
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NEW QUESTION # 182
You are planning to load some of your existing on-premises data into BigQuery on Google Cloud. You want to either stream or batch-load data, depending on your use case. Additionally, you want to mask some sensitive data before loading into BigQuery. You need to do this in a programmatic way while keeping costs to a minimum. What should you do?
Answer: A
Explanation:
To load on-premises data into BigQuery while masking sensitive data, we need a solution that offers flexibility for both streaming and batch processing, as well as data masking capabilities. Here's a detailed explanation of why option B is the best choice:
Apache Beam and Dataflow:
Apache Beam SDKprovides a unified programming model for both batch and stream data processing.
Google Cloud Dataflowis a fully managed service for executing Apache Beam pipelines, offering scalability and ease of use.
Customization for Different Use Cases:
By using the Apache Beam SDK, you can write custom pipelines that can handle both streaming and batch processing within the same framework.
This allows you to switch between streaming and batch modes based on your use case without changing the core logic of your data pipeline.
Data Masking with Cloud DLP:
Google Cloud Data Loss Prevention (DLP)API can be integrated into your Apache Beam pipeline to de- identify and mask sensitive data programmatically before loading it into BigQuery.
This ensures that sensitive data is handled securely and complies with privacy requirements.
Cost Efficiency:
Using Dataflow can be cost-effective because it is a fully managed service, reducing the operational overhead associated with managing your own infrastructure.
The pay-as-you-go model ensures you only pay for the resources you consume, which can help keep costs under control.
Implementation Steps:
Set up Apache Beam Pipeline:
Write a pipeline using the Apache Beam SDK for Python that reads data from your on-premises storage.
Add transformations for data processing, including the integration with Cloud DLP for data masking.
Configure Dataflow:
Deploy the Apache Beam pipeline on Google Cloud Dataflow.
Customize the pipeline options for both streaming and batch use cases.
Load Data into BigQuery:
Set BigQuery as the sink for your data in the Apache Beam pipeline.
Ensure the processed and masked data is loaded into the appropriate BigQuery tables.
Reference Links:
Apache Beam Documentation
Google Cloud Dataflow Documentation
Google Cloud DLP Documentation
BigQuery Documentation
NEW QUESTION # 183
You are migrating your on-premises data warehouse to BigQuery. One of the upstream data sources resides on a MySQL. database that runs in your on-premises data center with no public IP addresses. You want to ensure that the data ingestion into BigQuery is done securely and does not go through the public internet. What should you do?
Answer: A
NEW QUESTION # 184
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?
Answer: A
NEW QUESTION # 185
You have a network of 1000 sensors. The sensors generate time series data: one metric per sensor per second, along with a timestamp. You already have 1 TB of data, and expect the data to grow by 1 GB every day You need to access this data in two ways. The first access pattern requires retrieving the metric from one specific sensor stored at a specific timestamp, with a median single-digit millisecond latency. The second access pattern requires running complex analytic queries on the data, including joins, once a day. How should you store this data?
Answer: D
Explanation:
To store your data in a way that meets both access patterns, you should:
* A. Store your data in Bigtable Concatenate the sensor ID and timestamp and use it as the row key Perform an export to BigQuery every day. This option allows you to leverage the high performance and scalability of Bigtable for low-latency point queries on sensor data, as well as the powerful analytics capabilities of BigQuery for complex queries on large datasets. By using the sensor ID and timestamp as the row key, you can ensure that your data is sorted and distributed evenly across Bigtable nodes, and that you can easily retrieve the metric for a specific sensor and time. By performing an export to BigQuery every day, you can transfer your data to a columnar storage format that is optimized for analytical queries, and take advantage of BigQuery's features such as partitioning, clustering, and caching.
* B. Store your data in BigQuery Concatenate the sensor ID and timestamp. and use it as the primary key. This option is not optimal because BigQuery is not designed for low-latency point queries, and using a concatenated primary key may result in poor performance and high costs. BigQuery does not support primary keys natively, and you would have to use a unique constraint or a hash function to enforce uniqueness. Moreover, BigQuery charges by the amount of data scanned, so using a long and complex primary key may increase the query cost and complexity.
* C. Store your data in Bigtable Concatenate the sensor ID and metric, and use it as the row key Perform an export to BigQuery every day. This option is not optimal because using the sensor ID and metric as the row key may result in data skew and hotspots in Bigtable, as some sensors may generate more metrics than others, or some metrics may be more common than others. This may affect the performance and availability of Bigtable, as well as the efficiency of the export to BigQuery.
* D. Store your data in BigQuery. Use the metric as a primary key. This option is not optimal because using the metric as a primary key may result in data duplication and inconsistency in BigQuery, as multiple sensors may generate the same metric at different times, or the same sensor may generate different metrics at the same time. This may affect the accuracy and reliability of your analytical queries, as well as the query cost and complexity.
NEW QUESTION # 186
You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average 200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
What should you do?
Answer: C
Explanation:
To optimize the performance of a complex Spark job on Dataproc that heavily relies on shuffling operations, and given the cost constraints of using preemptible VMs, switching from HDDs to SSDs and using HDFS as an intermediate storage layer can significantly improve performance. Here's why option C is the best choice:
Performance of SSDs:
SSDs provide much faster read and write speeds compared to HDDs, which is crucial for performance-intensive operations like shuffling in Spark jobs.
Using SSDs can reduce I/O bottlenecks during the shuffle phase of your Spark job, improving overall job performance.
Intermediate Storage with HDFS:
Copying data from Google Cloud Storage (GCS) to HDFS for intermediate storage can reduce latency compared to reading directly from GCS.
HDFS provides better locality and faster data access within the Dataproc cluster, which can significantly improve the efficiency of shuffling and other I/O operations.
Cost Considerations:
Although SSDs are more expensive than HDDs, the performance improvement for shuffle-heavy workloads can justify the cost, especially if the improved performance reduces the overall runtime and thereby the cost of using preemptible VMs.
Using preemptible VMs with SSDs for this workload balances the cost and performance trade-offs effectively.
NEW QUESTION # 187
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