ACD300 Appian Certified Lead Developer Questions and Answers

The option B involves the lowest additional server cost and the least code retrofit effort, as it only requires one additional environment (DEV2) for the production support work stream. The production support work stream can use the existing STAGE environment for testing and user acceptance testing, as it is shared with the phase 2 development work stream. This way, there is no need to create a separate TEST2 environment or to retrofit any code from TEST to STAGE or from STAGE to PROD. Verified References: [Appian Certified Lead Developer study guide], page 16, section “Environment Strategy”.

When creating a script to add a new field to a table, you should consider two actions:

• Create a rollback script that removes the field. A rollback script is a script that can undo the changes made by the original script, in case something goes wrongor the changes need to be reverted. A rollback script is a good practice to have, as it can help to restore the previous state of the database and avoid any errors or inconsistencies. In this case, the rollback script should remove the new field from the table, and any other changes that were made by the original script.
• Create a script that adds the field and then populate it. A script that adds the field and then populate it is a script that can create the new field in the table, and then fill it with data from another table or source. This way, you can ensure that the new field has valid and consistent data, and that it can be used by the report in the upcoming release. In this case, the script should add the new field to the customer table, and then populate it with data from another table that contains the data used in the calculation.

• D. The join between the tables order_detail and product needs to be fine-tuned due to Indices. This is correct because the result of the explain statement showsthat the join between these two tables has a high cost of 0.99, which indicates that it is inefficient and needs to be fine-tuned. One possible reason for the high cost is that there are no indices on the columns that are used for joining these two tables, which leads to a full table scan. Therefore, creating indices on these columns could improve the performance of this join.
• E. The worst join is the one between the table order_detail and customer. This is correct because the result of the explain statement shows that the join between these two tables has a very high cost of 1.00, which indicates that it is the worst join in terms of efficiency and needs to be fine-tuned. One possible reason for the high cost is that there are no indices on the columns that are used for joining these two tables, which leads to a full table scan. Therefore, creating indices on these columns could improve the performance of this join.

The other options are incorrect for the following reasons:

• A. The request is good enough to support a high volume of data, but could demonstrate some limitations if the developer queries information related to the product. This is incorrect because the request is not good enough to support a high volume of data, as it has two joins with very high costs that need to be fine-tuned. Moreover, querying information related to the product would not necessarily cause any limitations, as long as the join between order_detail and product is optimized.
• B. The worst join is the one between the table order_detail and order. This is incorrect because the result of the explain statement shows that the join between these two tables has a low cost of 0.01, which indicates that it is efficient and does not need to be fine-tuned.
• C. The join between the tables order_detail, order and customer needs to be fine-tuned due to indices. This is incorrect because there is no such join between three tables in the result of the explain statement. There are only two joins: one between order_detail and order, and another between order_detail and customer. Each of these joins needs to be fine-tuned separately due to indices.

The field would benefit greatly from the index in the following scenarios:

• A. The field contains a textual short Business code. This is a scenario where an index can improve the performance of queries that search for exact matches or ranges of values in the field. A textual short Business code is likely to have high cardinality, meaning that it has many distinct values and low duplication. This makes the index more selective and efficient, as it can quickly narrow down the results based on the search criteria.
• C. The field contains many datetimes, covering a large range. This is a scenario where an index can improve the performance of queries that search for exact matches or ranges of values in the field. A datetime field is likely to have high cardinality, meaning that it has many distinct values and low duplication. This makes the index more selective and efficient, as it can quickly narrow down the results based on the search criteria.
• D. The field contains big integers, above and below 0. This is a scenario where an index can improve the performance of queries that search for exact matches or ranges of values in the field. A big integer field is likely to have high cardinality, meaning that it has many distinct values and low duplication. This makes the index more selective and efficient, as it can quickly narrow down the results based on the search criteria.

The other options are incorrect for the following reasons:

• B. The field contains long unstructured text such as a hash. This is a scenario where an index might not improve the performance of queries that search for exact matches or ranges of values in the field. A long unstructured text field is likely to have low cardinality, meaning that it has few distinct values and high duplication. This makes the index less selective and efficient, as it cannot quickly narrow down the results based on the search criteria. Moreover, indexing a long unstructured text field could increase thestorage space and maintenance cost for the database, which could affect the overall performance.
• E. The field contains a structured JSON. This is a scenario where an index might not improve the performance of queries that search for exact matches or ranges of values in the field. A structured JSON field is not a native data type in MySQL, and it requires special functions or operators to access or manipulate its elements. Indexing a structured JSON field could increase the complexity and overhead for the database, which could affect the overall performance. Verified References: Appian Documentation, section “Query Optimization”.

The @Version annotation is a feature of Appian that allows for optimistic locking of CDTs. Optimistic locking assumes that concurrent updates to the same data are rare and does not lock the data until it is saved. If two users try to save changes to the same data, the user who saves first will succeed, while the user who saves second will get an error message and will have to resolve the conflict manually. This approach is suitable for the client’s requirement, as itallows any user to update a case without locking it, ensures no information is lost when concurrent edits occur, and does not require any additional effort from the process administrators. Verified References: [Appian Documentation], section “Optimistic Locking”.

The two advantages of having High Availability (HA) for Appian Cloud applications are:

• B. Data and transactions are continuously replicated across the active nodes to achieve redundancy and avoid single points of failure. This is an advantage of having HA, as it ensures that there is always a backup copy of data and transactions in case one of the nodes fails or becomes unavailable. This also improves data integrity and consistency across the nodes, as any changes made to one node are automatically propagated to the other node.
• D. In the event of a system failure, your Appian instance will be restored and available to your users in less than 15 minutes, having lost no more than the last 1 minute worth of data. This is an advantage of having HA, as it guarantees a high level of service availability and reliability for your Appian instance. If one of the nodes fails or becomes unavailable, the other node will take over and continue to serve requests without any noticeable downtime or data loss for your users.

The other options are incorrect for the following reasons:

• A. An Appian Cloud HA instance is composed of multiple active nodes running in different availability zones in different regions. This is not an advantage of having HA, but rather a description of how HA works in Appian Cloud. An Appian Cloud HA instanceconsists of two active nodes running in different availability zones within the same region, not different regions.
• C. A typical Appian Cloud HA instance is composed of two active nodes. This is not an advantage of having HA, but rather a description of how HA works in Appian Cloud. A typical Appian Cloud HA instance consists of two active nodes running in different availability zones within the same region, but this does not necessarily provide any benefit over having one active node. Verified References: Appian Documentation, section “High Availability”.